Model predictive control (MPC) is a favored method for handling constrained linear control problems. Normally, the MPC optimization problem is solved on-line, but in ‘explicit MPC’ an explicit precomputed feedback law is used for each region of active constraints (Bemporad et al., 2002). In this paper we make a link between this and the ‘self-optimizing control’ idea of finding simple policies ...

Model predictive control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. This paper focuses on exploring the inclusion...

Strong feasibility of MPC problems is usually enforced by constraining the state at the final prediction step to a controlled invariant set. However, such terminal constraints fail to enforce strong feasibility in a rich class of MPC problems, for example when employing move-blocking. In this paper a generalized, least restrictive approach for enforcing strong feasibility of MPC problems is pro...

This paper proposes a discrete-time adaptive model predictive control (MPC) algorithm for a class of constrained linear time-invariant systems, which updates the estimation of system parameters on-line and produces the control input subject to the given input/state constraints. This method is based on a robust MPC algorithm using comparison model which enable us to estimate the prediction error...

This thesis is concerned with the theoretical foundations of Robust Model Predictive Control and its application to tracking control problems. Its first part provides an introduction to MPC for constrained linear systems as well as a survey of different Robust MPC methodologies. The second part consists of a discussion of the recently developed Tube-Based Robust MPC framework and its extension ...

The focus of this chapter is on MPC of constrained dynamic systems, both linear and nonlin‐ ear, to illuminate the ability of MPC to handle constraints that makes it so attractive to in‐ dustry. We first give an overview of the origin of MPC and introduce the definitions, characteristics, mathematical formulation and properties underlying the MPC. Furthermore, MPC methods for linear or nonlinea...

Any proper operation could be translated as a constrained optimization problem inside a WWTP, whose nonlinear behavior renders its control problems quite attractive for performance of multivariable optimization–based control technique algorithms, such as NMPC. The main advantage of this control technique lies in its ability to handle model nonlinearity as well as various types of constraints on...

The control of fluidized-bed operations processes is still one of the major areas of research due to the complexity of the process and the inherent nonlinearity and varying dynamics involved in its operation. There are varieties of problems in chemical engineering that can be formulated as NonLinear Programming (NLPs). The quality of the developed solution significantly affects the performa...

Wireless sensor networks (WSNs) are becoming fundamental components of modern control systems due to their flexibility, ease of deployment and low cost. However, the energy-constrained nature of WSNs poses new issues in control design; in particular the discharge of batteries of sensor nodes, which is mainly due to radio communications, must be taken into account. In this paper we present a nov...

Multiparametric quadratic and linear programming theory has been applied with success for implementing deterministic MPC controllers. In this note, we have proposed to apply the approximate multipara-metric convex programming solver of [18] to the robust MPC control scheme proposed in [7]. An explicit description of the control law is obtained for ease of implementation of robust MPC. The contr...