A new control for the pneumatic muscle bionic legged robot based on neural network

IF 1.5 Q3 AUTOMATION & CONTROL SYSTEMS IET Cybersystems and Robotics Pub Date : 2022-10-09 DOI:10.1049/csy2.12065
Chaoyue Xu, Feifei Qin, Kun Zhou, Binrui Wang, Yinglian Jin
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Abstract

The bionic joints composed of pneumatic muscles (PMs) can simulate the motion of biological joints. However, the PMs themselves have non-linear characteristics such as hysteresis and creep, which make it difficult to achieve high-precision trajectory tracking control of PM-driven robots. In order to effectively suppress the adverse effects of non-linearity on control performance and improve the dynamic performance of PM-driven legged robot, this study designs a double closed-loop control structure based on neural network. First, according to the motion model of the bionic joint, a mapping model between PM contraction force and joint torque is proposed. Second, a control strategy is designed for the inner loop of PM contraction force and the outer loop of bionic joint angle. In the inner control loop, a feedforward neuron Proportional-Integral-Derivative controller is designed based on the PM three-element model. In the control outer loop, a sliding mode robust controller with local model approximation is designed by using the radial basis function neural network approximation capability. Finally, it is verified by simulation and physical experiments that the designed control strategy is suitable for humanoid motion control of antagonistic PM joints, and it can satisfy the requirements of reliability, flexibility, and bionics during human–robot collaboration.

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基于神经网络的气动肌肉仿生腿机器人新控制
由气动肌肉组成的仿生关节可以模拟生物关节的运动。然而,由于电机本身具有滞后和蠕变等非线性特性,使得电机驱动机器人难以实现高精度的轨迹跟踪控制。为了有效抑制非线性对控制性能的不利影响,提高pm驱动的腿式机器人的动态性能,本研究设计了一种基于神经网络的双闭环控制结构。首先,根据仿生关节的运动模型,建立了PM收缩力与关节力矩的映射模型;其次,设计了PM收缩力内环和仿生关节角度外环的控制策略;在内部控制回路中,基于PM三元模型设计了前馈神经元比例-积分-导数控制器。在控制外环中,利用径向基函数神经网络的逼近能力,设计了具有局部模型逼近的滑模鲁棒控制器。最后,通过仿真和物理实验验证了所设计的控制策略适用于对抗性PM关节的类人运动控制,能够满足人机协作对可靠性、灵活性和仿生性的要求。
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来源期刊
IET Cybersystems and Robotics
IET Cybersystems and Robotics Computer Science-Information Systems
CiteScore
3.70
自引率
0.00%
发文量
31
审稿时长
34 weeks
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