Intelligent Actor-Critic Learning Control for Collison-Free Trajectory Tracking of Mecanum-Wheeled Mobile Robots

In the paper, an intelligent actor-critic learning control method augmented by a fuzzy broad learning system with output recurrent feedback (abbreviated as ORFBLS) is proposed for obstacle-avoiding trajectory tracking of heterogeneous Mecanum-wheeled omnidirectional mobile robots (HMOMRs) augmented with un-modeled errors and the learning control system’s parameters are changed with the working environment. By using actor-critic learning algorithms and the ORFBLS online function approximation, a new ORFBLS-based actor-critic learning control approach, dubbed as ORFBLS-ACLC, is presented to accomplish stable trajectory tracking of such uncertain HMOMRs. The ORFBLS-ACLC method combined with an obstacle avoidance scheme is presented to follow the desired collision-free trajectory tracking more closely than the existing methods do. The merits, superiority, and practicality of the raised control approach are well verified by carrying out three simulation comparisons and two experiments on an uncertain HMOMR.