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Neural network adaptive control for nonlinear nonnegative dynamical systems.

Tomohisa Hayakawa¹, Wassim M Haddad, Naira Hovakimyan

¹Japan Science and Technology Agency, Saitama 332-0012, Japan. tomohisa_hayakawa@ipc.i.u-tokyo.ac.jp

IEEE Transactions on Neural Networks

|March 25, 2005

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This study introduces a neural adaptive control framework for nonlinear uncertain nonnegative and compartmental systems. The Lyapunov-based method ensures system states and controller gains remain bounded and nonnegative.

Area of Science:

Control Theory
Dynamical Systems
Biomedical Engineering

Background:

Nonnegative and compartmental dynamical systems are crucial in engineering and life sciences, modeling mass/energy balance with nonnegative states.
These systems often assume kinetic homogeneity within compartments and involve nonnegative quantity exchange between subsystems.

Purpose of the Study:

To develop a full-state feedback neural adaptive control framework.
To achieve adaptive set-point regulation for nonlinear uncertain nonnegative and compartmental systems.

Main Methods:

Utilizing a Lyapunov-based framework for stability analysis.
Implementing a neural adaptive controller for system regulation.
Ensuring nonnegative system states and bounded neural network weights.

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Main Results:

The proposed framework guarantees ultimate boundedness of error signals for physical system states.
Neural network weighting gains are also shown to be ultimately bounded.
The controller ensures physical system states remain within the nonnegative orthant for nonnegative initial conditions.

Conclusions:

The developed neural adaptive control framework effectively regulates nonlinear uncertain nonnegative and compartmental systems.
The approach guarantees system stability and state nonnegativity, crucial for real-world applications.
This work advances control strategies for systems with inherent nonnegativity constraints.