State-constrained bipartite tracking of interconnected robotic systems via hierarchical prescribed-performance control

Abstract

This paper investigates the collaborative design problem aiming to achieve state-constrained bipartite tracking of the interconnected robotic systems (IRSs) with prescribed performance. In practical applications, the physical limitations of the robots are inevitable. Besides, it is difficult to ensure that the target trajectory is known for each robot of the IRSs in advance. Thus, it is important to follow the target trajectory and meanwhile obey the state constraint being generated from the physical limitations and the external environment of the IRSs. To this end, we propose a new hierarchical state-constrained estimator-based control framework with the characteristics of low computation complexity and high task adaptability. With limited accessibility of the target trajectory, we newly present the estimator to observe it at each time interval through the interconnections among the robots. The state constraint is never violated throughout the convergence process by using the presented control algorithm. The theoretical proof and simulation results are presented to validate the feasibility of the control framework.