Keywords: Manufacturing systems, Complex networks

Keywords: Automotive applications, Estimation, Sensor fusion
Abstract: This paper focuses on the design and implementation on low-cost embedding systems of the overall signal processing chain required for the estimation of the turbocharger rotating speed from the overall sound emissions acquired via a microphone placed in the vehicle hood. Because the useful information about the turbo speed is disturbed by all other noisy subsystems of the engine and powertrain, the core of the scheme is represented by an adaptive Frequency Locked-Loop (FLL) filter that is properly designed so as to extract useful frequency content from the acquired audio signals. Fixed-point vs floating-point implementations have been compared in several tests. Experimental outcomes demonstrate that the approach is ready to be introduced in the Engine Control Unit (ECU) in order to achieve new strategies for the turbocharger control.

Keywords: Linear parameter-varying systems, Automotive applications, Modeling
Abstract: Engine EGR (exhaust gas recirculation) valves are highly nonlinear due to its nonlinear friction, especially at the low-velocity region, where the friction increases sharply, resulting in a large steady-state valve displacement error. Multiple friction factors are considered in this paper using a switched LPV (linear parameter-varying) model, where the scheduling parameter is the friction coefficient as a function of valve velocity. Switched gain-scheduling LPV controllers are designed with hysteresis switching logic using the linear matrix inequality (LMI) convex optimization approach. The LPV controller performance is compared with that of PID controllers in the simulation study, showing a notable improvement in the system response performance and robustness.

Keywords: Modeling, Energy Systems, Transportation systems
Abstract: This paper details the development of a zero-dimensional combustion model for compression ignition engines with the main focus on incorporating physics-dependent features. Physical processes, such as injection, evaporation and mixing of fuel and air were given special attention and were linked to physical parameters such as injector geometry and fuel properties to allow for a realistic representation of the in-cylinder combustion process. In its current form, the model aids offline optimization and will further serve as the basis for a model predictive control strategy. Modeling parameters were calibrated against experimental steady-state data of a heavy-duty multi-cylinder engine, mostly at 1038rpm and 14.1bar brake mean effective pressure. Calibration coefficients were fitted to various parameter sweeps, including intake valve closure, injection pressure, combustion phasing and EGR. The modeling framework is capable of predicting premixed and diffusion combustion and was calibrated with up to three injection events. The predicted and experimentally measured combustion phasings are within 1 crank angle degree at high loads, while increasing discrepancies were found for lower loads.

Keywords: Reduced order modeling, Automotive applications, Stochastic/uncertain systems
Abstract: Improving modern downsized turbocharged engines requires not only the use of optimized engine components but also the implementation of control algorithms that minimize certain performance indicators such as fuel consumption. This paper proposes a two-input two-output (TITO) tracking control problem to achieve the optimal combustion process while minimizing indicated specific fuel consumption (ISFC) by means of adjusting spark advance (SA) and maximizing the levels of external exhaust gas recirculation (EGR). We show that at a fixed engine speed and various intake manifold pressures, the optimal combustion phase that maximizes piston work is fairly independent of EGR levels. If the crank angle of 50% mass fraction burned (CA50) is used as the combustion phasing indicator, this paper shows that the crank angle duration from spark advance to CA50 can be used as the second output of the TITO system without causing directionality or infeasibility problems. Feasibility of the integral control problem using (SA, EGR) as inputs and (CA50, CA50+SA) as outputs is discussed and a simple combustion model is proposed to account for the mean behavior and the cycle-to-cycle combustion variability.

Keywords: Automotive applications, Linear robust control
Abstract: The slip control during clutch engagement process is essential to ensure drive quality in terms of driveline jerk and slipping time for automated transmissions. Internal Model Control (IMC) has advantages in simplifying control algorithm and improving robustness. In this work, a Two-Input Two-Output (TITO) IMC for robust engagement of clutches is proposed. Considering two reference inputs in different styles, the closed-loop control system is decoupled with control blocks dealing with the two reference inputs independently. Simulation results show that the proposed IMC gains better performance than the conventional IMC without differentiating the reference input types, and also better than the sliding mode control.

Keywords: Real-time systems, Optimization, Control applications
Abstract: Nowadays the data center industry is playing a leading role in the world economic development and it is growing rapidly and constantly. On the other hand, this sector consumes a significant amount of energy and it contributes to Global Greenhouse Gas Emissions (GHGE). For these reasons, there are concerted and significant efforts to efficiently operate data centers. In particular, efficient cooling for data centers is essential for reducing operation costs because cooling equipment is energy intensive. In this context, we consider the problem of real-time efficient operation of an environmentally friendly cooling technology. In particular, we use a sinusoidally perturbed Newton-Like Extremum-Seeking Control (ESC) scheme, which benefits of the input-output plant map curvature estimate, to optimal operation of an indirect evaporative data center cooling system in a model-free fashion. Finally, by using a Matlab-based computer room air conditioning simulation environment, the effectiveness and performance of the proposed ESC scheme are compared in silico to those of standard ESC algorithm.

Keywords: Autonomous systems, Mobile Robots, Robotics
Abstract: We examine the practical implementation of an evolutionary algorithm to solve a pursuit evasion problem. In this problem, an Attacker and a Target move about an infinite plane with constant speeds. The Target moves in a constant circle, whereas the Attacker is free to change its direction with a bounded turn rate. The goal of the Attacker is to capture the Target in minimum time. We used pursuit evasion robots to physically model this problem and implement an evolutionary algorithm to evaluate the controller performance in real-world systems.

Keywords: Mechatronic systems, Verification and validation, Control architectures
Abstract: Particle accelerators have a singular environment where multiple constraints are driving the engineering of equipment. Designers have to deal with the destructive effects of charged particles, high vacuum requirements, large temperatures and particular system architectures due to large-scale installations such as the Large Hadron Collider (LHC) at the CERN laboratory. At the same time, there is a continuous challenge to produce, measure and control smaller particle beams to increase the discovery potentials of large physics experiments. In this context, an innovative actuator has been built to measure precisely the size of beams down to 150um sigma, by moving a thin carbon wire of 30um at about 20m/s through particle beams. Called Beam Wire Scanner (BWS), this system uses direct drive coupling to actuate a shaft inside a vacuum vessel without moving parts outside it. We are reporting on the design and validation of its control system based on torque control feedback as the only on-line closed-loop system to operate this instrument. The proposed strategy keeps the smoothest action as possible on the system avoiding speed and position corrections that would lead to undesired torque variations, increasing the uncertainty of the carbon wire position.

Keywords: Embedded systems, Control architectures, Control applications
Abstract: Abstract--- The design, noise, and performance analysis of a digital controller architecture directly processing Sigma Delta bitstreams is described. The use of Sigma Delta bitstream representation allows the controller to achieve very low control latency by removing the bit-stream to parallel conversion step. The controller has a small footprint and low power dissipation from multiplierless design. Based on integrated state variables, the controller achieves stable implementation of a wide range of controller designs with near continuous time performance. A generic technique for estimating the signal to noise ratio of the controller using an appropriate model of Sigma Delta bitstream signal and noise characteristics is outlined. To demonstrate the effectiveness of the Sigma Delta controller in a low latency application, a Q controller for an atomic force microscope cantilever was designed and simulated. The Sigma Delta controller was able to achieve similar performance to its ideal continuous time counterpart and surpass its conventional discrete equivalent while maintaining high output signal resolution and having a footprint that fits easily into an inexpensive micro-power FPGA.

Keywords: Linear systems, Control architectures
Abstract: This paper deals with the so-called Disturbance Feedback Control concept, which is a technique to improve the disturbance rejection capabilities of existing control loops. We perform an internal stability analysis of a generic DFC setup and identifies several stability conditions that the DFC law must satisfy in order to guarantee stable closed-loop operation, assuming all subsystem blocks are linear and time invariant. The validity of the stability conditions are illustrated on two examples, a simple scalar example that serves to illustrate instability in case of an open-loop unstable plant model, and a more involved example concerning a DFC design for a gantry crane considered in earlier publications.

Keywords: Control architectures, Autonomous systems, Nonlinear systems
Abstract: This paper studies distributed platoon control with virtual path constraints. Using transverse feedback linearization, the control approach decouples the platoon's dynamics into components tangential and transversal to the path. A platoon controller exclusively in the tangential subsystem controls the platoon's formation tangentially along the path, while a feedback control law in the transversal component stabilizes the platoon to remain on the path. As an application of the theory, a human-robot interaction experiment is performed on a platoon of quadrotors. The platoon leader implements an admittance controller, which allows the platoon to respond to human-applied forces. The path constraints limit the platoon's movement to only be along the path, ensuring safety to the interacting human.

Keywords: Kalman filtering, Energy Storage, Estimation
Abstract: In this paper, a method for current and state of charge estimation of lithium-ion battery packs is proposed. On the basis of a fractional 1-RQ equivalent circuit cell model, a string model containing cells in serial connection, and a pack model containing strings in parallel connection is built up. In order to reduce computational costs, the model is distributed string-wise into subsystems. An algorithm using distributed fractional extended Kalman filters is applied to estimate the state of charge of all cells of each string, locally. To avoid costly measurements of numerous currents, a model based calculation is proposed which describes how the total battery current is split up between the strings. The algorithm is tested and validated using measurement data.

Keywords: Modeling, Estimation, Identification
Abstract: Unmodeled battery-cell voltage hysteresis can cause state-of-charge (SOC) estimation error in a battery-management system (BMS) of an EV system. To improve BMS performance, hysteresis voltage must be modeled correctly. This paper presents the use of linear least-squares identification of a Krasnosel’skii-Pokrovskii (KP) model to estimate hysteresis for a Li//Si half cell. We find that the KP model provides good estimates and has lower root-mean-squared estimation error than a classical discrete Preisach model with the same degree of discretization.

Keywords: Energy Storage, Automotive applications, Learning
Abstract: In this article an approach is presented based on the use of measured experimental data from conventional battery packs to generate {it synthetic} operational data for subsequent use in monitoring, predicting and controlling battery pack state of health. Generally speaking, experimentation-based synthetic data is effective in extensive simulation models possessing many varied operating conditions. The results presented in this article focus on proof of concept and are part of a comprehensive study into general capacity estimation and capacity fade prediction in battery packs. Experimental data is derived from scaled operational cycles with multiple charge and discharge pulses applied repetitively on a commercially available battery pack. The resulting synthetically generated data, using Markov chain approaches, has the flexibility of matching user-imposed conditions and can be of any length. Therefore, the focus in this article is the generation of sufficient training data for models built from machine learning techniques, utilizing only a relatively limited amount of actual data. In the context of the overall ongoing study, the behavior of the battery pack is characterized by features and a supervised learning approach is adopted in order to estimate capacity fade during real-time operation without the use of specific capacity tests.

Keywords: Observers, Modeling, Energy Storage
Abstract: This paper presents an observer design for the simultaneous estimation of charge state and crossover flux in disproportionation redox flow batteries, which exhibits exponential estimation error convergence to a bounded residual set. The crossover flux of vanadium through the porous separator is considered as an unknown function of the battery states, model-approximated as the output of a persistently excited linear system. This parametric model and the simple isothermal lumped parameter model of the battery are combined to form an augmented space state representation suitable for the observer design, which is carried out via Lyapunov stability theory including the error-uncertainty involved in the approximation of the crossover flux. The observer gain is calculated by solving a polytopic linear matrix inequality problem via convex optimization. The performance of this design is evaluated with a laboratory flow battery prototype undergoing self discharge.

Keywords: Estimation, Energy Storage, Simulation
Abstract: This paper studies the state estimation of a solid-state battery. Partial differential equations based on a nonporous insertion model are presented to model the solid state battery. Two assumptions simplifying the battery model underlie the study of state estimation: that the Li-ion concentration in the solid electrolyte is uniform and the charge transfer coefficient at the positive electrode is 0.5. The assumptions made resolve the issue of very weak observability: the diffusion dynamics in the electrolyte and positive electrode are connected via Butler-Volmer equation and the contribution of the charge transfer overpotential is found to be insignificant. For state estimation, an extended Kalman filter (EKF) is applied based on the simplified battery model using measurements of current and voltage of the cell. Simulation results show that the state-of-charge (SOC) of the battery can be accurately estimated by the EKF in specific SOC ranges, i.e., SOC<0.2 and SOC>0.4.

Keywords: Energy Storage, Energy Systems, Complex systems
Abstract: Electrification of transportation and energy storage systems is accelerating the advent of battery ``systems of systems." Due to high complexity thereof, a number of challenges exist in battery management such as charge balancing and anomaly detection. Internal short circuit (ISC) is one of the most common and hazardous anomalies, and a few methods have been proposed to detect it inside a serial-parallel (two-dimensional) battery network. This paper extends the previous work and proposes a novel concept of three-dimensional battery packs. It follows the industry practices of constructing a pack from modules consisting of batteries, but use of an extra dimension and additional current paths enhance ISC detectability with the minimal use of current sensors. This paper reports ISC simulation results from the proposed 3D battery pack topologies.

Keywords: Identification, Optimization, Computational methods
Abstract: The quality of the data for system identification is of utmost of importance. Ideally, the output data should satisfy requirements regarding variance, and in the case of multiple-input multiple-output (MIMO) systems, correlation between the outputs. Usually, it is desired to achieve this by input perturbations with peak values as small as possible. A related measure is the crest factor, which is a measure of the (inverse) power of an input perturbation with given peak value. In this paper, the effect of minimizing the input peak and the crest factor, subject to desired output variances/covariances, is studied for various types of perturbation signals. The design procedure is completely data based; data are obtained by one or more preliminary experiments with the system to be identified. A model of an ill-conditioned distillation column is used for illustration.

Keywords: Identification, Estimation, Semidefinite programming
Abstract: We consider the problem of estimating the output of an unknown discrete-time linear time-invariant system and identifying a model of the system, where only measurements via a nonlinear dynamic sensor with known dynamics are available. The main result of this paper is a rank-constrained semidefinite program, which provides an equivalent characterization of this identification and estimation problem. This extends existing results from Wiener system identification to the more general case that the nonlinear block exhibits dynamic behavior, which is a commonly found scenario in practical applications. Notably, the result can be applied in the presence of nonlinear sensors with general non-invertible system dynamics. Two examples are used to illustrate the applicability of our approach.

Keywords: Machine learning, Intelligent systems, Identification
Abstract: In view of the difficulty of real-time measurement of the effluent total phosphorus (TP) for a wastewater treatment plant (WWTP), in this paper, a new TP soft sensor which is different from the traditional single value method is presented. It realizes the guaranteed estimation of the TP concentration by predicting the upper and lower bounds. Partial least squares is used to obtain the secondary variables of the effluent TP. Then, an input-output model with secondary variables as the inputs and the effluent TP as the output is built by the radial basis function neural network (RBFNN). Considering the bounded modeling error, the linear-in- parameter set membership identification algorithm is used to obtain a description of the uncertain set of the output weights of the RBFNN. During the operation of the WWTP, the established soft sensor can predict the upper and lower bounds of the effluent TP concentration. Besides, a bundle of soft sensors is constructed and the intersection of the results given by the soft sensors is used to reduce the conservativeness caused by using a single sensor. The experimental results show the effectiveness of the proposed method.

Keywords: Power Electronics, Control applications, Estimation
Abstract: Several heuristic procedures to estimate the rotor position of interior permanent magnet synchronous motors via signal injection have been reported in the applications literature, and are widely used in practice. These methods, based on the use linear time invariant high-pass/low-pass filters, are instrumental for sensorless controllers. To the best of our knowledge, no theoretical analysis has been carried out for them. The objectives of this note, are (i) to invoke some recent work on the application of averaging techniques for injectionbased observer design to develop a theoretical framework to analyze the sensorless methods, and (ii) to propose a new method that, on one hand, ensures an improved accuracy and, on the other hand, can be related with the current filtering technique. An additional advantage of the new method is that it relies on the use of linear operators, implementable with simple computations. The effectiveness of the proposed scheme is assessed by experiments.

Keywords: Identification, Nonlinear systems, Automotive applications
Abstract: Real systems are often nonlinear, and accounting for the nonlinearity can be essential for the attainable closed-loop performance in model-based control. This is especially true for many engine related control problems, as the achievable tradeoff between different targets, e.g. emissions and consumption, depends strongly on the model quality. However, engine systems tend to change over time, and it would be beneficial to be able to track these changes in the model. Against this background, we propose here a novel recursive algorithm for online adaptive system identification aimed to estimate an approximating parametric nonlinear model (polynomial NARX) of systems. This model structure has been used earlier e.g. for the air path control. The presented identification scheme is also suitable for parameter estimation in a closed-loop setting, provided that the data is sufficiently exciting. The main contribution of this paper is a recursive algorithm minimizing the k-step ahead prediction error for updating the model parameters in a computationally efficient way. We show its effectiveness by means of simulation examples of a nonlinear case study system and real data of a Diesel engine air path.

Keywords: Complex networks, Chaotic systems, PID control
Abstract: This paper investigates the problem of controlling Hopf bifurcation of delayed small-world networks with fractional-order dynamics. Specifically, a fractional-order PID feedback controller is designed to realize control of the Hopf bifurcation that is embedded in delayed small-world networks with fractional-order dynamics. The characteristic equation of the controlled fractional-order network is analyzed, which yields the useful conditions for stability as well as Hopf bifurcation. Moreover, the analysis reveals that the control parameters of the fractional-order PID feedback control scheme may be varied to have a significant effect on the bifurcation dynamics of the controlled small-world network with fractional-order dynamics. The theoretical findings are also confirmed through computer simulations.

Keywords: Complex networks, Energy Storage, Optimization
Abstract: This work develops a congestion management method for the power grid utilizing the notion of curvature. It initially uses the curvature concept to detect areas prompt to congestion (negative curvature areas) and it subsequently applies load balancing techniques (through FACTS devices) and load (storage devices) deployment to maximize curvature (grid decongestion) and cost-effectively minimize the generated energy throughout the grid, while at the same time guaranteeing stability under phase angle and voltage constraints. Two different curvature definitions are compared (Ollivier-Ricci Curvature and Effective Resistance Curvature), and an entropy concept suitable to power grid is introduced as a new measure to analyze grid congestion.

Keywords: Optimization, Power systems
Abstract: Network utility maximization (NUM) has been often formulated to solve the optimal resource allocation problem in wireless networks and power systems. The network planner aims to optimally balance the supply and demand among producers and consumers so that the social welfare is maximized while the network constraints are respected. However, when the intertemporal dynamics of agents are taken into account, a single-period NUM formulation becomes insufficient. In this paper, the multi-period NUM with intertemporal dynamic constraints is considered and solve by a proposed iterative method. It features a price iteration scheme that achieves the optimal social welfare, and the algorithm is guaranteed to converge if proper concavity conditions hold. Simulation results verifies the effectiveness of the proposed iterative method. It is also demonstrated that there is an increase in social welfare compared to a single-period formulation.

Keywords: Complex systems, Time delays
Abstract: This paper proposes a new model of nonlinear complex dynamical networks with time-delay and sampled-data, which is transformed based on the characteristics of the sampling period, and investigates the group synchronization of nonlinear complex dynamical networks with time-delay and sampled-data. The Lyapunov function is established to prove that the network can achieve the group synchronization under some special conditions. Numerical example and simulations are proposed to illustrate the theoretical results.

Keywords: Biosystems, Complex networks, Algebraic/geometric methods
Abstract: In this paper, synchronization of interconnected Boolean networks subject to stochastic function perturbations is investigated by resorting to the semi-tensor product technique. First, the concept of stochastic function perturbations for interconnected Boolean networks is introduced base on the algebraic state space representation. Then, by constructing a transition probability matrix, a necessary and sufficient condition is obtained assuring the synchronization of the interconnected Boolean networks under stochastic function perturbations. Subsequently, the synchronization of interconnected Boolean networks with positive probability is further studied, and an algebraic synchronization criterion is established. Finally, a numerical example is employed to illustrate the efficiency of the obtained theoretical results.

Keywords: Sliding mode control, Control Technology
Abstract: In this work, the discrete-time sliding mode control problem for the networked control systems under fading channels is investigated. Moreover, the state signals will be quantized through a logarithmic quantizer before transmitted to the controller. Subsequently, a sliding surface relating to fading coefficient is designed to cope with the fading measurements. It is shown that the designed sliding mode controller can drive the state trajectories into the neighborhood of the specified sliding surface and the closed-loop system is asymptotically stable in the sense of mean square. In the end, a numerical simulation is given.

Keywords: Stochastic/uncertain systems

Keywords: Stochastic/uncertain systems, Modeling

Keywords: Stochastic/uncertain systems, Modeling, Complex systems

Keywords: Complex networks

Keywords: Stochastic/uncertain systems

Keywords: Cybersecurity

Keywords: Automotive applications, Control applications
Abstract: Cabin heating demand and engine efficiency degradation in cold weather lead to considerable increase in fuel consumption of hybrid electric vehicles (HEVs), especially in congested traffic conditions. This paper presents an integrated power and thermal management (i-PTM) scheme for the optimization of power split, engine thermal management, and cabin heating of HEVs. A control-oriented model of a power split HEV, including power and thermal loops, is developed and experimentally validated against data collected from a 2017 Toyota Prius HEV. Based on this model, the dynamic programming (DP) technique is adopted to derive a bench-mark for minimal fuel consumption, using 2-dimensional (power split and engine thermal management) and 3-dimensional (power split, engine thermal management, and cabin heating) formulations. Simulation results for a real-world congested driving cycle show that the engine thermal effect and the cabin heating requirement can significantly influence the optimal behavior for the power management, and substantial potential on fuel saving can be achieved by the i-PTM optimization as compared to conventional power and thermal management strategies.

Keywords: Automotive applications, Control applications, Predictive control
Abstract: In this paper, we propose an MPC-based precision cooling strategy (PCS) for energy efficient thermal management of an automotive air conditioning (A/C) system. The proposed PCS is able to provide precise tracking of the time-varying cooling power trajectory, which is assumed to meet the passenger comfort requirements. In addition, by leveraging the emerging connected and automated vehicles (CAVs) technology, vehicle speed preview can be incorporated in our A/C thermal management strategy for further energy efficiency improvement. This proposed A/C thermal management strategy is developed and evaluated based on a physics-based A/C system model (ACSim) from Ford Motor Company for the vehicles with electrified powertrains. Over SC03 cycle, for tracking the same cooling power trajectory, the proposed PCS provides 4.9% energy saving at the cost of slight increase in the cabin temperature (less than 1degC), compared with Ford benchmark case. It is also demonstrated that by coordinating with future vehicle speed and shifting the A/C power load, the A/C energy consumption can be further reduced.

Keywords: Automotive applications, Predictive control, Optimization
Abstract: The thermal system of the passenger compartment is one of the main auxiliary loads in vehicles with electrified powertrains. One possibility to reduce the energy consumption of the thermal system is to implement optimization-based thermal management strategies, which are able to minimize the energy consumption, while maintaining passengers' thermal comfort. Focus of the present work is the extension of an existing thermal management strategy by an additional control input determining whether to activate the compressor of the air conditioning system or not. The main modules of the existing strategy are two linear model predictive controllers, for the cases with and without activated air conditioning system, formulated as quadratic programs. The existing strategy is extended by a binary control input describing the status of the air conditioning system, such that both quadratic programs are combined to one mixed-integer nonlinear program (MINLP). This problem is reformulated to a mixed-integer quadratic program (MIQP). Besides the MIQP also a heuristic approach for the activation of the air conditioning system is developed to reduce the computational effort. Both approaches are analyzed and compared to show their advantages.

Keywords: Reinforcement learning, Intelligent systems, Automotive applications
Abstract: Reinforcement learning (RL) is in essence a trialand- error process which involves exploratory actions. These explorations can lead to system constraint violations and physical system damages, impeding RL’s use in many realworld engineered systems. In this paper, we develop a safe RL framework that integrates model-free learning with modelbased safety supervision to bridge the gap. We exploit the underlying system dynamics and safety-related constraints to construct a safety set using recursive feasibility techniques. We then integrate the safety set in RL’s exploration to guarantee safety while simultaneously preserving exploration efficiency by using the hit-and-run sampling. We design a novel efforts-toremain- safe penalty to effectively guide RL to learn system constraints. We apply the proposed safe RL framework to the active suspension system in which actuation and state constraints are present due to ride comfort, road handling, and actuation limits. We show that the developed safe RL is able to learn a safe control policy safely while outperforming a nominal controller.

Keywords: Actuators, Automotive applications, Nonlinear robust control
Abstract: This paper presents an adaptive robust force control of a pump-controlled electro-hydraulic actuator for application in an active suspension system. To overcome the limitations of a linearized model for a pump-controlled electro-hydraulic actuator (EHA), which has been already studied in existing literature, an improved nonlinear model is proposed. To realize force tracking, a linear tracking differentiator-based adaptive robust control (LTDARC) is applied to deal with parametric uncertainties and unknown nonlinear dynamics. The convergence of the combination of adaptive robust control (ARC) and linear tracking differentiator (LTD) is investigated. The effectiveness of LTDARC is verified in a simulative study of a quarter-car suspension with a pump-controlled EHA.

Keywords: Predictive control, Energy Systems, Energy Storage
Abstract: In this paper we present simulations of Economic Model Predictive Control (EMPC) for control of the energy system in a smart home. The energy system in a smart home consists of a stationary battery, photo voltaic solar cells on the roof, a heat pump for heating, and an electrical vehicle battery that is charged. Using weather forecasts, thermal comfort and driving profiles, the EMPC coordinates this energy system and its interaction with the external energy system by purchasing and selling electricity at prices that are announced in advance. The EMPC is a linear program with soft constraints for the output constraints and an objective function that represents the cost of energy. In contrast to existing methods, the key novelties in the present paper is the use of multi-level soft constraints and an objective function that accounts not only for the cost of energy used during the prediction horizon but also for the value of energy stored at the end of the prediction horizon. We demonstrate by simulation, that these novelties are important for well-behaved closed-loop performance of the EMPC.

Keywords: Control applications, Predictive control, Adaptive control
Abstract: In this paper, we present a data-driven predictive control (DDPC) strategy suitable for (general bilinear) building energy systems, which only relies on historical measurements. The introduced control technique operates iteratively in a receding horizon scheme. During the operation of DDPC, using available historical building data which are collected during the normal operation of building, the dynamics of building are approximated and estimated. Therefore, the system dynamics are decomposed into two stable subsystems, one describing the thermal dynamics of the mass of building and the other one describing the dynamics of the temperature of rooms leading to a linear infinite-dimensional interconnected system. Linear regression is used to estimate a finite approximation thereof. Employing the approximate model, a cost is minimized in each iteration. The performance of the introduced data-driven control method is numerically verified for a validated building simulation environment.

Keywords: Predictive control, Control applications, Energy Systems
Abstract: In this paper we present a novel approach to control a shopping center HVAC system which significantly reduces the amount of energy spent on cooling. The HVAC system considered is for a section of a Danish shopping center, including central ventilation, fan coil units and a chiller delivering cooling.

The system is modeled using a grey-box RC-equivalent approach and identified parameters using measurement data extracted directly from the Building Management System from several days of live operation. From a comparison with measurements it has been concluded that the model is usable for the purpose of control design.

An optimal control problem to minimize total cooling effort by manipulating central ventilation supply temperature and chiller forward temperature has been posed. The intention being to shift cooling from the chiller to the ventilation unit when cooling is available through a low ambient temperature -- avoiding both heating and cooling the same air. This optimal control problem has been used as the basis for a Model Predictive Controller. For prediction purposes, input signals from the previous days have been used, exploiting the fairly periodic behaviour of the system.

Simulation studies show that during heating seasons the Model Predictive Controller is capable of shifting the entire cooling load to the ventilation unit and still maintain the same performance as the nominal controller. This amounts to energy savings of 21%.

Keywords: Predictive control, Optimization, Nonlinear systems
Abstract: Model predictive control (MPC) is a widely used technique for temperature set-point tracking and energy optimization of Heating Ventilation and Air Conditioning (HVAC) systems in buildings. Unfortunately, a nonlinear thermal building model leads to a computationally expensive nonlinear MPC problem that is not suitable for real-time control and optimization. This paper presents a novel approximate linearized model for building's thermal dynamics and the HVAC system power consumption that leads to a computationally efficient linear model predictive controller (LMPC) for the buildings' HVAC systems. We employ feedback linearization technique to obtain an equivalent linearized model for the nonlinear thermal building dynamics and use constraint mapping approach to obtain a linearized formulation for new control variables. Next, using piecewise linearization, we obtain a linearized analytical model for the HVAC system power consumption. The proposed LMPC technique is validated using multiple simulation case studies. We demonstrate that the proposed LMPC technique is not only computationally efficient but also accurately approximates the nonlinear optimal control decisions.

Keywords: Smart grid, Energy Systems, Predictive control
Abstract: Ancillary services to the power grids are in demand due to a rapid growth of renewable energy in recent years. Demand-side flexible loads have the potential to provide reliable and affordable operational reserve services to the grid. However, this must be achieved without compromising the resources' delivery of quality service to the end users. In this paper, we present demand flexibility estimator and ramping service controls for multi-zone commercial building's air distribution system and chiller plant. The building flexibility is represented by a simple virtual battery model and a model-based optimization algorithm is used to estimate the parameters of the virtual battery in a receding horizon manner. The proposed ramping controller varies the Heating, Ventilation and Air Conditioning (HVAC) systems' power consumption so that the deviation from its baseline power profile tracks the grid reference signal while maintaining comfortable indoor climate. This is achieved by modifying the zones temperature set-points within a given comfort bounds and adjusting the temperature set-point of the chilled water. The controller's performance is demonstrated with a high fidelity simulation model representing a medium sized office building to meet the ARPA-E NODES project targets for response time, ramp time, reserve magnitude, variability tolerance and duration of service. Overall, the results validate the capability of buildings to provide high quality grid ramping reserves with minimal indoor comfort impact.

Keywords: Estimation, Data analytics, Optimization
Abstract: In this paper, the estimation of passivity based on data is considered. In recent works, a sampling scheme to estimate the passivity level of unknown systems from iterative open-loop experiments was introduced. However, there are many cases where it is useful to estimate the passivity level of a sub-system in closed-loop systems; therefore, we extend this method to estimate the passivity level from closed-loop experiments.

Keywords: Smart grid, Distributed control, Optimization
Abstract: This paper presents a distributed model predictive control (MPC) technique for power management of a PV-installed microgrid using passivity-based generalized primal-dual dynamics. The PV power has large uncertainty because it depends on weather conditions. To keep stable power supply to the microgrid, both accurate prediction of PV power supplies and efficient energy management based on the prediction are essential. We propose a method for microgrid management by combining the MPC and the passivity-based generalized primal-dual dynamics. The passivity-based primal-dual method can solve a certain class of convex optimization problem in a distributed manner. We demonstrate the effectiveness of the proposed method by a numerical simulation.

Keywords: Smart grid, Power systems, Distributed control
Abstract: A time averaging algorithm is proposed for load balancing in distributed power dispatch, where a multi-agent system technique is employed. Each agent is connected to each generator to be controlled, and the agents are linked by the information lines modeled by a directed graph. The objective of this distributed algorithm is to achieve an average load balancing of generators over time. The convergence analysis of the algorithm is provided, which reveals an explicit relation between the number of iterations and the closeness of the ideal balancing in terms of the characteristic values of the graph Laplacian. This result also gives a rigorous stopping rule for the time averaging algorithm. Numerical examples demonstrate preferable behaviors of the proposed algorithm.

Keywords: Energy Storage, Optimization, Stochastic/uncertain systems
Abstract: In this paper, as for a microgrid system, we establish a game-theoretic framework for modeling the strategic behavior of the energy aggregators (microgrid components) that are connected to solar energy resources. Considering the prediction uncertainty of the solar energy, we model the profit function of each aggregator (market player) with a two-stage stochastic structure. Then, according to the power flow equation, a coupled constraint equilibrium is defined, and some properties are studied. To compute the specific equilibrium, we recast the problem by its Karush–Kuhn–Tucker (KKT) conditions and then reformulate the problem. The numerical simulation illustrates the coupled strategies of the market players and the existence of the coupled constraint equilibrium.

Keywords: Control applications, Optimization, Energy Systems
Abstract: Hydronic cooling systems are often used in large buildings. In these systems, chilled water is transported from the chillers to Air Handling Units (AHUs) via a water distribution system. The temperature of exhaust air is controlled by the flow of the chilled water in AHUs. Due to a poorly balanced hydronic network, some of the AHUs may experience starvation at high load conditions, meaning that air temperature control is impossible. To overcome this problem, we propose an automatic approach to balancing the hydronic system, such that all AHUs can control their air temperatures. The cost is that all zones of the building are controlled to a slightly higher air temperature, which often can be compensated by higher air flow, to keep the room temperature at the desired value. The balancing algorithm is verified on a Modelica model of an office building.

Keywords: Adaptive control, Actuators
Abstract: In order to reduce tonal orders of a combustion engine with electrical drives as acoustical actuators, a parallel control structure for active noise cancellation (ANC) applications is used. It consists of map-based and adaptive feedforward control algorithms to reduce the convergence time and improve the tracking behavior during fast changes of the disturbing motor order. This paper describes two extensions of the parallel control structure. A method to improve the noise reduction during the adaption process is proposed. Furthermore, a method is described to constrain the filter weights of the parallel control structure to avoid nonlinear distortions due to output constraints.

Keywords: Time delays, Nonlinear systems, Adaptive control
Abstract: Drawing upon Lyapunov stability theories and Neural Networks (NNs) techniques, we propose an online adaptive dynamic programming (ADP) based optimal control of nonlinear system with time delays. Our contribution is three-fold. First, we investigate the asymptotical stability on nonlinear time delay systems and reveal how the delay parameter may influence the stability. Second, we utilize two approximate NNs to estimate the optimal control policy such that the weights for both NNs are online tuned simultaneously. Third, we prove the convergence of NN weights estimation errors. Numerical examples are presented to illustrate our results.

Keywords: Adaptive control, Sliding mode control, Variable structure systems
Abstract: This paper presents an extension of the Binary Model Reference Adaptive Control (BMRAC) for uncertain multivariable (square) systems with non-uniform arbitrary relative degrees, using only output feedback. The BMRAC algorithm is a robust adaptive strategy which has the good transient and robustness properties of sliding mode control with the important advantage of preserving a continuous control signal free of chattering, inherited from classical adaptive systems. Unlike most of the publications which consider plants with uniform relative degree one, here we circumvent the arbitrary relative degree obstacle using a multivariable generalization of global finite-time differentiators with dynamic gains and higher-order sliding modes. State-norm observers are employed to obtain an upper bound for the unmeasured state and to update the gains of the proposed robust and exact multivariable differentiator. Global and exact output tracking is guaranteed without requiring stringent symmetry assumptions on the plant High-Frequency Gain matrix, usually assumed in other works in the literature.

Keywords: Adaptive control, Complex systems, Aerospace applications
Abstract: Loop Heat Pipes (LHPs) are complex, thermodynamic heat transport systems for the thermal control of temperature-sensitive systems with locally separated heat sources and heat sinks. Through evaporation and condensation of a working fluid, the two-phase LHP achieves a high heat transfer coefficient. The LHP operating temperature strongly depends on the heat load and the sink temperature. Therefore, an additional control heater on the compensation chamber is used to keep a desired operating temperature under varying operating conditions. The temperature control performance of the LHP is improved by a nonlinear model identification adaptive control approach for the control heater and compared to the commonly used PID controller. Time-variant model parameters are estimated online to improve the model accuracy. The temperature prediction is validated with experimental data from a LHP test bench. The presented control algorithm is implemented and tested in a numerical simulation of the LHP.

Keywords: Mechatronic systems, Robotics applications, Actuators
Abstract: Autonomous excavators have great potential to increase productivity and to improve safety. For the design process of autonomous functions, it is advantageous to use an underlying controller for each actuator instead of using the physical system input. This work considers a velocity controller for each cylinder of a mini excavator. This controller is designed in a two-degree-of-freedom control structure. The feedforward part is a simplified inverse model of the cylinder dynamics and the feedback part consists of a PI controller. Furthermore, the controller considers the pressure difference over the valve and therefore decouples each cylinder. The valve opening area which is generally a nonlinear function is necessary for this controller approach. Since this function is not always known and changes over time, a moving horizon estimator is designed which estimates it in an online fashion. The experimental results on a mini excavator show the ability of the moving-horizon-estimator to estimate the valve opening area. The velocity controller is able to track the reference velocity and to decouple each cylinder.

Keywords: Stochastic/uncertain systems, Fault detection/accomodation
Abstract: This paper focuses on the problem of H_- index for linear Markov jump stochastic systems. Firstly, a set of finite horizon backward generalized differential Riccati equations (GDREs) and a set of matrix inequalities are introduced. Then, based on the introduced backward GDREs and matrix inequalities, for nominal Markov jump linear time-varying stochastic systems, two necessary and sufficient conditions for the finite horizon H_- index larger than a given prescribed level beta>0 are given. Secondly, considering Markov jump linear uncertain stochastic systems, we obtain two results about finite horizon H_- index.

Keywords: Stochastic/uncertain systems, Markov processes, Simulation
Abstract: This paper studies the the stability of time-varying linear stochastic Markovian jump systems. Compared with the past literature, the effect of both time-varying parameter and Markovian jumps have been highlighted in the main results.

Keywords: Stochastic/uncertain systems, Complex networks
Abstract: This paper is concerned with the stability of neural stochastic Hopfield neural networks with time varying delays. Under some reasonable conditions, we obtain some sufficient criteria to check the exponential stability of the considered neural networks.

Keywords: Control applications, Cyberphysical systems
Abstract: A novel attack detection scheme is developed for linear discrete-time systems with unknown dynamics that are subject to the additive process and output measurements noise. A novel stochastic adaptive observer is proposed to estimate the state vector in the presence of noisy sensor measurements and uncertain dynamics, and also to generate the innovation signal to detect attacks using a modified χ2 detector. It has been shown that the innovation signal, which is defined as the difference between the measured and the estimated output from the observer, has a Gaussian distribution with non-zero mean. The modified χ2 detector uses the steady-state bound of the innovation signal. Not only this detector can detect false data injection attacks, but also sophisticated attacks like replay attacks can be detected. The learning involved in the system acts as a watermarking signal, which helps in the detection of stealthy attacks. Simulation results are presented to support the theoretical claims.

Keywords: Stochastic/uncertain systems, Verification and validation, Nonlinear systems
Abstract: This paper studies the problem of enforcing safety of a stochastic dynamical system over a finite time horizon. We use stochastic barrier functions as a means to quantify the probability that a system exits a given safe region of the state space in finite time. A barrier certificate condition that bounds the infinitesimal generator of the system, and hence bounds the expected value of the barrier function over the time horizon, is recast as a sum-of-squares optimization problem for efficient numerical computation. Unlike prior works, the proposed certificate condition includes a state-dependent bound on the infinitesimal generator, allowing for tighter probability bounds. Moreover, for stochastic systems for which the drift dynamics are affine-in-control, we propose a method for synthesizing polynomial state feedback controllers that achieve a specified probability of safety. Two case studies are presented that benchmark and illustrate the performance of our method.

Keywords: Complex systems, Control Technology, Stochastic/uncertain systems
Abstract: In this paper, the almost sure asymptotic stability of non-autonomous stochastic functional differential equations (SFDEs) is considered. By introducing a more general function and a technique for dividing the probability space, we establish LaSalle-type theorem for non-autonomous SFDEs. The results are illustrated by an examples.