基于改进Funnel控制的机械臂轨迹跟踪约束控制

Constraint control of robotic manipulator trajectory tracking based on improved Funnel control

针对关节柔性机械臂系统在未知输入饱和的情况下的稳态和瞬态性能约束问题,提出一种改进Funnel控制结合RBF自适应神经网络的轨迹跟踪控制方法。设计了改进的Funnel控制变量以避免控制律中的微分不可导的情况;构造了时变的瞬态性能约束函数,调节系统输出初始阶段的超调量和收敛速度;采用最小参数学习法的RBF自适应神经网络,逼近机械臂系统模型的未知函数和虚拟控制律的导数,简化了控制器的设计;通过李雅普诺夫稳定性定理,证明了闭环系统中所有变量是半全局一致最终有界的。仿真实验验证了所提方法有效性。

A trajectory tracking control method was proposed based on improved Funnel control and with RBF adaptive neural network to address the steady-state and transient performance constraints of a flexible joint robotic manipulator system with unknown input saturation.The non-differentiable in the control law was avoided by designing improved Funnel control variables.The construction of time-varying transient performance constraint function made it possible to qualitatively design the overshoot amount and convergence speed in the initial stage of the system output;The problem of excessive computational burden in the backstepping step method and the unknown function of the flexible-joint robotic manipulator system were approximated by the RBF adaptive neural network with minimum parameter learning method,which simplified the design of the controller;The Lyapunov stability theory justified that all variables in the closed-loop system are semi-global uniformly ultimately bounded(SGUUB),and simulation experiments verified the effectiveness of the proposed method.