Deep Neural Network Enhanced Adaptive Control for a Robotic Manipulator with Actuator Failures

This paper presents a novel control strategy for a robotic manipulator subject to actuator failures. The proposed approach integrates a deep neural network (DNN) with an adaptive control framework to enhance robustness and fault tolerance. The manipulator dynamics are modeled using a rigid-body model, incorporating potential actuator malfunctions such as partial loss of effectiveness and complete failures. A robust adaptive controller is designed to stabilize the manipulator's motion despite these uncertainties. A DNN is then employed to estimate and compensate for the effects of actuator failures in real-time. Lyapunov stability analysis is conducted to guarantee the stability and convergence of the closed-loop system. Simulation results demonstrate the effectiveness of the proposed approach in achieving accurate trajectory tracking and maintaining stability in the presence of various actuator failures.