Intrinsic Plasticity-Driven Neuroadaptive Asymptotic Tracking Control for a Class of Uncertain Robotic Manipulators

Area of Science:

• Robotics
• Control Systems Engineering
• Computational Neuroscience

Background:

• Uncertainty and complex dynamics in multi-input multi-output (MIMO) robotic manipulators pose significant control challenges.
• Existing control methods often struggle with achieving high precision and efficiency in dynamic environments.

Purpose of the Study:

• To develop a novel neuroadaptive asymptotic tracking control method for uncertain MIMO robotic manipulators.
• To enhance the adaptability and computational efficiency of control systems for robotic applications.

Main Methods:

• Construction of an intrinsic plasticity (IP)-driven cycle echo state network (ESN) to approximate system nonlinearities.
• Integration of the IP-driven cycle ESN with the robust integral of the sign of the error (RISE) framework for adaptive control.
• Rigorous stability analysis to guarantee asymptotic convergence of tracking errors.

Main Results:

• The proposed neuroadaptive controller demonstrated faster error convergence rates compared to RBFNN-based PI control.
• Achieved higher tracking precision and smoother control inputs in simulations.
• Effectively combined the approximation capabilities of the IP-driven cycle ESN with the robust compensation of the RISE framework.

Conclusions:

• The novel neuroadaptive control method offers superior performance for uncertain nonlinear robotic systems.
• The integration of IP-driven cycle ESN and RISE framework provides a robust and efficient solution for high-precision robotic control.
• This approach advances the field of adaptive control for complex robotic manipulators.