For discrete-time linear time-invariant systems with constraints on inputs and states, we
describe a method to determine explicitly, as a function of the initial state, the solution to optimal …

This paper proposes a framework for modeling and controlling systems described by
interdependent physical laws, logic rules, and operating constraints, denoted as mixed logical …

This paper gives an overview of robustness in Model Predictive Control (MPC). After reviewing
the basic concepts of MPC, we survey the uncertainty descriptions considered in the MPC …

Model Predictive Control (MPC), the dominant advanced control approach in industry over
the past twenty-five years, is presented comprehensively in this unique book. With a simple, …

For discrete-time linear time invariant systems with constraints on inputs and states, we
develop an algorithm to determine explicitly, the state feedback control law which minimizes a …

We prove, in a constructive way, the equivalence between piecewise affine systems and a
broad class of hybrid systems described by interacting linear dynamics, automata, and …

This paper presents a computational framework for modeling hybrid systems in discrete-time.
We introduce the class of discrete hybrid automata (DHA) and show its relation with …

Model-based design is well recognized in industry as a systematic approach to the development,
evaluation, and implementation of feedback controllers. Model predictive control (MPC) …

Explicit solutions to constrained linear model predictive control problems can be obtained
by solving multi-parametric quadratic programs (mp-QP) where the parameters are the …

Explicit model predictive control (MPC) addresses the problem of removing one of the main
drawbacks of MPC, namely the need to solve a mathematical program on line to compute the …