Recurrent-Neural-Network-Based Multivariable Adaptive Control for a Class of Nonlinear Dynamic Systems With...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Traditional robust control for Nonlinear Autoregressive Moving Average (NARMA) models faces challenges with time-varying delays, bounded uncertainty, and pre-learned models.
• Recurrent Neural Networks (RNNs) are used for online learning of NARMA models, but performance degrades with significant system variations.

Purpose of the Study:

• To develop an advanced RNN-based adaptive control strategy for multivariable nonlinear dynamic systems with time-varying delays.
• To address the limitations of existing robust control methods for NARMA models.

Main Methods:

• An approximate NARMA model is used for system representation.
• A recurrent neural network (RNN) is employed for online system identification.
• A compensating network addresses RNN approximation errors.
• An epsilon-modification learning law with weight matrix projection ensures boundedness.

Main Results:

• The proposed RNN-based adaptive control achieves semiglobally ultimately bounded tracking.
• The boundedness of the estimated weight matrix is guaranteed.
• Simulations demonstrate the effectiveness and robustness of the control strategy.

Conclusions:

• The novel RNN-based adaptive control effectively manages multivariable nonlinear systems with time-varying delays.
• The approach provides a robust solution outperforming traditional methods.
• The method ensures stable system performance and parameter boundedness.