基于迭代学习的机器人自适应滑模控制

Adaptive Sliding Mode Control of Robot Based on Iterative Learning

针对非线性强耦合、参数复杂且不确定以及干扰未知的机器人轨迹重复跟踪控制问题,提出一种基于迭代学习技术的自适应鲁棒控制策略。在迭代学习的基础上,引入自适应滑模控制器,以提高系统的鲁棒性;接着,对控制器进行了稳定性分析;最后,分别基于两种工况对该控制器的特性以及对随机干扰的抗干扰性能进行了仿真实验,结果表明基于迭代学习的自适应滑模控制相比于比例微分型迭代学习鲁棒性更强,轨迹精度更高且平滑。

Aiming at repetitive tracking control of robot trajectory with complex and uncertain parameters,strong nonlinear coupling and unknown disturbance,an adaptive robust control strategy based on iterative learning is proposed.Onthe basis of iterative learning,an adaptive sliding mode controller is introducedto improve the robustness of the system.Then,the stability of the controller is analyzed.Finally,the characteristics of the controller and the anti-interference performance against random disturbances are simulated based on two operating conditions.The results show that the adaptive sliding mode control based on iterative learning has stronger robustness,higher trajectory accuracy and smoothness than PD-type iterative learning.