基于RBF神经网络的谐波驱动机构动态面控制

Dynamic Surface Control of Harmonic Drive Mechanism based on RBF Neural Network

针对具有多变量和未知非线性的谐波驱动机构,建立其系统数学模型,定义系统误差,并逐步构造虚拟函数,利用RBF神经网络在线逼近谐波驱动机构中的非线性环节,最后建立系统自适应控制规律,设计基于RBF神经网络的动态面控制器。结合Lyapunov稳定性分析方法论证闭环系统的收敛性,通过理论分析并与常规PID的仿真结果进行比较。结果表明,所提出的神经网络动态面控制器实现了对谐波驱动机构的高性能跟踪控制,且具有很好的控制精度,有效地抑制未知非线性外界扰动对系统的影响。

The mathematic model for the harmonic drive mechanism with multi- variable and nonlinear external disturbance is established,the system error is defined and virtual function is introduced gradually. The RBF neural network is employed to approximate the nonlinear part of the harmonic drive mechanism on- line.The dynamic surface controller is developed based on RBF neural network. The closed- loop system convergence is demonstrated with the Lyapunov stabilization theory,through the theory analysis and simulation results comparing with general PID control show that the proposed controller not only satisfies high performance position tracking,but also have good accuracy and stability,which effectively suppresses the influence of the harmonic drive mechanism like nonlinear external disturbance.