Predefined-time adaptive neural control for electro-hydraulic actuator systems with parameter uncertainties

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Intelligence Pub Date : 2026-10-13 DOI:10.1007/s10489-026-07535-0

Chen Wang, Jinping Ye, Xin Chen, Zhenlei Chen, Dan Jiang, Qing Guo

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引用次数: 0

Abstract

Electro-hydraulic actuator systems (EHASs) are widely used in industrial automation. Existing EHAS controllers mainly improve transient and steady-state performance, but guaranteeing tracking-error convergence within a designer-specified time remains challenging under parameter uncertainties and unknown external loads. To address this problem, a predefined-time adaptive neural backstepping controller is developed. The parameter uncertainties are reformulated as two lumped nonlinear terms and compensated using radial basis function neural networks (RBFNNs), while twice continuously differentiable (\(C^2\)) segmented functions regularize the low-order fractional-power terms to avoid singularities in the virtual controllers. Rigorous theoretical analysis establishes practical predefined-time stability: the tracking error and adaptive-parameter estimation errors converge to adjustable neighborhoods of zero within a designer-specified time. Simulation and comparative experimental results validate the method and demonstrate improved tracking accuracy over finite-time and conventional backstepping controllers.

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参数不确定电液执行器系统的预定义时间自适应神经控制

电液执行器系统在工业自动化中有着广泛的应用。现有的EHAS控制器主要提高暂态和稳态性能，但在参数不确定和未知外部负载的情况下，如何保证跟踪误差在设计人员指定的时间内收敛仍然是一个挑战。针对这一问题，提出了一种自适应神经反步控制器。将参数不确定性重新表述为两个集总非线性项，并使用径向基函数神经网络（RBFNNs）进行补偿，而两次连续可微（\(C^2\)）分段函数对低阶分数次项进行正则化以避免虚拟控制器中的奇异性。严格的理论分析建立了实际的预定义时间稳定性：跟踪误差和自适应参数估计误差在设计人员指定的时间内收敛到零的可调邻域。仿真和对比实验结果验证了该方法的有效性，表明该方法在有限时间内的跟踪精度优于传统的反步控制器。

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来源期刊

Applied Intelligence 工程技术-计算机：人工智能

CiteScore

6.60

自引率

20.80%

发文量

1361

审稿时长

5.9 months

期刊介绍： With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.