Purpose

This paper aims to design an event-triggered adaptive prescribed performance controller for flexible manipulators, with the primary objectives of achieving output performance constraints and addressing communication resource limitations.

Design/methodology/approach

The authors propose a novel prescribed performance barrier Lyapunov function (PP-BLF) that considers both output and tracking performance constraints. The PP-BLF ensures that the system's output, transient behavior and steady-state performance, adhere to prescribed constraints. The boundary of the PP-BLF is established by an exponential function that decays over time. Notably, the PP-BLF can be applied seamlessly in unconstrained cases without necessitating controller redesign. Moreover, the controller design incorporates an event-triggered mechanism, effectively reducing the frequency of controller updates and optimizing the utilization of communication resources. Additionally, the authors employ adaptive techniques to estimate the system's unknown parameters and approximate unknown nonlinear functions using radial basis function neural networks (RBFNN). To address the challenge of “complexity explosion”, dynamic surface technology is employed.

Findings

Numerical simulations are conducted under five different cases to verify the effectiveness of the proposed controller. The results demonstrate that the controller successfully constrains the output tracking error within the prescribed performance boundary. Moreover, compared with the traditional time-triggered mechanism, the event-triggered mechanism significantly reduces the controller's update frequency, resolving the problem of limited communication resources.

Originality/value

The paper reduces the update frequency of control signals and improves resource utilization through an event-triggered mechanism in the form of relative thresholds. The authors recognize that the event-triggered mechanism may impact the output performance of the system. To address this challenge, the authors propose a prescribed performance Barrier Lyapunov Function (PP-BLF). The PP-BLF is designed to effectively constrain the output performance of the system, ensuring satisfactory control even when the control signal updates are reduced.

中文翻译：

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具有输出约束的柔性关节机械臂的事件触发自适应规定性能控制

目的

本文旨在为柔性机械臂设计一种事件触发的自适应规定性能控制器，其主要目标是实现输出性能约束和解决通信资源限制。

设计/方法论/途径

作者提出了一种新颖的规定性能障碍 Lyapunov 函数（PP-BLF），该函数考虑了输出和跟踪性能约束。PP-BLF 确保系统的输出、瞬态行为和稳态性能遵守规定的约束。PP-BLF 的边界由随时间衰减的指数函数确定。值得注意的是，PP-BLF 可以在不受约束的情况下无缝应用，而无需重新设计控制器。此外，控制器设计融入了事件触发机制，有效降低了控制器更新频率，优化了通信资源的利用率。此外，作者采用自适应技术来估计系统的未知参数，并使用径向基函数神经网络 (RBFNN) 来近似未知的非线性函数。为了应对“复杂性爆炸”的挑战，采用了动态表面技术。

发现

在五种不同情况下进行数值模拟，以验证所提出的控制器的有效性。结果表明控制器成功地将输出跟踪误差限制在规定的性能边界内。而且，与传统的时间触发机制相比，事件触发机制显着降低了控制器的更新频率，解决了通信资源有限的问题。

原创性/价值

论文通过相对阈值形式的事件触发机制，降低了控制信号的更新频率，提高了资源利用率。作者认识到事件触发机制可能会影响系统的输出性能。为了应对这一挑战，作者提出了规定的性能屏障李亚普诺夫函数（PP-BLF）。PP-BLF旨在有效约束系统的输出性能，即使在控制信号更新减少的情况下也能确保令人满意的控制。