Task-Driven-Based Robust Control Design and Fuzzy Optimization for Coordinated Robotic Arm Systems

Uncertainty and Jacobian transformation matrix (JMT) are two critical aspects that affect the coordinated robotic arm systems (CRAS) to achieve high accuracy task space trajectory tracking. To address the above two problems, a robust control design and parameter optimization method is proposed for the task space trajectory tracking of the CRAS in this paper. First, a fuzzy dynamical model of the CRAS is established. In this model, the uncertainty is assumed to be bounded and described by fuzzy set theory. It provides a bridge between the dynamical model and the practical system. Then, based on the fuzzy dynamical model, a robust approximate constraint-following servo control is developed to guarantee uniform boundedness (UB) and uniform ultimate boundedness (UUB) of the controlled CRAS. The proposed control can realize the trajectory tracking in task space without JMT, which alleviates the difficulty of control design and implementation. Third, the optimal parameter of the proposed control is selected by solving a fuzzy-based performance index. This performance index is formulated to merge the system manifestation and the control consumption. Finally, a numerical simulation of a dual-arm system is carried out to show the effectiveness of the proposed control method.