Constrained nonlinear output regulation using model predictive control -- extended version
Johannes Köhler, Matthias A. Müller, Frank Allgöwer
备注：
Extended version of accepted paper in Transaction on Automatic Control, 2021. Contains the following additional results: Exponential bounds on the suboptimality index using an observability condition and an extension of the derived theory to the noisy error feedback case
We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of the proposed framework is that the application does not require the solution to classical regulator (Francis-Byrnes-Isidori) equations or any other offline design procedure. In particular, the proposed formulation simply minimizes the predicted output error, possibly with some input regularization. Instead of using terminal cost/sets or a positive definite stage cost as is standard in MPC theory, we build on the theoretical results by Grimm et al. 2005 using a detectability notion. The proposed formulation is applicable if the constrained nonlinear regulation problem is (strictly) feasible, the plant is incrementally stabilizable and incrementally input-output to state stable (i-IOSS/detectable). We show that for minimum phase systems such a design ensures exponential stability of the regulator manifold. We also provide a design procedure in case of unstable zero dynamics using an incremental input regularization and a nonresonance condition. Inherent robustness properties for the noisy error/output-feedback case are established under simplifying assumptions (e.g. no state constraints). The theoretical results are illustrated with an example involving offset free tracking with noisy error feedback. The paper also contains novel results for MPC without terminal constraints with positive semidefinite input/output stage costs that are of independent interest.
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A new strategy is proposed to control interior permanent magnet generators in dc microgrids interfaced through an active rectifier. The controller design is based on the decomposition of the system dynamics into slow and fast modes using singular perturbation theory. An inner current controller is developed based on output regulation techniques and an outer voltage controller is proposed using Nonlinear Model Predictive Control (NMPC). The NMPC regulates the dc bus voltage and minimizes the ac side losses. Simulation results are then presented based on realistic conditions for aircraft power s…
In this paper, the highly acknowledged advantages of the Model Predictive Control (MPC) approach are utilized to regulate the wind turbines' output power in the partial load region. In this region, the purpose of the designed controller is to capture maximum power from the wind. When the wind speed is above rated wind speed (full load region), control strategy is to keep output power and generator speed at their rated value. Because of the wind turbine's nonlinearities and constraints of the variables this is a challenging problem and two MPC-based methods are proposed, i.e. online and offline…
Distributed Model Predictive Control for Optimal Consensus of Constrained Heterogeneous Multi-agent Systems
The Hong Kong University of Science and Technology（香港科技大学）；The University of Hong Kong（香港大学）；HKU Shenzhen Institute of Research and Innovation（香港大学深圳研究院创新）；Beihang University（北京航空航天大学）；Peking University（北京大学）
