Explicit model predictive control of switched nonlinear systems: Stability and optimality

Process operation often involves controlled, discrete transitions between multiple, continuous dynamical modes of operation, in order to handle changes in raw materials, energy sources, product specifications and market demands. This leads to an overall p rocess behaviour that is more appropriately viewed as a hybrid (switched) system, i.e. intervals of piecewise continuous behaviour interspersed by discrete transitions. The hybrid nature of these systems and the presence of state and input constraints mak es them difficult to analyze and control. In this respect, model predictive control (MPC) methodology is very suitable for the stabilization and optimal control of switched nonlinear systems, since it allows the solution of complex constrained multivariab le control problems. This project will focus on the development of efficient methods and software tools for explicit solution of constrained MPC problems for switched nonlinear systems with scheduled mode transitions. The problem of stabilization of a sw itched process, transiting between its constituent modes at prescribed transition times, will be considered. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation, which i s an essential issue in safety-critical applications. The stability and the performance of the explicit MPC controller in closed-loop with the switched nonlinear system will be analyzed through test on several simulation examples.