Distributed and economic model predictive control
Matthias A. Müller
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Matthias A. Müller, Distributed and economic model predictive control (2014), Logos Verlag, Berlin, ISBN: 9783832599515
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Beschreibung / Abstract
In this thesis, we study model predictive control (MPC) schemes for control tasks which go beyond the classical objective of setpoint stabilization. In particular, we consider two classes of such control problems, namely distributed MPC for cooperative control in networks of multiple interconnected systems, and economic MPC, where the main focus is on the optimization of some general performance criterion which is possibly related to the economics of a system. The contributions of this thesis are to analyze various systems theoretic properties occurring in these type of control problems, and to develop distributed and economic MPC schemes with certain desired (closed-loop) guarantees.
To be more precise, in the field of distributed MPC we propose different algorithms which are suitable for general cooperative control tasks in networks of interacting systems. We show that the developed distributed MPC frameworks are such that the desired cooperative goal is achieved, while coupling constraints between the systems are satisfied. Furthermore, we discuss implementation and scalability issues for the derived algorithms, as well as the necessary communication requirements between the systems.
In the field of economic MPC, the contributions of this thesis are threefold. Firstly, we analyze a crucial dissipativity condition, in particular its necessity for optimal steady-state operation of a system and its robustness with respect to parameter changes. Secondly, we develop economic MPC schemes which also take average constraints into account. Thirdly, we propose an economic MPC framework with self-tuning terminal cost and a generalized terminal constraint, and we show how self-tuning update rules for the terminal weight can be derived such that desirable closed-loop performance bounds can be established.
To be more precise, in the field of distributed MPC we propose different algorithms which are suitable for general cooperative control tasks in networks of interacting systems. We show that the developed distributed MPC frameworks are such that the desired cooperative goal is achieved, while coupling constraints between the systems are satisfied. Furthermore, we discuss implementation and scalability issues for the derived algorithms, as well as the necessary communication requirements between the systems.
In the field of economic MPC, the contributions of this thesis are threefold. Firstly, we analyze a crucial dissipativity condition, in particular its necessity for optimal steady-state operation of a system and its robustness with respect to parameter changes. Secondly, we develop economic MPC schemes which also take average constraints into account. Thirdly, we propose an economic MPC framework with self-tuning terminal cost and a generalized terminal constraint, and we show how self-tuning update rules for the terminal weight can be derived such that desirable closed-loop performance bounds can be established.
Inhaltsverzeichnis
- BEGINN
- 1 Introduction
- 1.1 Motivation
- 1.2 Research topic overview
- 1.3 Contributions and outline of this thesis
- 2 Background
- 2.1 Stabilizing MPC
- 2.2 Economic MPC
- 2.3 Summary
- 3 Distributed MPC for cooperative control
- 3.1 Problem setup
- 3.2 Distributed MPC schemes for general cooperative control problems
- 3.3 Application to consensus and synchronization
- 3.4 Summary
- 4 Economic MPC: Dissipativity, average constraints, and self-tuning terminal cost
- 4.1 Dissipativity in economic MPC
- 4.2 Average constraints
- 4.3 Economic MPC with self-tuning terminal cost
- 4.4 Summary
- 5 Distributed economic MPC
- 5.1 Problem setup and distributed economic MPC scheme
- 5.2 Convergence analysis of distributed economic MPC
- 5.3 Application: Consensus under conflicting objective
- 5.4 Summary and discussion
- 6 Conclusions
- 6.1 Summary and discussion
- 6.2 Outlook
- A Stability of discrete-time systems
- B Nonlinear programming