An adaptive tracking controller using neural networks for a class of nonlinear systems

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Nonlinear systems present significant challenges in control due to inherent uncertainties and complex dynamics.
• Adaptive control strategies are crucial for achieving precise tracking performance in the presence of unknown system parameters.

Purpose of the Study:

• To develop a novel adaptive tracking control scheme for nonlinear systems.
• To leverage neural networks for online estimation of system uncertainty bounds.
• To ensure robust control and asymptotic convergence of tracking errors.

Main Methods:

• Utilized radial basis function (RBF) neural networks for adaptive learning of system uncertainty bounds.
• Employed Lyapunov stability analysis to guarantee controller performance.
• Integrated neural network outputs as controller parameters to compensate for system uncertainties.

Main Results:

• Demonstrated strong robustness against uncertain dynamics and nonlinearities.
• Achieved asymptotic convergence of the output tracking error to zero.
• Validated the proposed neural control scheme through simulation examples.

Conclusions:

• The proposed neural-network-based adaptive control scheme effectively handles uncertainties in nonlinear systems.
• The RBF neural networks provide a robust mechanism for online adaptation and compensation.
• The control strategy ensures precise tracking performance and stability.