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Exponential stabilization for sampled-data neural-network-based control systems.

Zheng-Guang Wu, Peng Shi, Hongye Su

IEEE Transactions on Neural Networks and Learning Systems

|November 25, 2014

Summary

This summary is machine-generated.

This study presents new methods for stabilizing neural network control systems using sampled-data control, maximizing sampling intervals and minimizing costs. The research ensures exponential stability for neural network-based control systems with optimal guaranteed cost.

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Area of Science:

Control Systems Engineering
Artificial Intelligence
Applied Mathematics

Background:

Neural network-based control systems are increasingly complex.
Sampled-data control introduces challenges in stability analysis.
Optimizing control performance with guaranteed cost is crucial.

Purpose of the Study:

To develop sampled-data stabilization techniques for neural-network-based control systems.
To achieve optimal guaranteed cost control.
To maximize the allowable sampling interval.

Main Methods:

Utilizing a time-dependent Lyapunov functional approach.
Proposing novel stability conditions that leverage actual sampling patterns.
Designing sampled-data three-layer fully connected feedforward neural-network-based controllers.

Main Results:

Novel conditions for guaranteeing exponential stability of closed-loop systems.
Design methods for controllers that maximize sampling intervals.
Achieving the smallest upper bound for the cost function.
Demonstrated effectiveness and feasibility through a practical example.

Conclusions:

The proposed methods effectively stabilize neural-network-based control systems under sampled-data operation.
The techniques allow for larger sampling intervals while ensuring optimal guaranteed cost.
The approach is validated by a practical example, showing its real-world applicability.