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Barrier Lyapunov function-based robot control with an augmented neural network approximator
Industrial Robot ( IF 1.8 ) Pub Date : 2021-12-07 , DOI: 10.1108/ir-06-2021-0114
Zuguo Zhang 1 , Qingcong Wu 1 , Xiong Li 2 , Conghui Liang 2
Affiliation  

Purpose

Considering the complexity of dynamic and friction modeling, this paper aims to develop an adaptive trajectory tracking control scheme for robot manipulators in a universal unmodeled method, avoiding complicated modeling processes.

Design/methodology/approach

An augmented neural network (NN) constituted of radial basis function neural networks (RBFNNs) and additional sigmoid-jump activation function (SJF) neurons is introduced to approximate complicated dynamics of the system: the RBFNNs estimate the continuous dynamic term and SJF neurons handle the discontinuous friction torques. Moreover, the control algorithm is designed based on Barrier Lyapunov Function (BLF) to constrain output error.

Findings

Lyapunov stability analysis demonstrates the exponential stability of the closed-loop system and guarantees the tracking errors within predefined boundaries. The introduction of SJFs alleviates the limitation of RBFNNs on discontinuous function approximation. Owing to the fast learning speed of RBFNNs and jump response of SJFs, this modified NN approximator can reconstruct the system model accurately at a low compute cost, and thereby better tracking performance can be obtained. Experiments conducted on a manipulator verify the improvement and superiority of the proposed scheme in tracking performance and uncertainty compensation compared to a standard NN control scheme.

Originality/value

An enhanced NN approximator constituted of RBFNN and additional SJF neurons is presented which can compensate the continuous dynamic and discontinuous friction simultaneously. This control algorithm has potential usages in high-performance robots with unknown dynamic and variable friction. Furthermore, it is the first time to combine the augmented NN approximator with BLF. After more exact model compensation, a smaller tracking error is realized and a more stringent constraint of output error can be implemented. The proposed control scheme is applicable to some constraint occasion like an exoskeleton and surgical robot.



中文翻译:

具有增强神经网络逼近器的基于障碍 Lyapunov 函数的机器人控制

目的

考虑到动态和摩擦建模的复杂性,本文旨在以通用的非建模方法开发一种机器人机械手的自适应轨迹跟踪控制方案,避免复杂的建模过程。

设计/方法/方法

引入了由径向基函数神经网络 (RBFNNs) 和附加的 sigmoid-jump 激活函数 (SJF) 神经元组成的增强神经网络 (NN) 来逼近系统的复杂动力学:RBFNNs 估计连续动态项,SJF 神经元处理不连续的摩擦力矩。此外,控制算法是基于Barrier Lyapunov Function (BLF) 设计的,以约束输出误差。

发现

Lyapunov 稳定性分析证明了闭环系统的指数稳定性,并保证跟踪误差在预定义的范围内。SJFs 的引入缓解了 RBFNNs 在不连续函数逼近上的限制。由于 RBFNN 的快速学习速度和 SJF 的跳跃响应,这种改进的 NN 逼近器能够以较低的计算成本准确地重构系统模型,从而获得更好的跟踪性能。在机械手上进行的实验验证了所提出的方案与标准 NN 控制方案相比在跟踪性能和不确定性补偿方面的改进和优越性。

原创性/价值

提出了一种由RBFNN和附加的SJF神经元组成的增强型NN逼近器,可以同时补偿连续动态和非连续摩擦。该控制算法在具有未知动态和可变摩擦的高性能机器人中具有潜在用途。此外,这是第一次将增强的 NN 逼近器与 BLF 相结合。经过更精确的模型补偿,实现了更小的跟踪误差,可以实现更严格的输出误差约束。所提出的控制方案适用于一些约束场合,如外骨骼和手术机器人。

更新日期:2022-02-10
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