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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.
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
CasADi: a software framework for nonlinear optimization and optimal control
TLDR
This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last 7 years.
On Average Performance and Stability of Economic Model Predictive Control
TLDR
It is shown that average performance of economic MPC is never worse than the optimal steady-state operation, and two modified MPC controllers are developed that asymptotically guarantee improved performance compared to optimal periodic control and satisfaction of constraints on average values of states and inputs.
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.
A Lyapunov Function for Economic Optimizing Model Predictive Control
TLDR
For a class of nonlinear systems and economic stage costs, this technical note constructs a suitable Lyapunov function, and the optimal steady-state solution of the economic stage cost is an asymptotically stable Solution of the closed-loop system under economic MPC.
Tutorial overview of model predictive control
TLDR
The concepts are introduced, a framework in which the critical issues can be expressed and analyzed are provided, and it is pointed out how MPC allows practitioners to address the trade-offs that must be considered in implementing a control technology.
Constrained linear quadratic regulation
TLDR
The constrained LQR outlined does not feature the undesirable mismatch between open-loop and closed-loop nominal system trajectories, which is present in the other popular forms of model predictive control (MPC) that can be implemented with a finite quadratic programming algorithm.
The stability of constrained receding horizon control
TLDR
An infinite horizon controller that allows incorporation of input and state constraints in a receding horizon feedback strategy is developed and guarantees nominal closed-loop stability for all choices of the tuning parameters in the control law.
Application of Interior-Point Methods to Model Predictive Control
We present a structured interior-point method for the efficient solution of the optimal control problem in model predictive control. The cost of this approach is linear in the horizon length,
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