Enhanced stability regions for model predictive control of nonlinear process systems

This work considers the problem of predictive control of nonlinear process systems subject to input constraints. Lyapunov-based tools are used to develop control- law independent characterizations of the stability region and this characterization is exploited via the constraints handling capabilities of model predicative controllers to expand on the set of initial conditions for which closed-loop stability can be achieved. The utilization of this idea is first illustrated for the case of linear systems and a predictive controller is designed that achieves closed-loop stability for every initial condition in the null controllable region. For nonlinear process systems, constraints are formulated requiring the process to evolve within the region from where continued decay of the Lyapunov function value is achievable and incorporated in the predictive control design, thereby expanding on the set of initial conditions from where closed-loop stability can be achieved. The proposed method is illustrated using a chemical reactor example.