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Constrained model predictive control: Stability and optimality
TLDR
This review focuses on model predictive control of constrained systems, both linear and nonlinear, and distill from an extensive literature essential principles that ensure stability to present a concise characterization of most of the model predictive controllers that have been proposed in the literature.
Nonlinear and Adaptive Control Design [Book Review]
  • D. Mayne
  • Computer Science, Mathematics
    IEEE Transactions on Automatic Control
  • 1 December 1996
Robust model predictive control of constrained linear systems with bounded disturbances
This paper provides a novel solution to the problem of robust model predictive control of constrained, linear, discrete-time systems in the presence of bounded disturbances. The optimal control
Postface to “ Model Predictive Control : Theory and Design ”
The goal of this postface is to point out and comment upon recent MPC papers and issues pertaining to topics covered in the first printing of the monograph by Rawlings and Mayne (2009). We have tried
Robust receding horizon control of constrained nonlinear systems
TLDR
This paper presents a method for the construction of a robust dual-mode, receding horizon controller which can be employed for a wide class of nonlinear systems with state and control constraints and model error, and requires considerably less online computation than existingReceding horizon controllers for nonlinear, constrained systems.
Min-max feedback model predictive control for constrained linear systems
TLDR
The control schemes the authors discuss introduce the notion that feedback is present in the receding-horizon implementation of the control, which leads to improved performance, compared to standard model predictive control, and resolves the feasibility difficulties that arise with the min-max techniques.
Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations
TLDR
This work proposes a general theory for constrained moving horizon estimation, and applies this theory to develop a practical algorithm for constrained linear and nonlinear state estimation.
Model predictive control: Recent developments and future promise
  • D. Mayne
  • Engineering, Computer Science
    Autom.
  • 1 December 2014
This paper recalls a few past achievements in Model Predictive Control, gives an overview of some current developments and suggests a few avenues for future research.
A Second-order Gradient Method for Determining Optimal Trajectories of Non-linear Discrete-time Systems
ABSTRACT A second-order method of successively improving a control sequence for a non-linear discrete-time system is derived. One step convergence is obtained for linear systems with quadratic
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