摘要
针对一类非完整移动机器人的轨迹跟踪控制系统,提出一种基于RBF神经网络的滑模控制与转矩控制相结合的智能控制方法。该方法同时考虑机器人运动学和动力学模型,通过RBF神经网络进行移动机器人运动过程学习,与速度误差结合构成力矩控制器,可保证闭环误差系统一致最终渐进稳定。采用基于李亚普诺夫(Lyapunov)稳定性理论的判稳方法,证明整个闭环控制系统的稳定性。仿真结果表明,该控制方案具有较强的鲁棒性。
An intelligent control strategy for trajectory tracking control system ofnonholonomic mobile robot is presented, which is the combination of sliding-mode control and torque control based on RBF neural networked control. Considering both kinematic and dyna- mical model, the RBF neural networks learn the process of mobile robot motion, and constitutes a torque controller combined with the speed error. The uniformly ultimately asymptotic stability of the closed loop error system can be obtained. The stability of entire closed loop system is proved by Lyapunov stability theory. The simulation results demonstrate that this control strategy has good robustness.
出处
《计算机工程与设计》
CSCD
北大核心
2011年第5期1804-1806,1832,共4页
Computer Engineering and Design
基金
金陵科技学院校级自然科学基金项目(JIT-N-2007019)
关键词
非完整系统
轮式移动机器人
轨迹跟踪
滑模控制
RBF神经网络控制
nonholonomic system
wheeled mobile robot
trajectory tracking
sliding-mode control
RBF neural networked control
