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Dynamic Output Feedback MPC with Pre-optimized Feedback Estimator Gain
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AI
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DOI：10.1016/j.jfranklin.2026.108711.png)
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Abstract
En 中文
In the dynamic output feedback model predictive control (MPC) design, optimizing both the estimator and controller gains on-line usually requires the solution of a non-convex optimization problem. In this paper, we show that through an appropriate lexicographic optimization problem formulation, the estimator gain can be pre-optimized so that the on-line MPC problem reduces to a convex one. The core of the proposed approach consists in formulating the stability guarantee condition by a multi-step quadratic bounded robust invariant set and optimizing the step-ahead feedback estimator gains in a lexicographic order. The key is to decoupling the step-ahead feedback estimator gains from the controller design so that they can be optimized independently. The recursive feasibility and stability of the proposed approach are guaranteed. A numerical example demonstrates the performance and region of attraction superiorities of the proposed approach.
Keywords:
Dynamic Output Feedback MPC
Pre-optimized Estimator Gain
Lexicographic Optimization
Convex Optimization
Robust Invariant Set
Journal
J
IF:
4.2
Papers:
812
Citations:
0
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Cited Papers