具有量化输入机械臂系统的预设性能自适应神经网络控制

Predefined Performance Adaptive Neural Network Control of Manipulator System with Quantized Input

针对具有量化输入的单连杆机械臂系统,文中提出了基于Funnel控制的预设性能自适应神经网络控制方法。不同于传统Funnel控制方案,该方法通过构建新型性能函数可保证系统在预定时间达到预设的性能指标,并且使用径向基神经网络和动态面控制技术解决了系统中的未知非线性项与传统反步控制方法中的微分爆炸问题。理论分析证明,所提控制方案不仅消除了量化输入引起的负面影响,而且保证了系统的稳定性。通过调节MTLAB仿真实验中的设计参数,实现了跟踪误差的预定时间收敛,验证了所提控制方法的有效性。

In view of the single-link manipulator system with quantized input,a predefined performance adaptive neural network control method based on Funnel control is proposed in this study.Different from the traditional Funnel control scheme,this method can ensure that the system reaches the predefined performance index within the predetermined time by constructing a new performance function,and the neural network and dynamic surface control technology are used to solve the unknown nonlinear items in the system and the differential explosion problem in the traditional backstepping control method.Theoretical analysis shows that the proposed control scheme not only eliminates the negative effects caused by quantized inputs,but also ensures the stability of the system.By adjusting the design parameters in the MATLAB simulation experiment,the predetermined time convergence of the tracking error is realized,which verifies the effectiveness of the proposed control method.