Quantum mechanics

Adaptive control

Control (management)

Backstepping

Biology

Computer science

Bounded function

Controller (irrigation)

Stochastic neural network

Physics

Nonlinear system

Mathematics

Agronomy

Mathematical analysis

Artificial intelligence

Control theory (sociology)

Artificial neural network

Recurrent neural network

IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)

This paper focuses on the problem of neural-network-based decentralized adaptive output-feedback control for a class of nonlinear strict-feedback large-scale stochastic systems. The dynamic surface control technique is used to avoid the explosion of computational complexity in the backstepping design process. A novel direct adaptive neural network approximation method is proposed to approximate the unknown and desired control input signals instead of the unknown nonlinear functions. It is shown that the designed controller can guarantee all the signals in the closed-loop system to be semiglobally uniformly ultimately bounded in a mean square. Simulation results are provided to demonstrate the effectiveness of the developed control design approach.

Neural-Network-Based Decentralized Adaptive Output-Feedback Control for Large-Scale Stochastic Nonlinear Systems