Optimization-based hybrid control tools

The paper discusses a framework for modeling, analyzing and controlling systems whose behavior is governed by interdependent physical laws, logic rules, and operating constraints, denoted as Mixed Logical Dynamical (MLD) systems. They are described by linear dynamic equations subject to linear inequalities involving real and integer variables. MLD models are equivalent to various other system descriptions like Piecewise Affine (PWA) systems and Linear Complementarity (LC) systems. They have the advantage, however, that many problems of system analysis (like reachability/controllability, observability, and verification) and many problems of synthesis (like controller design and filter design) can be readily expressed as mixed integer linear or quadratic programs, for which many commercial software packages exist. In this paper we first recall MLD models and the modeling language HYSDEL (HYbrid Systems DEscription Language). Subsequently, we illustrate the use of Model Predictive Control (MPC) based on mixed-integer programming for hybrid MLD models, and the use of multiparametric programming for obtaining explicitly the equivalent piecewise linear control form of MPC. The eventual practical success of these methods will depend on progress in the development of the various optimization algorithms and tools so that problems of realistic size can be tackled.