一种改进的移动机器人轨迹跟踪迭代学习方法

通过对轮式移动机器人轨迹跟踪优化问题的研究,提出了一种适应性强、收敛速度快且跟踪误差小的迭代滤波学习控制方法,充分发挥了迭代学习控制和Kalman滤波算法的优势,通过引入状态补偿项和设计新的迭代学习增益矩阵对迭代学习律进行了改进。改进的迭代学习控制能够更快速、更精确、更有效地跟踪期望的圆轨迹。采用离散的Kalman滤波器对干扰和噪声进行滤波,抑制了干扰和噪声对轨迹跟踪的影响,使该控制算法更适合于工程应用。计算机实验和仿真表明该方法具有较好的轨迹跟踪能力。

Improved iterative learning control method for mobile robot trajectory tracking

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.