Incremental learning tracking control of complex dynamical trajectories for robotic manipulator

Abstract

The inherent complexity and unpredictability of complex trajectories pose significant challenges for tracking them using a robotic manipulator’s end effector. To address this challenge, a novel incremental learning control scheme is proposed and applied to the tracking of complex dynamic trajectories by the end effector (TCDTEE). Stochastic input torque signals are exerted on the end effector of robotic manipulator so that the modeling data are collected. Then, the T-S fuzzy model (also known as an inverse model) is constructed for TCDTEE via an ensemble fuzzy framework, making it possible to obtain a control signal that can accomplish most control tasks under nominal conditions. However, the controller with fixed structure suffers from the accumulation of tracking errors and may be incapable of addressing some unexpected dynamic changes. To overcome such two problems, an interval type-2 evolving fuzzy neural network (IT2EFNN) is designed to estimate the ideal torque control rates. The IT2EFNN initializes its structure from scratch and optimizes it through the elimination of redundant rules based on the quality assessment of the fuzzy rules, thereby enhancing tracking control accuracy and system responsiveness. Meanwhile, with the help of Lyapunov analysis approach, the stability of the IT2EFNN is guaranteed. Finally, the effectiveness of the proposed method has been validated through simulations and experiments.