2025/03/18 by Hamed Rahimi Nohooji, Nohooji, Hamed Rahimi, Abolfazl Zaraki +3
Computer Science · Engineering · Neuroscience · #EEG and Brain-Computer Interfaces #Elevator Systems and Control #FOS: Computer and information sciences #FOS: Electrical engineering #Neural Networks and Applications #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2503.14669
openalex publication_date 2025/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents a reinforcement learning-based neuroadaptive control framework for robotic manipulators operating under deferred constraints. The proposed approach improves traditional barrier Lyapunov functions by introducing a smooth constraint enforcement mechanism that offers two key advantages: (i) it minimizes control effort in unconstrained regions and progressively increases it near constraints, improving energy efficiency, and (ii) it enables gradual constraint activation through a prescribed-time shifting function, allowing safe operation even when initial conditions violate constraints. To address system uncertainties and improve adaptability, an actor-critic reinforcement learning framework is employed. The critic network estimates the value function, while the actor network learns an optimal control policy in real time, enabling adaptive constraint handling without requiring explicit system modeling. Lyapunov-based stability analysis guarantees the boundedness of all closed-loop signals. The effectiveness of the proposed method is validated through numerical simulations.