Data-driven based optimal output feedback control with low computation cost

IF 3.9 4区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Adaptive Control and Signal Processing Pub Date : 2024-05-13 DOI:10.1002/acs.3832

Xinyi Yu, Jiaqi Yu, Yongqi Zhang, Jiaxin Wu, Yan Wei, Linlin Ou

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Abstract

A partial model-free, data-driven adaptive optimal output feedback (OPFB) control scheme with low computational cost continuous-time is proposed in this paper. The design objective is to obtain the optimal control law by using measurable input and output data, without some knowledge of system model information. Firstly, the system states are decoupled into measurable and unmeasurable parts, and a new state-space equation is built to estimate the unmeasurable states by using a reduced-order observer. Based on this, a parametrization method is utilized to reconstruct the system states. Subsequently, by using the reconstructed states, the adaptive dynamic programming (ADP) Bellman equations based on policy-iteration (PI) and value-iteration (VI) are presented to solve the control problems with initially stable and unstable conditions, respectively. Then, the convergence of the system is proved. Compared with the early proposed OPFB algorithms, only the unknown internal state needs to be reconstructed. Therefore, the computation cost and design complexity are reduced for the proposed scheme. The effectiveness of the proposed scheme is verified through two numerical simulations. In addition, a practical inverted pendulum experiment is carried out to demonstrate the performance of the proposed scheme.

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基于数据驱动的低计算成本优化输出反馈控制

摘要本文提出了一种部分无模型、数据驱动、低计算成本的连续时间自适应最优输出反馈（OPFB）控制方案。设计目标是在不了解系统模型信息的情况下，利用可测量的输入和输出数据获得最优控制法则。首先，将系统状态解耦为可测量和不可测量两部分，并建立一个新的状态空间方程，利用降阶观测器来估计不可测量的状态。在此基础上，利用参数化方法重建系统状态。随后，利用重建的状态，提出基于策略迭代（PI）和值迭代（VI）的自适应动态编程（ADP）贝尔曼方程，分别解决初始条件稳定和不稳定的控制问题。然后，证明了系统的收敛性。与早期提出的 OPFB 算法相比，该算法只需重建未知的内部状态。因此，提出的方案降低了计算成本和设计复杂度。通过两次数值模拟验证了所提方案的有效性。此外，还进行了实际的倒立摆实验，以证明所提方案的性能。

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来源期刊

International Journal of Adaptive Control and Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

16.10%

发文量

163

审稿时长

5 months

期刊介绍： The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.