Adaptive control and state error prediction of flexible manipulators using radial basis function neural network and...

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Flexible joint manipulators present significant control challenges due to inherent uncertainties and external disturbances.
• Existing control methods often struggle to achieve high precision and adaptability in dynamic environments.

Purpose of the Study:

• To develop a novel robust control strategy for flexible joint manipulators.
• To enhance tracking accuracy and system adaptability under uncertain dynamics and disturbances.

Main Methods:

• Integration of Radial Basis Function Neural Network (RBFNN) for approximating system dynamics.
• Application of Adaptive Dynamic Surface Control (ADSC) with a nonlinear damping term.
• Development of an adaptive law for real-time parameter and weight updates.
• Utilization of Lyapunov stability analysis to ensure system boundedness.
• Incorporation of Long Short-Term Memory (LSTM) networks for predictive state analysis.

Main Results:

• The proposed RBFNN-ADSC strategy effectively approximates uncertain dynamics and mitigates external disturbances.
• Real-time adaptation of control parameters ensures robustness against dynamic changes.
• Lyapunov stability analysis guarantees semi-globally uniformly bounded signals, minimizing tracking errors.
• LSTM-based predictive analysis validates the control method's effectiveness and robustness through simulations.

Conclusions:

• The novel control strategy demonstrates superior performance in managing uncertainties in flexible joint manipulators.
• The integration of RBFNN, ADSC, and LSTM offers a robust and adaptive solution for complex robotic systems.
• Extensive simulations confirm the practical applicability and effectiveness of the proposed approach.