带有输出约束机械臂系统的固定时间控制

Fixed-Time Control for Robotic Manipulator Systems with an Output Constraint

针对具有未知非线性特性和输出约束机械臂的轨迹跟踪控制问题,提出了一种基于径向基神经网络(radial basis function neural network,RBFNN)的事件触发式固定时间控制算法。首先,将障碍Lyapunov函数引入到了反步控制框架中,并利用RBFNN处理系统中的未知非线性特性;其次,为了缓解系统通信压力,设计了事件触发机制以减小控制信号的更新频率。在上述基础上,基于固定时间稳定理论,构建了事件触发式固定时间稳定控制器。从理论上证明了系统可以在一个与初始系统状态无关的时间内实现稳定,且大幅地节约了系统的通信资源;最后,仿真实验验证了方法的可行性。

An event-triggered fixed-time control algorithm based on radial basis neural network(RBFNN)is proposed for the trajectory tracking control problem of a robotic manipulator with unknown nonlinear characteristics and output constraints.Firstly,a barrier Lyapunov function is introduced into the backstepping control framework and the RBFNN is used to deal with the unknown nonlinear characteristics of the system;secondly,to relieve the system communication pressure,an event-triggered mechanism is designed to reduce the update frequency of the control signals.Based on the above,an event-triggered fixed-time stable controller is constructed based on the fixed-time stability theory.It is theoretically demonstrated that the system can be stabilized in a time independent of initial system states,and the communication resources of the system are substantially saved.Finally,simulation experiments verify the feasibility of the method.