基于数据驱动高阶学习律的轮式移动机器人轨迹跟踪控制

轮式机器人执行巡逻、播种和工业生产等任务是一个强非线性的间歇过程.针对重复运行的轮式机器人轨迹跟踪问题,本文提出了一种基于数据驱动的高阶迭代学习控制算法.首先,对轮式移动机器人的模型进行推导设计,并对推导得到的状态空间形式的离散时间模型利用基于状态转移的迭代动态线性化方法,将轮式机器人系统转化为线性输入输出数据模型；其次,设计高阶迭代优化目标函数得到控制律,并利用参数更新律估计线性输入输出数据模型中的未知参数.控制器的设计和分析只使用系统的输入输出数据,不包含任何显式的模型信息.通过采用高阶学习控制方法,在控制律中利用更多之前迭代的控制输入信息,提高了控制性能.最后,仿真结果验证了该方法在轮式机器人轨迹跟踪控制中的有效性.

Data-driven high-order learning control for path tracking of wheeled mobile robots

The task of patrolling,seed sowing and industrial production of wheeled robots is a strongly nonlinear intermittent process.In this paper,a data-driven high-order iterative learning control algorithm is proposed for the path tracking of wheeled mobile robots in repeated running scenes.First,the model of wheeled mobile robot is derived and designed,and the discrete-time model in state space is transformed into linear input/output data model by using iterative dynamic linearization method based on state transition.Second,a high-order iterative optimization objective function is designed to obtain the control law,and the parameter update law is used to estimate the unknown parameters in the linear data model.By using high order learning control method,more control input information of previous iteration is used in the control law to improve the control performance.Finally,the simulation results verify the effectiveness of this method in the trajectory tracking control of wheeled robot.