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This paper describes a new robust model predictive control (MPC) scheme to control the discrete-time linear parameter-varying input-output models subject to input and output constraints. Closed-loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal set, which are solved offline, for the underlying online MPC optimization problem. The main attractive feature of the proposed scheme in comparison with previously published results is that all offline computations are now based on the convex optimization problem, which significantly reduces conservatism and computational complexity. Moreover, the proposed scheme can handle a wider class of linear parameter-varying input-output models than those considered by previous schemes without increasing the complexity. For an illustration, the predictive control of a continuously stirred tank reactor is provided with the proposed method. Copyright 2017 John Wiley & Sons, Ltd. This work was supported by National Priorities Research Program (NPRP) Grant 5-574-2-233 from the Qatar National Research Fund (a member of the Qatar Foundation). Scopus

In this paper, the problem of integrated fault detection, isolation, and control design of continuous-time Markovian jump linear systems with uncertain transition probabilities is introduced and addressed for the first time in the literature. A single Markovian jump module designated as the integrated fault detection, isolation, and control under a mixed robust H?/H-/H1 framework is considered to simultaneously achieve the desired detection, isolation, and control objectives. Conventional mixed robust H?/H-/H1 approaches to the fault detection and isolation problem lead to conservative results due to the selection of identical Lyapunov matrices. Consequently, the extended linear matrix inequality methodology is utilized in this work to reduce the conservativeness of standard approaches by introducing additional matrix variables so that the coupling of Lyapunov matrices with the system matrices is eliminated. Simulation results for an application to the GE F-404 aircraft engine system illustrate the effectiveness and capabilities of our proposed design methodologies. Comparisons with relevant work in the literature are also provided to demonstrate the advantages of our proposed solutions. Copyright - 2017 John Wiley & Sons, Ltd. This publication was made possible by the National Priorities Research Program through grant 5-045-2-017 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

With the advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in understanding the molecular properties of anesthetic agents. However, there has been little focus on how the molecular properties of anesthetic agents lead to the observed macroscopic property that defines the anesthetic state-that is, lack of responsiveness to noxious stimuli. In this chapter we use dynamical system theory to develop a mechanistic mean field model for neural activity to study the anesthetic cascade. The proposed synaptic drive firing rate model predicts the conscious-unconscious transition as the implied anesthetic concentration increases, where excitatory neural activity is characterized by a Poincar-Andronov-Hopf bifurcation with the awake state transitioning to a stable limit cycle and then subsequently to an asymptotically stable unconscious equilibrium state. Furthermore, we address the more general question of synchronization of neural activity without mean field assumptions. We do this by focusing on a postulated subset of inhibitory neurons that are not themselves connected to other inhibitory neurons. Finally, several numerical experiments are presented to illustrate the different aspects of the proposed theory. Scopus