Event-triggered adaptive neural prescribed performance admittance control for constrained robotic systems without velocity measurements

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS ISA transactions Pub Date : 2024-11-01 DOI:10.1016/j.isatra.2024.08.013
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Abstract

In this paper, an event-triggered adaptive neural prescribed performance admittance control (ETANPPAC) scheme is proposed to control the constrained robotic systems without velocity sensors. To ensure compliance during human–robot interaction, the reference trajectory is obtained by reshaping the desired trajectory for the robotic systems based on the admittance relationship, where a saturation function is used to constrain the reference trajectory, avoiding excessive contact forces that could render the trajectory inexecutable. Moreover, a barrier Lyapunov function is used to constrain the tracking errors for prescribed performance, where a velocity observer and a radial basis function neural network are designed to estimate the velocity and the uncertainty of the robotic systems, respectively, to enhance control performance. To reduce the communication burden, an event-triggered mechanism is introduced and the Zeno behavior is avoided with the event-triggered condition. The stability of the whole control scheme is analyzed by the Lyapunov function. Simulation and experimental tests demonstrate that the proposed ETANPPAC scheme can track the desired trajectory well under constraints and reduce the communication burden, thereby achieving better efficiency for controlling the robotic systems compared with similar control schemes.
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针对无速度测量的受约束机器人系统的事件触发自适应神经规定性能导纳控制。
本文提出了一种事件触发自适应神经规定性能导纳控制(ETANPPAC)方案,用于控制没有速度传感器的受约束机器人系统。为确保人机交互过程中的顺应性,根据导纳关系重塑机器人系统的期望轨迹,从而获得参考轨迹,其中饱和函数用于约束参考轨迹,避免过大的接触力导致轨迹无法执行。此外,为了获得规定的性能,还使用了屏障 Lyapunov 函数来约束跟踪误差,并设计了速度观测器和径向基函数神经网络,分别用于估计机器人系统的速度和不确定性,以提高控制性能。为了减轻通信负担,引入了事件触发机制,并通过事件触发条件避免了芝诺行为。利用 Lyapunov 函数分析了整个控制方案的稳定性。仿真和实验测试表明,所提出的 ETANPPAC 方案能在约束条件下很好地跟踪所需的轨迹,并减少了通信负担,因此与类似的控制方案相比,能更好地控制机器人系统。
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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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Editorial Board ROM-based stochastic optimization for a continuous manufacturing process Multiscale dynamically parallel shrinkage network for fault diagnosis of aviation hydraulic pump and its generalizable applications Uncertainty propagation from probe spacing to Fourier 3-probe straightness measurement Event-triggered adaptive neural prescribed performance admittance control for constrained robotic systems without velocity measurements
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