CAJ | 학술논문

针对不确定机器人系统轨迹跟踪问题,并更好地消除系统不确定性对控制性能的影响,提出一种基于低通滤波器的迭代学习控制方法.采用滑模变结构控制(SMC)以提高控制器对系统干扰和摄动的鲁棒性,并在控制器输出端引入低通滤波器(LPF)来消除滑模控制中出现的抖振现象.将系统的不确定项描述为周期性和非周期性两部分,通过采用迭代学习算法对周期性不确定部分进行迭代学习,采用RBF神经网络对非周期性不确定部分的未知上界进行自适应学习.该控制方法不仅对系统的不确定性和有界外部扰动具有鲁棒性,而且使得整个系统在迭代域中是全局渐近稳定的.严格的理论推导和仿真结果表明了该控制策略的有效性.
침대불학정궤기인계통궤적근종문제,병경호지소제계통불학정성대공제성능적영향,제출일충기우저통려파기적질대학습공제방법.채용활모변결구공제(SMC)이제고공제기대계통간우화섭동적로봉성,병재공제기수출단인입저통려파기(LPF)래소제활모공제중출현적두진현상.장계통적불학정항묘술위주기성화비주기성량부분,통과채용질대학습산법대주기성불학정부분진행질대학습,채용RBF신경망락대비주기성불학정부분적미지상계진행자괄응학습.해공제방법불부대계통적불학정성화유계외부우동구유로봉성,이차사득정개계통재질대역중시전국점근은정적.엄격적이론추도화방진결과표명료해공제책략적유효성.
An iterative learning control algorithm based on low pass filter(LPF) is proposed for trajectory tracking of uncertain robot system.Sliding mode variable structure control is used to improve the robustness to disturbance and perturbation,and LPF is used to eliminate the chattering of sliding mode control.The uncertain part is described as periodical and non-periodic parts,herative learning control algorithm is used to learn the periodical uncertainty,and RBF neural network is used to learn the unknown upper bound of non-periodic uncertainty adaptively.The control algorithm not only guarantees the robustness to system uncertainties and bounded external disturbances,but also guarantees that the system is asymptotical stable in iterative domain.The simulation results show that the expected control purpose can be achieved using the proposed algorithm.