Deep Neural Network Enhanced Adaptive Control for a Robotic Manipulator with Actuator Failures
DOI:
https://doi.org/10.5281/zenodo.14633657Keywords:
robotic manipulator, actuator failures, adaptive control, deep neural networks, fault tolerance, robust control, trajectory tracking, lyapunov stabilityAbstract
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.
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Copyright (c) 2024 Kannika Chaiwong, Lukas Pichler
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