摘要
采用Lagrange方法建立了一类新型3P6R平面3-DOF串-并混联拟人臂的标称动力学模型。针对该机构的重复轨迹跟踪问题,考虑其不确定性,充分利用其已知动力学部分,提出了一种集中参数自适应-闭环迭代学习控制器。在每个迭代周期内采用自适应算法学习由未建模动态、外部干扰及摩擦力等多种因素造成的集中不确定性上界,进而逐次补偿由其造成的误差;闭环变系数迭代学习算法保证了该系统在迭代域内收敛,实现了完全轨迹跟踪。严格的证明及仿真结果验证了此控制器的有效性。
The normal dynamics model of a novel 3P6R planar 3-DOF serial-parallel Hybrid Humanoid Arm （HHA） was established by Lagrange method. Considering the uncertainties and the normal dynamics model of this system, a centralized parameter Adaptive-closed loop Iterative Learning Controller （ILC） used for the trajectory repeated tracking was proposed. The upper bound oft he centralized parameter uncertainty, which consists of unmodeled dynamics, disturbance and friction, was learned by adaptive strategy in each iteration. The closed loop variable coefficient ILC guarantees the convergence of system in the iterative region, and the utterly trajectory tracking was realized. The strict proof and simulation results validate the effectivity of the controller.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2010年第1期148-151,156,共5页
Journal of System Simulation
基金
Scientific Research Platform of Yanshan University and Foundation of Doctor(B51)
关键词
混联拟人臂
动力学建模
迭代学习控制
自适应
hybrid humanoid ann
dynamics modeling
iterative learning control
adaptive
