Unified Design Method for Suboptimal Control of Nonlinear System With Multiple Constraints

Area of Science:

• Control Theory
• Nonlinear Systems
• Artificial Intelligence

Background:

• Designing controllers for nonlinear systems with multiple constraints (state, input, output) is challenging.
• Existing methods often struggle with unknown system dynamics and unmodeled uncertainties.
• Handling inequality constraints requires specialized techniques.

Purpose of the Study:

• To propose a unified suboptimal controller design method for unknown general nonlinear systems.
• To effectively manage state, input, and output constraints simultaneously.
• To develop a data-driven approach using neural networks and integral reinforcement learning.

Main Methods:

• Transforming inequality constraints into equality constraints using slack functions and Pade approximation.
• Defining an unconstrained augmented system encompassing original system dynamics and constraints.
• Utilizing neural networks (NNs) and integral reinforcement learning (IRL) for data-based control design.

Main Results:

• The optimal controller for the augmented system acts as a suboptimal controller for the original system.
• The proposed method effectively handles unknown dynamics and multiple constraints.
• Simulation results demonstrate the efficacy of the unified design approach.

Conclusions:

• The developed method offers a unified framework for suboptimal controller design in constrained nonlinear systems.
• Neural networks and IRL provide a robust, data-based solution for complex control problems.
• The approach is effective for systems with unmodeled dynamics and various constraint types.