Adaptive Performance Control for Input Constrained MIMO Nonlinear Systems

IF 8.6 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-09-25 DOI:10.1109/TSMC.2024.3462728
Panagiotis S. Trakas;Charalampos P. Bechlioulis
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Abstract

In this work, we propose an approximation-free adaptive performance control scheme for unknown high-relative degree, multi-input-multioutput (MIMO) nonlinear systems with saturation on the control input signal. We introduce a novel reconciling adaptive modification of the predefined performance specifications based on the input constraints of the controlled plant, providing the best-feasible output performance. The automatic gain tuning in combination with the simplicity of the proposed controller enhance its robustness and enable its easy deployment in practical scenarios. Notably, the introduced control methodology ensures the necessary compromise between input-output constraints on a semi-global sense for ISS systems. However, the stability attributes for general nonlinear systems are inevitably limited to compact domains due to the inherent conflict between performance demand and actuation capability. In this context, we provide a sufficient closed-loop stability criterion through Lyapunov analysis. Finally, illustrative simulation studies and experimental results clarify and verify the efficacy of the proposed controller.
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输入受限多输入多输出非线性系统的自适应性能控制
在这项研究中,我们针对控制输入信号饱和的未知高相对度、多输入多输出(MIMO)非线性系统提出了一种无近似自适应性能控制方案。我们根据受控工厂的输入约束条件,引入了一种新颖的调和自适应修改预定义性能指标的方法,以提供最佳可行的输出性能。自动增益调整与拟议控制器的简易性相结合,增强了控制器的鲁棒性,使其能够轻松应用于实际场景。值得注意的是,引入的控制方法确保了 ISS 系统在半全局意义上的输入输出约束之间的必要折衷。然而,由于性能需求与执行能力之间的内在冲突,一般非线性系统的稳定性属性不可避免地局限于紧凑域。在这种情况下,我们通过 Lyapunov 分析提供了充分的闭环稳定性标准。最后,说明性仿真研究和实验结果澄清并验证了所提控制器的功效。
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来源期刊
IEEE Transactions on Systems Man Cybernetics-Systems
IEEE Transactions on Systems Man Cybernetics-Systems AUTOMATION & CONTROL SYSTEMS-COMPUTER SCIENCE, CYBERNETICS
CiteScore
18.50
自引率
11.50%
发文量
812
审稿时长
6 months
期刊介绍: The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.
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