Improved iterative learning control method for mobile robot trajectory tracking
一种改进的移动机器人轨迹跟踪迭代学习方法

Through studying trajectory tracking optimization problems of the wheeled mobile robot, this paper proposed a iterative learning control approach based on Kalman filter with strong adaptability, fast convergence and small error. In order to bring the advantages of Kalman filtering algorithm and the iterative learning control algorithm into full play, it used the introduction of state compensation term and designed new iterative learning gain matrix to improve the law of iterative learning control. An improved iterative learning control could track the desired circular trajectory more quickly, more accuratly and more effectively. It used a discrete Kalman filter to filter rejection and noise, and restrained the influence of interference and noise on trajectory tracking. It made this algorithm more suitable for engineering application. Experiments and computer simulations show that the method has good tracking ability.