Finite-Time Convergence Adaptive Neural Network Control for Nonlinear Servo Systems

Area of Science:

• Control Engineering
• Nonlinear Systems
• Adaptive Control

Background:

• Classical adaptive laws using gradient descent with modifications like σ-modification or e-modification often fail to guarantee parameter estimation convergence in nonlinear systems.
• This lack of convergence can result in slow system responses and complicated parameter tuning, hindering the effectiveness of adaptive control.
• Function approximators like neural networks (NNs) and fuzzy logic systems are common in adaptive control but face challenges with traditional adaptive laws.

Purpose of the Study:

• To propose a new learning strategy for adaptive control of nonlinear servo systems that overcomes the convergence limitations of existing methods.
• To design alternative adaptive laws driven by estimation error, ensuring simultaneous convergence of parameters and tracking error.
• To enhance robustness and achieve finite-time convergence for adaptive control of systems with unknown nonlinearities.

Main Methods:

• A new learning strategy is proposed, utilizing the parameter estimation error as a novel leakage term within adaptive laws.
• An augmented neural network (NN) incorporating a new friction model is employed to address unknown nonlinearities.
• Online estimation of NN weights and friction model coefficients is performed using the proposed adaptive algorithms.

Main Results:

• The proposed learning method achieves simultaneous convergence of estimated parameters and tracking error.
• The adaptive algorithm is tailored for finite-time convergence, improving response speed.
• Simulations and experiments demonstrate superior performance and robustness compared to the σ-modification algorithm.

Conclusions:

• The novel estimation error-driven adaptive laws provide a robust and efficient solution for adaptive control of nonlinear servo systems.
• The proposed method enhances convergence properties and offers superior performance over traditional gradient-descent-based adaptive laws.
• The integration of an augmented NN with online friction modeling further improves the handling of complex nonlinear dynamics.