Nash Game Based Mixed H2/H8 Model Predictive Control Applied With Laguerre-Wavelet Network Model

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Model predictive control (MPC) is one of the most successful approaches for controlling constrained processes. As MPC design explicitly uses a process model in the computation of the optimal control input, an efficient model, the closed-loop stability and robustness are the major issues in this controller design approach. The scope of this thesis broadly spans two areas: nonlinear system identification and robust MPC design. In nonlinear system identification, a newer kind of Wiener-type nonlinear model, namely Laguerre-Wavelet network model has been developed: the linear dynamic part is formed by the Laguerre filters and the static nonlinear part by the wavelet-network. The performance of the developed model is compared with suitable examples against a similar model of the same class. Various forms of robust MPC approaches have been reported in the literature. On the part of robust MPC design in the present thesis, a novel robust MPC approach is addressed with a game theoretic interpretation. A Nash game approach to mixed H2/H1 Model Predictive Control (NGM-MPC) for linear dynamic systems with actuator saturation is proposed. Two-player non-cooperative game strategy is adopted by solving two separate objective functions, viz., H2 and H1 performance measures, subject to constraints of the system dynamics and the bounded constraints on its control input. The problem ultimately reduces to solving a pair of cross-coupled Riccati equations. Although solving coupled Riccati equations resulting The efficacy of the proposed mixed H2/H1 MPC design is demonstrated with suitable examples by comparing its closed-loop performance and conservativeness with already existing mixed H2/H1 MPC design approach. The infinite horizon state feedback mixed H2/H1 MPC designs are analysed using set-theoretic approach for reasoning out their closed-loop performance and the associated conservativeness issues. The design issues of output feedback mixed H2/H1 MPC design are addressed, where three coupled algebraic Riccati equations are solved simultaneously. Furthermore, the output feedback controller is used with the Laguerre Wavelet Network model for controlling processes with parametric uncertainty, by considering it as disturbance rejection problem. The limitations of the proposed strategy are also discussed. The proposed system identification and robust control techniques are demonstrated on a benchmark process viz., continuous bioreactor. from linear-quadratic games for their exact solution by itself is of theoretical importance, in the present work it has been extended, for the first of its kind, to the regime of receding horizon control....
Supervisor: P. K. Saha