Deterministic Learning of Manipulators With Closed Architecture Based on Outer-loop Speed Compensation Control
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摘要: 针对未开放力矩控制接口的一类封闭机器人系统, 提出一种基于外环速度补偿的确定学习控制方案. 该控制方案考虑机器人受到未知动力学影响, 且具有未知内环比例积分(Proportional-integral, PI)速度控制器. 首先, 利用宽度径向基函数(Radial basis function, RBF)神经网络对封闭机器人的内部未知动态进行逼近, 设计外环自适应神经网络速度控制指令. 在实现封闭机器人稳定控制的基础上, 结合确定学习理论证明了宽度RBF神经网络的学习能力, 提出基于确定学习的高精度速度控制指令. 该控制方案能够保证被控封闭机器人系统的所有信号最终一致有界且跟踪误差收敛于零的小邻域内. 在所提控制方案中, 通过引入外环补偿控制思想和宽度神经网络动态增量节点方式, 减小了设备计算负荷, 提高了速度控制下机器人的运动性能, 解决了市场上封闭机器人系统难以设计力矩控制的难题, 实现了不同工作任务下的高精度控制. 最后数值系统仿真结果和UR5机器人实验结果验证了该方案的有效性.Abstract: In this paper, a deterministic learning outer-loop speed compensation control scheme is proposed for a class of manipulator systems with closed architecture and without open torque control interface. The proposed scheme focuses on that the manipulator is affected by unknown modelling dynamics and has an unknown inner-loop proportional-integral (PI) speed controller. Firstly, the broad radial basis function (RBF) neural network is used to approximate the internal unknown dynamics of the manipulator with closed architecture, and the outer-loop adaptive neural network speed control command is designed by using the Lyapunov function. Based on the stable control of manipulator with closed architecture, the dynamic learning ability of RBF neural network is verified, and then the high-accuracy speed control command is designed based on the deterministic learning theory. The proposed control scheme guarantees that all signals of the manipulator system with closed architecture are ultimately uniformly bounded, and the tracking error converges to a small neighborhood of zero. By the combination of outer-loop compensation control and dynamic incremental node of broad neural networks, the proposed scheme reduces the computing load, improves the motion performance of the robot under speed control, solves the torque control design difficulty of the closed manipulator, and realizes high-precision control in different working tasks. Finally, simulation results of numerical system and experimental results of UR5 robot are used to show the effectiveness of the proposed scheme.
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图 1 封闭机器人控制系统框图
Fig. 1 Schematic diagram of manipulators with closed architecture control system
图 2 封闭机器人关节角位置跟踪效果(自适应控制)
Fig. 2 Angular-position tracking performances of two joints for the manipulator with closed architecture (Adaptive control)
图 4 神经网络对未知动态$ f(Z_{1}) $学习效果(自适应控制)
Fig. 4 Neural network＇s learning performance of unknown dynamics $ f(Z_{1}) $ (Adaptive control)
图 5 封闭机器人关节角位置跟踪误差(控制方案对比)
Fig. 5 Angular-position tracking errors of two joints for the manipulator with closed architecture (Comparison of different control methods)
图 7 UR5 机器人关节角位置跟踪效果(自适应控制)
Fig. 7 Angular-position tracking performance of UR5 (Adaptive control)
图 9 UR5机器人关节角位置跟踪误差(学习控制对比)
Fig. 9 Angular-position tracking errors of UR5 (Compared to learning control)
表 1 仿真结果对比
Table 1 Comparison of simulation results
神经元数 MAE (前100 s) 仿真时长(s) ANC 500 s (均匀布点) 6561 $z_{1,1}$ 0.0166 403.61 $z_{1,2}$ 0.0131 ANC 500 s (宽度RBF网络) 425 $z_{1,1}$ 0.0192 147.16 $z_{1,2}$ 0.0196 LC 500 s (均匀布点) 6561 $z_{1,1}$ 0.0038 299.47 $z_{1,2}$ 0.0033 LC 500 s (宽度RBF网络) 425 $z_{1,1}$ 0.0056 82.11 $z_{1,2}$ 0.0061 
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