某新型车载多管负载行进间稳定控制方法

doi:10.12382/bgxb.2021.0782

摘要/Abstract

摘要：

针对某新型车载多管负载存在内外扰动影响、行进间不易稳定的难点，设计一种扰动速度神经网络自适应补偿的稳定控制器。优化设计负载结构使耳轴两端的载荷基本平衡，减小控制过程中由于克服不平衡力矩所产生的能量消耗，以及非线性力矩干扰。稳定控制器采用PI控制计算主控制量；由扰动速度、扰动加速度作为输入构成单神经元控制器计算补偿控制量；利用扩张状态观测器观测载体行进间负载所受到的扰动速度，利用混合微分器得到扰动加速度；由扰动速度、扰动加速度构成单神经元控制器计算补偿控制量；利用RBF神经网络提供梯度信息，对速度补偿系数和加速度补偿系数进行在线学习。数值仿真及台架试验结果表明，所设计稳定控制器具有较强的自学习和自适能力，对不同频率、幅值的扰动均具有鲁棒性，参数学习时间小于3.7 s，积分位置误差均方差小于0.33 mil，由此验证了所提出控制策略对于提高某新型车载多管负载稳定控制精度的可行性和有效性。

关键词: 车载多管负载, 分数阶PID控制, 扩张状态观测器, 混合微分器, RBF神经网络

Abstract:

A novel vehicle multi-tube load faces internal and external disturbances, as well as instability on the move in travelling. In order to solve these problems, a stable controller with adaptive compensation of disturbance velocity is designed based on neural network. The load structure is optimized to balance the loads at both ends of the trunnion and reduce energy consumption and nonlinear interference caused by the unbalanced torque. The main control quantity is calculated by PI controller, and the velocity and acceleration of disturbance are obtained by the extended state observer and the hybrid differentiator, respectively. The single neuron controller is adopted to calculate the compensation control quantity, of which the inputs are disturbance velocity and acceleration. Gradient information is employed by the RBF neural network for online learning of the compensation coefficients of velocity and acceleration. Numerical simulation and bench test results show that the proposed stability controller has strong self-learning and adaptation ability, and it is robust to disturbances of different frequencies and amplitudes. The time for parameter learning is less than 3.7 seconds, and the mean square deviation of integral position errors is less than 0.33 mil. The results verify the feasibility and effectiveness of the proposed controller for vehicle multi-tube loads.

Key words: vehicle multi-tube load, fractional order PID, extended state observer, hybrid differentiator, RBFNN

高强, 侯远龙, 吕明明, 毛斌, 侯润民, 羊书毅, 吴斌. 某新型车载多管负载行进间稳定控制方法[J]. 兵工学报, 2023, 44(3): 736-747.

GAO Qiang, HOU Yuanlong, LÜ Mingming, MAO Bin, HOU Runmin, YANG Shuyi, WU Bin. A Marching Stable Control Method for a New Vehicle-Mounted Multi-Tube Load[J]. Acta Armamentarii, 2023, 44(3): 736-747.

图/表 20

图1 某新型车载多管负载系统结构示意图

Fig. 1 Structural diagram of a new vehicle-mounted multi-tube load system

图2 控制原理框图

Fig. 2 Block diagram of the controller

图3 正弦速度扰动观测图

Fig. 3 Diagram of Sine velocity disturbance observations

图4 正弦速度扰动观测误差图

Fig. 4 Diagram of Sine velocity disturbance observation errors

图5 扰动加速度对比图

Fig. 5 Comparison of disturbed accelerations

图6 扰动0.25Hz/0.5○积分位置误差图

Fig. 6 Position errors for the disturbance of 0.25 Hz/0.5○

图7 扰动0.25 Hz/2.0○积分位置误差图

Fig. 7 Position errors for the disturbance of 0.25 Hz/2.0○

图8 神经网络学习的扰动0.25 Hz/2.0○积分位置误差图

Fig. 8 Position errors for the disturbance of 0.25 Hz/2.0○ with NN

图9 扰动0.25 Hz/2.0○ 时β1 和 β2自学习曲线

Fig. 9 β1 and β2 learning curves for the disturbance of 0.25 Hz/2.0○

图10 神经网络学习的扰动0.5 Hz/4.0○积分位置误差图

Fig. 10 Position errors for the disturbance of 0.5 Hz/4.0○ with NN

图11 扰动0.5 Hz/4.0○ 时β1和 β2自学习曲线

Fig.11 β1 and β2 learning graph of disturbance 0.5 Hz/4.0○

图12 神经网络学习的扰动1.0 Hz/2.0○积分位置误差图

Fig. 12 Position errors for the disturbance of 1.0 Hz/2.0○ with NN

图13 扰动1.0 Hz/2.0○ 时β1 和 β2自学习曲线

Fig.13 β1 and β2 learning graph of disturbance 1.0 Hz/2.0○

图14 混合微分器和SOSMD位置误差对比图

Fig. 14 Comparison of position errors using HD and SOSMD

图15 摇摆台图

Fig. 15 Stewart platform

图16 摇摆台上位机操作界面

Fig. 16 Operational interface of the host computer of a Stewart platform

图17 扰动0.25 Hz/2.0○时摇摆台试验积分位置误差图

Fig. 17 Position errors for the disturbance of 0.25 Hz/2.0○ on a Stewart platform

图18 扰动0.5 Hz/4.0○时摇摆台试验积分位置误差图

Fig. 18 Position errors for the disturbance of 0.5 Hz/4.0○ on a Stewart platform

图19 扰动1.0 Hz/2.0○时摇摆台试验积分位置误差图

Fig. 19 Position errors for the disturbance of 1.0 Hz/2.0○ on a Stewart platform

图20 扰动1.0 Hz/2.0○，跟踪速度0.5○/s时摇摆台试验积分位置误差图

Fig. 20 Position errors for the disturbance of 1.0 Hz/2.0○ , tracking speed of 0.5○/s on a Stewart platform

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