无人驾驶机器人机械腿非线性模糊自适应反演滑模控制

Nonlinear fuzzy adaptive backstepping sliding mode control for mechanical legs on unmanned robot

为了实现无人驾驶机器人机械腿位置精确跟踪及提高车速跟踪精度,针对机械腿受到的非线性干扰,提出了一种基于非线性干扰观测器的机械腿模糊自适应反演滑模控制方法.首先,通过对机械腿机构操纵踏板时的位置运动分析,建立了机械腿运动学模型,构建了考虑运动副非线性摩擦的机械腿动力学模型,描述了机械腿关节间摩擦力矩与相对速度之间的关系,求得了摩擦参数.接着,设计了油门/制动机械腿切换控制器和非线性干扰观测器.最后,针对观测误差以及其他不确定干扰,设计了模糊自适应反演滑模控制器,进行了李雅普诺夫稳定性分析.实验结果表明,所提方法有效削减了控制输出的抖振,且相较于未对摩擦进行补偿的情况,机械腿位置最大跟踪误差从5.5×10^-2 rad减小为1.1×10^-3 rad,最大车速跟踪误差从2.21 km/h减小为1.91 km/h.基于干扰观测器的机械腿模糊自适应反演滑模控制方法能够有效提高机械腿跟踪精度与车速跟踪精度.

To accurately track the position of mechanical legs for unmanned robot and improve the tracking accuracy of the vehicle speed,a fuzzy adaptive backstepping sliding mode control method based on the nonlinear disturbance observer was proposed to deal with the non-linear disturbance to the mechanical legs.First,a kinematics model of mechanical legs was established by analyzing the position and the motion of the mechanical leg mechanism when it operated the pedal.The dynamics model of mechanical legs considering the non-linear friction of the motion pair was constructed.The relation between the friction moment and the relative velocity of the joint of the mechanical legs was described,and the friction parameters were obtained.Then,a throttle/brake mechanical leg switching controller and a non-linear disturbance observer were designed.Finally,a fuzzy adaptive backstepping sliding mode controller was designed for the observation error and other uncertain disturbances,and the Lyapunov stability was analyzed.The experimental results show that the method can effectively reduce the chattering of the control output,and the maximum tracking error of the mechanical leg position is reduced from 5.5×10^-2 rad to 1.1×10^-3 rad,and the maximum speed tracking error is reduced from 2.21 km/h to 1.91 km/h,compared with the case without the friction compensation.The fuzzy adaptive backstepping sliding mode control method based on the disturbance observer can improve the tracking accuracy of the mechanical legs and the tracking accuracy of the vehicle speeds.