Learning-Based Prescribed-Time Fuzzy Optimal Quantized Control for Large-Scale Systems With Bridge-Hole Constraint

This study presents an advanced adaptive fuzzy optimal bridge-hole constraint control method for large-scale interconnected systems under quantized input. To address the conflict in constraint ranges caused by the combined effect of both results in the bridge-hole and performance constraints, a new prescribed time function with parameter requirements is proposed, which bridges the balance between them and keeps the tracking error within a desired zone in a prescribed time. Meanwhile, output constraint is realized by building a new bridge-hole constraint function, which ensures the time interval for the constraint behavior to occur by the flexible setting of the switching time. Unlike traditional optimal control schemes, the designed optimal controller is further quantized by a hysteresis quantizer, which minimizes energy cost and saves bandwidth. Besides, a reinforcement learning (RL) scheme based on an actor–critic-identifier fuzzy logic system (FLS) structure is designed; its overall control idea is to optimize the entire backstepping control system by using all virtual and actual backstepping control as the optimal solution of their respective subsystems. Finally, the effectiveness of the proposed scheme is confirmed by simulation experiments.