Nonlinear systems identification and control via dynamic multitime scales neural networks

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Nonlinear systems present significant challenges in accurate modeling and control.
• Adaptive control strategies are crucial for systems with time-varying or uncertain dynamics.
• Neural networks offer powerful tools for approximating complex nonlinear functions.

Purpose of the Study:

• To develop adaptive nonlinear identification and trajectory tracking methods using dynamic multilayer neural networks (NNs).
• To propose two NN identifiers capable of handling systems with different timescales (fast and slow phenomena).
• To design indirect adaptive NN controllers for nonlinear systems with complex dynamic processes.

Main Methods:

• Two dynamic neural network (NN) identifiers were proposed for nonlinear system identification.
• The first NN identifier used actual system output signals; the second used NN state variables.
• Lyapunov functions and singularly perturbed techniques were employed for online identification algorithm development.
• Indirect adaptive NN controllers were developed based on the identified NN models.

Main Results:

• Online identification algorithms were successfully developed for both NN identifier parameters.
• The stability of the developed adaptive NN controllers was rigorously proved.
• Simulation results indicated that the controller based on the second NN identifier (using state variables) outperformed the first.
• The proposed methods effectively handled nonlinear systems with both slow and fast dynamic processes.

Conclusions:

• Dynamic multilayer neural networks with different timescales are effective for adaptive nonlinear identification and trajectory tracking.
• Utilizing NN state variables in the identifier leads to improved control performance compared to using system output signals.
• The proposed Lyapunov-based and singularly perturbed techniques ensure system stability and accurate online parameter adaptation.