Last updated on December 15, 2014. This conference program is tentative and subject to change

This paper suggests a particular form of a nonlinear control law for civil structures designed to minimize one objective (a serviceability and performance measure) for small excitation and small response, and a different objective (life safety) for larger motion. An analytical approach is presented to cast the nonquadratic cost function in a form for which the HJB equation can be solved. The resulting optimal nonlinear control law can be written as the sum of a linear term, which is related to an LQR problem and is effective in small excitations, and some nonlinear terms that dominate when the excitation level is large. The performance of the proposed nonlinear control strategy is demonstrated by a numerical simulation for a simple building model subjected to different historical earthquakes and a synthetic ground motion, showing that it can successfully achieve both life safety (drift reduction) and serviceability (acceleration reduction) objectives at different excitation levels.

This paper proposes a robust economic model predictive controller, which takes advantage of constraint tightening techniques to guarantee feasibility despite modelling errors. Input-to-state stability is proven using a Lyapunov function. The advantages of this method are highlighted against alternative control structures in the application of power tracking for diesel engines in series hybrid type applications.

Finally, we discuss the fitting of the time-varying state space with an affine LPV state space model. This step is work in progress.

Special attention is given to the sparsity and structure in the frequency domain calculations.

This paper considers a particular randomized distributed control architecture introduced in our own recent work. In numerical results it was found that the associated mean-field model had attractive properties for purposes of control. In particular, when viewed as an input-output system, its linearization was found to be minimum phase.

In this paper we take a closer look at the control model. The results are summarized as follows:

(i) The Markov Decision Process framework of Todorov is extended to continuous time models, in which the ``control cost'' is based on relative entropy. This is the basis of the construction of a family of controlled Markovian generators.

(ii) A decentralized control architecture is proposed in which each agent evolves as a controlled Markov process. A central authority broadcasts a common control signal to each agent. The central authority chooses this signal based on an aggregate scalar output of the Markovian agents. This is the basis of the mean field model.

(iii) Provided the control-free system is a reversible Markov process, the following identity holds for the transfer function G obtained from the linearization,

Re(G(j omega)) = PSD_{Y}(omega)>=0, omega in R,

where the right hand side denotes the power spectral density for the output of any one of the individual (control-free) Markov processes.

In the present paper, we investigate the applicability of QSSAs from a stochastic viewpoint, by making use of the CMEs in the specific case of the double phosphorylation-dephosphorylation reaction. To this end, the stochastic approach is applied to the non-approximated original chemical network, as well as to the standard and total QSSAs, confirming by simulations the effectiveness and superiority of the latter with respect to the former.

The observer and the control approach are validated on realistic operating conditions.

The architecture is illustrated with the aid of a model problem involving locomotion of coupled planar rigid body systems, with two links. For this problem, the coupled oscillator feedback particle filter is designed and its control performance demonstrated in a simulation environment.

This raises a natural exploration-exploitation trade-off: should the market platform match existing buyers to known good quality sellers, or alternately, use existing buyers to ``explore'' new, unknown sellers? The former ensures that matches are always successful, leading to good user experience; the latter, though holding the potential of discovering new high-quality participants, can lead to poor user experience.

We investigate this tradeoff in a setting where existing buyers help uncover seller quality. We develop a stylized model, which captures the salient features inherent in such markets, while retaining analytical tractability. Our model uncovers a qualitative difference between exploration and exploitation, based on underlying network externalities between buyers and sellers in the market. In particular, in many settings, pure exploration achieves the optimal rate of successful matches.

This work is closely related and motivated by the study of the complexity of sub-Riemannian geodesics for generic regular distributions, i.e., whose derived flag has maximal growth vector. Of particular interest is the approximation of curves transversal to the distribution by admissible curves.

We also present a surprising example that shows that it is possible to simultaneously kill higher moments without increasing the number of self-intersections of the base curve.

In this paper, we consider fixed modes for arbitrary information structures, where certain control inputs may depend on some measurements but not others. We provide a comprehensive proof that the modes which can be altered by a static controller with the given structure can be moved by a dynamic one to any chosen location with arbitrary precision, thus generalizing and solidifying the second part of Wang and Davison's result. A previous paper discussed the first part.

This shows that a system can be stabilized by a linear time-invariant controller with the given information structure as long as all of the modes which are fixed with respect to that structure are in the left half-plane; an algorithm for synthesizing such a stabilizing decentralized controller is then distilled from the proof.

We characterize the systems through their fixed points, and equip them with two operators. We uncover properties of the operators, and express global systems through combinations of local systems. We enhance the theory with a notion of failure, and understand the class of shocks inducing a system to failure. We develop a notion of mu-rank to capture the energy of a system, and understand the minimal amount of effort required to fail a system, termed resilience. We deduce a dual notion of fragility and show that the combination of systems sets a limit on the amount of fragility inherited.