移动机器人自适应模糊神经滑模控制  

Adaptive Fuzzy-neural Sliding Mode Control of Mobile Robot

作　　者：朱玲 李艳东[2] 郭媛[2] ZHU Ling;LI Yandong;GUO Yuan(School of Mechanical and Electronic Engineering,Qiqihar University,Qiqihar Heilongjiang 161006,China;College of Computer and Control Engineering,Qiqihar University,Qiqihar Heilongjiang 161006)

机构地区：[1]齐齐哈尔大学机电工程学院,黑龙江齐齐哈尔161006 [2]齐齐哈尔大学计算机与控制工程学院,黑龙江齐齐哈尔161006

出　　处：《微电机》2020年第1期59-64,共6页Micromotors

基　　金：国家自然科学基金面上项目(61872204);黑龙江省自然科学基金(F2015025);黑龙江省教育厅基本科研业务费青年创新人才项目(135309373)

摘　　要：针对含有驱动器动力学的非完整移动机器人轨迹跟踪控制问题,以驱动器电压作为控制输入,提出了一种新型非完整移动机器人自适应模糊神经滑模控制算法,采用自适应动态递归模糊神经网络在线估计由于参数不确定和外加干扰而引起的未知时变函数,减小了不确定性的估计误差;结合自适应鲁棒控制器,不但克服了移动机器人所受的参数与非参数的多不确定性问题,同时也确保了机器人对指定轨迹的跟踪;基于Lyapunov方法进行了参数自适应律设计和系统稳定性分析,保证了控制系统跟踪误差的收敛性,仿真结果表明了该方法的有效性。For trajectory tracking control of nonholonomic mobile robots including actuator dynamics,the actuator voltage is employed as the control input,a adaptive fuzzy-neural sliding mode control method is proposed.The adaptive dynamic recurrent fuzzy neural network(ADRFNN)is used to achieve on-line estimation for the unknown time-varying functions arising from parameter uncertainties and external disturbances,which reduces estimation errors of uncertainties.Combined with the adaptive robust controller,this method not only solved the problem of parameters and non-parameter uncertainties of mobile robot,but also ensured the desired trajectory tracking of mobile robot.Design of parameter adaptive law,stability and convergence of the control system were proved by using the Lyapunov theory.The simulation results showed the effectiveness of the proposed method.

关 键 词：非完整移动机器人 滑模控制 不确定性 驱动器动力学 自适应递归模糊神经网络 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]