Adaptive inverse control of linear and nonlinear systems using dynamic neural networks

Area of Science:

• Control Engineering
• Signal Processing
• System Identification

Background:

• Traditional control systems often rely on direct feedback, which can be limiting for complex dynamical systems.
• Adaptive control offers a more flexible approach but can be challenging to implement effectively.

Purpose of the Study:

• To propose a novel three-part adaptive filtering framework for controlling dynamical systems.
• To demonstrate the efficacy of this approach for a wide range of system types and configurations.

Main Methods:

• Modeling the dynamical system using adaptive system identification techniques.
• Implementing an adaptive feedforward controller without direct feedback, using system output for parameter adjustment.
• Employing an additional adaptive filter for disturbance cancellation to minimize output disturbance power.

Main Results:

• The proposed adaptive control techniques successfully manage minimum-phase or nonminimum-phase, linear or nonlinear, and SISO or MIMO systems.
• The disturbance canceler effectively minimizes output disturbance power without altering system dynamics.
• Simulation examples confirm the high performance of the proposed methods.

Conclusions:

• The three-part adaptive filtering approach provides a robust and versatile solution for adaptive control.
• This framework is applicable to a broad spectrum of dynamical systems, including those with challenging characteristics.
• The method allows for practical implementation with optional constraints on control effort.