Prescribed-Time Adaptive Fuzzy Control for Pneumatic Artificial Muscle-Actuated Parallel Robots With Input Constraints

With the advantages of natural flexibility, large force-weight ratios, and green cleanliness, pneumatic artificial muscle (PAM) actuators that mimic biological skeletal muscles have attracted much attention. However, the inherent defects of PAMs, such as high nonlinearities, limited contraction lengths and frequencies, and multiple input constraints, pose significant challenges to the motion control of PAM-actuated parallel robots; meanwhile, most existing methods do not take into account motion constraints and working efficiency. To this end, a prescribed-time adaptive fuzzy motion control method is developed in this article, where PAM-actuated parallel robots can accurately achieve prescribed tracking performance within an allowable input pressure range. In particular, regardless of the initial values of target trajectories, the expected tracking accuracy is achieved within the prescribed time by restricting the tracking errors to the improved performance constraints; also, the motion velocities remain within the preset dynamic constraints, thereby improving the working safety and efficiency. To the best of authors' knowledge, this article presents the first adaptive fuzzy motion control method for PAM-actuated parallel robots, which can simultaneously achieve motion constraints and prescribed tracking performance. Moreover, the stability of all signals is proved through theoretical analysis, and then the effectiveness of the proposed method is fully verified by a series of hardware experiments.