Quantum mechanics

Adaptive control

Control (management)

Engineering

Backstepping

Biology

Control engineering

Computer science

Bounded function

Controller (irrigation)

Physics

Nonlinear system

State observer

Mathematics

Agronomy

Mathematical analysis

Artificial intelligence

Observer (physics)

Decentralised system

Control theory (sociology)

Artificial neural network

IEEE Transactions on Neural Networks and Learning Systems

This paper addresses the problem of adaptive neural output-feedback decentralized control for a class of strongly interconnected nonlinear systems suffering stochastic disturbances. An state observer is designed to approximate the unmeasurable state signals. Using the approximation capability of radial basis function neural networks (NNs) and employing classic adaptive control strategy, an observer-based adaptive backstepping decentralized controller is developed. In the control design process, NNs are applied to model the uncertain nonlinear functions, and adaptive control and backstepping are combined to construct the controller. The developed control scheme can guarantee that all signals in the closed-loop systems are semiglobally uniformly ultimately bounded in fourth-moment. The simulation results demonstrate the effectiveness of the presented control scheme.

Adaptive Neural Output-Feedback Decentralized Control for Large-Scale Nonlinear Systems With Stochastic Disturbances