Motivated by the fact that many nonlinear plants can be represented through Linear
Parameter Varying (LPV) embedding, and being this framework very popular for control …

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 …

This article provides an overview of model predictive control (MPC) frameworks for dynamic
operation of nonlinear constrained systems. Dynamic operation is often an integral part of …

Nonlinear Model Predictive Control often suffers from excessive computational complexity,
which becomes critical when fast plants are to be controlled. This papers presents an …

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 …

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 …

A novel closed-loop model-based predictive control (MPC) strategy for input-saturated
polytopic linear parameter varying (LPV) discrete-time systems is proposed. It is postulated …

Over the past decades, the advantages of optimization-based control techniques over
conventional controllers inspired developments that enabled the use of model predictive …

This paper presents a heterogeneously parameterized tube-based model predictive control
(MPC) design applicable to linear parameter-varying (LPV) systems. In a heterogeneous …

The ability to solve model predictive control (MPC) problems of linear time-invariant systems
explicitly and offline via multi-parametric quadratic programming (mp-QP) has become a …