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Constrained Nonlinear Output Regulation Using Model Predictive Control
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DOI：10.1109/TAC.2021.3081080.png)
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摘要
En 中文
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. 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. The theoretical results are illustrated with an example involving offset-free tracking.
Keyword:
Regulation
Regulators
Mathematical model
Trajectory
Trajectory tracking
Steady-state
Predictive control
Constrained control
disturbance rejection
incremental system properties
minimum phase
nonresonance condition
output regulation
predictive control for nonlinear systems
trajectory tracking
zero dynamics
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期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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