Robust Constrained MPC based on Nominal Performance Cost with Applications in Chemical Processes

AbstractIn this paper, a synthesis approach to robust constrained model predictive control (MPC) for uncertain polytopic discrete-time systems is presented. An overall algorithm is derived by using parameter-dependent Lyapunov function. The nominal model of the plant is included in the controller design in order to improve control performance. The optimization problem at each time step is formulated as the convex optimization problem involving linear matrix inequalities (LMI). Thus, the algorithm is computationally tractable. The algorithm is proved to guarantee robust stability. The controller design is illustrated with an example of continuous stirred-tank reactor