Brief paper: Dual adaptive control of nonlinear stochastic systems using neural networks

Authors:
Simon Fabri;Visakan Kadirkamanathan
Affiliations:
Department of Automatic Control & Systems Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3JD, U.K.;Department of Automatic Control & Systems Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3JD, U.K.
Venue:
Automatica (Journal of IFAC)
Year:
1998

Citing 7
Cited 4

Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A function estimation approach to sequential learning with neural networks

Neural Computation
A two-stage dual suboptimal controller for stochastic systems using approximate moments

Automatica (Journal of IFAC)
Paper: Dual adaptive control and uncertainty effects in macroeconomic systems optimization

Automatica (Journal of IFAC)
Paper: Dual adaptive control of chip refiner motor load

Automatica (Journal of IFAC)
Identification and control of dynamical systems using neural networks

IEEE Transactions on Neural Networks
Gaussian networks for direct adaptive control

IEEE Transactions on Neural Networks

Dual adaptive dynamic control of mobile robots using neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Dual version of neural adaptive controller for stochastic nonlinear systems with functional uncertainty

Control and Intelligent Systems
Dual adaptive ANN controllers based on wiener models for controlling stable nonlinear systems

ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Self-tuning control of non-linear systems using gaussian process prior models

Switching and Learning in Feedback Systems

Quantified Score

Hi-index	22.14

Visualization

Abstract

A suboptimal dual adaptive system is developed for control of stochastic, nonlinear, discrete time plants that are affine in the control input. The nonlinear functions are assumed to be unknown and neural networks are used to approximate them. Both Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are considered and parameter adjustment is based on Kalman filtering. The result is a control law that takes into consideration the uncertainty of the parameter e stimates, thereby eliminating the need to perform prior open-loop plant identification. The performance of the system is analyzed by simulation and Monte Carlo analysis.