Title: Design of robust gain-scheduled model predictive controllers for nonlinear processes (½ hour)
Abstract: Gain-scheduling has proven to be a successful design methodology for nonlinear systems. However, in the absence of a sound theoretical analysis, these designs have no guarantees of robust stability, performance or even nominal stability of the overall gain-scheduled deign. In this talk, we will present such an analysis for one type of nonlinear gain-scheduled control system where scheduling is based on the process input. Conditions which guarantee robust stability and performance of the closed-loop systems are formulated as a finite set of Linear Matrix Inequalities (LMI) and hence, the resulting problem is numerically tractable. Based on these conditions, robust gain-scheduled MPC's (Model Predictive Controllers) are designed. A simulation study of a nonlinear continuous stirred tank reactor (CSTR) process indicates that this approach can lead to the design of efficient robust gain-scheduled controllers.