Adaptive output-feedback Lyapunov-based model predictive control of nonlinear process systems

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.