Decentralized Adaptive Neural Inverse Optimal Control of Nonlinear Interconnected Systems

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence in Control

Background:

• Decentralized optimal control for interconnected nonlinear systems traditionally involves solving complex Hamilton-Jacobi-Bellman (HJB) equations.
• These iterative solutions are computationally intensive and time-consuming, posing a significant challenge for practical applications.

Purpose of the Study:

• To develop a novel decentralized adaptive neural inverse approach for continuous-time nonlinear interconnected systems.
• To circumvent the need for solving HJB equations while ensuring optimized performance.
• To establish a new criterion for inverse optimal practical stabilization.

Main Methods:

• A direct adaptive neural strategy is proposed for controller design.
• A modified tuning functions method is employed.
• A new criterion for inverse optimal practical stabilization is introduced and utilized.

Main Results:

• The proposed method successfully avoids the complex and time-consuming solution of HJB equations.
• Demonstrated inverse optimal practical stabilization for the targeted systems.
• Proved that all closed-loop signals remain bounded, ensuring system stability.
• Achieved the objective of inverse optimality concerning the defined cost functional.

Conclusions:

• The decentralized adaptive neural inverse approach offers an efficient alternative to traditional methods for controlling nonlinear interconnected systems.
• The proposed controller design ensures both stability and inverse optimality.
• The method's effectiveness is validated through illustrative examples.