A Cascaded Broad Learning System for Manipulator Motion Control

Abstract

Intelligent control methods have led to a significant simplification of the robotic arm modeling and control tuning process, and thus they have been widely used. To further improve the precision of robotic arm motion control, this paper proposes a robotic arm motion control strategy based on a cascaded feature-enhanced elastic-net broad learning system (CFE-EN-BLS). This will fully extract data features to improve motion control accuracy. Moreover, ElasticNet regression is introduced to reduce feature redundancy. Finally, Lyapunov stability theory is introduced to constrain the learning parameters of the proposed learning method to enhance the convergence of the control strategy. The simulation and experiment show that the proposed control strategy can realize high-precision trajectory tracking control of the robotic arm.