Neural networks for feedback feedforward nonlinear control systems.

Area of Science:

• Control Engineering
• Artificial Intelligence
• Applied Mathematics

Background:

• Designing control strategies for nonlinear dynamic systems to track trajectories and minimize costs is challenging.
• Conventional methods struggle with the generality and complexity of these problems.

Purpose of the Study:

• To develop an approximate solution for feedback feedforward control strategies for nonlinear dynamic systems.
• To utilize multilayer feedforward neural networks for trajectory tracking and cost minimization.

Main Methods:

• Constraining control strategies to a multilayer feedforward neural network structure.
• Applying the "linear-structure preserving principle" for a specific neural architecture.
• Reducing the functional problem to a nonlinear programming problem and using backpropagation.

Main Results:

• Derived recursive equations for gradient computation, generalizing adjoint system equations.
• Demonstrated the effectiveness of the proposed neural network control method through simulations.
• Achieved accurate trajectory tracking and cost function minimization for nonlinear, nonquadratic problems.

Conclusions:

• The proposed neural network-based control strategy offers an effective solution for complex trajectory tracking problems.
• This method overcomes limitations of conventional techniques in nonlinear control system design.
• The approach provides a generalized framework for optimal control using neural networks.