基于神经网络的机电伺服系统非线性控制  

Nonlinear control of mechatronic servo system based on neural network

作　　者：梁相龙 姚建勇[1] LIANG Xiang-long;YAO Jian-yong(School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学机械工程学院,南京210094

出　　处：《控制与决策》2023年第4期1008-1014,共7页Control and Decision

基　　金：国家重点研发计划项目(2021YFB2011300);国家自然科学基金项目(52075262);江苏省研究生科研与实践创新计划项目(KYCX_220474).

摘　　要：针对机电伺服系统精确动力学模型难以获取以及系统状态信息的测量易受噪声影响的问题,设计一种基于指令滤波与神经网络相结合的非线性反步控制算法,该算法能够有效地补偿未建模动态和外部扰动对机电伺服系统的影响.首先,引入指令滤波器来获取已知信号的微分估计并处理噪声;其次,利用神经网络估计未知的系统动态,包括未建模的摩擦和外部干扰;然后,神经网络权值的更新律通过梯度下降算法在线实现,没有离线学习阶段;最后,利用李雅普诺夫函数分析方法证明闭环系统的稳定性.为验证所提出算法的有效性,在机电伺服实验平台上进行大量对比实验,实验结果表明,所提出的算法具有良好的控制效果,对系统不确定性和外部干扰具有良好的鲁棒性.In order to deal with the problem that the system dynamics of mechatronic servo systems are difficult to be described with precise models,and the measurement of state information is also effected by noise,a nonlinear backstepping control approach based on command filters and neural networks is proposed.The approach can effectively compensate the influence of unmodeled dynamics and external disturbances on the mechatronic servo system.In this approach,a command filter is introduced to acquire the differential estimation of known signals and cope with the noise.And then a neural network is applied to approximate the unknown system dynamics,including the unmodeled friction and the external disturbance.The update law of neural network weights is implemented online by using the gradient descend algorithm,without an off-line learning phase.Finally,the closed-loop system stability is rigorously proven by using the Lyapunov-based method.To verify the effectiveness of the proposed algorithm,extensive comparative experiments are implemented in the mechatronic servo experimental platform.The experimental results show that the proposed controller achieves satisfactory performance and the closed-loop system obtains satisfactory robustness with respect to system uncertainties and external disturbances.

关 键 词：机电伺服系统 未建模动态 指令滤波 神经网络 梯度下降 反步控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]