This work considers the problem of control of nonlinear systems subject to uncertainty. To this end, a Lyapunov‐based robust model predictive controller (MPC) design is integrated with a moving horizon based offset‐free mechanism to enable improved closed–loop performance. Simulation results are presented to illustrate the key ideas of the proposed design.
This work addresses the problem of output feedack control of nonlinear uncertain systems via adaptive Lyapunov-based model predictive control design. To this end, at every control implementation, a moving horizon mechanism is first utilized to generate current estimates of the uncertainty and states. The model with the current estimated uncertainty is then used in a Lyapunov-based model predictive controller to achieve uncertainty rejection. The key ideas are explained through an illustrative example and the application demonstrated on a networked reactor-separator process subject to measurement noise and uncertainty.
KEYWORDSadaptive MPC, Lyapunov-based model predictive control, nonlinearity, output feedback Int J Robust Nonlinear Control. 2018;28:1597-1609. wileyonlinelibrary.com/journal/rnc
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