This paper develops a Closed-Loop Error Learning Control (CLELC) algorithm for feedback linearizable systems with experimental validation on a mobile robot. Traditional feedback and feedforward controllers are designed based on the nominal model by using Feedback Linearization Control (FLC) method. Then, an intelligent controller is designed based on sliding mode learning algorithm that utilizes closed-loop error dynamics to learn the system behavior. The controllers are working in parallel, and the intelligent controller can gradually replace the feedback controller from the control of the system. In addition to the stability of the sliding mode learning algorithm, the closed-loop stability of an $n$th order feedback linearizable system is proven. The simulation results demonstrate that CLELC algorithm can improve control performance (e.g., smaller rise time, settling time and overshoot) in the absence of uncertainties, and also provides robust control performance in the presence of uncertainties as compared to traditional FLC method. To test the efficiency and efficacy of CLELC algorithm, the trajectory tracking problem of a tracked mobile robot is studied in real-time. The experimental results demonstrate that CLELC algorithm ensures high-accurate trajectory tracking performance than traditional FLC method.
翻译:本文开发了以移动机器人实验验证的可线性反馈系统闭路错误学习控制(CLELC)算法。传统的反馈和饲料向前控制器是使用反馈线性控制(FLC)方法以名义模型为基础设计的。然后,智能控制器是根据滑动模式学习算法设计的,该算法使用闭路错误动态来学习系统行为。控制器同时工作,智能控制器可以逐步取代系统控制下的反馈控制器。除了滑动模式学习算法的稳定性外,还验证了美元顺序回馈可线性系统的闭路稳定。模拟结果显示,在没有不确定性的情况下,CLELC算法可以改进控制性(例如,较小升降时间、沉积时间和超时),并在与传统的FLC法方法相比存在不确定性的情况下提供强有力的控制性能。为了测试CLC算法的效率和效力,正在实时研究跟踪的移动机器人轨迹跟踪问题。实验结果显示,CLELC算法可以确保高于传统的LC法的高度精确轨迹性跟踪性。