在线数据记录和扰动观测器相结合的柔性机械臂复合学习控制

Composite learning control of flexible-link manipulator with online recorded data and disturbance observer

本文针对多输入多输出的柔性机械臂动力学模型,提出了一种基于神经网络估计和扰动观测器的复合学习控制策略.首先,通过奇异摄动分析将系统解耦为快慢子系统.然后,针对慢变刚性子系统的动力学模型,基于在线数据记录模型构造新型预测误差,结合跟踪误差设计自适应控制律;针对快变柔性子系统采用滑模控制抑制弹性振动.在此基础上,构建了扰动观测器实时估计复合扰动信号,并纳入在线数据记录模型作为前馈补偿.最后,基于Lyapunov稳定性分析可证系统误差信号一致终值有界,仿真算例验证了所提策略的有效性和优越性.

For the dynamics of multiple input multipe output(MIMO)flexible-link manipulator,this paper investigates a composite learning controller based on the neural networks(NN)and disturbance observer.Firstly,the system is decoupled into the fast and slow subsystems by singular perturbation analysis.Then for the slow-varying dynamics,a novel prediction error is constructed based on the online recorded data scheme.The update law for NN weights is designed by combining the tracking error.A sliding mode controller is constructed to suppress theflexible modes.Furthermore,a disturbance observer is built to estimate the compound disturbance,which is also used as the feedforward compensation of the online recorded data scheme.The boundedness of the system signals is proved via the Lyapunov approach.The simulation test illustrates the effectiveness and superiority of the proposed approach.