Modular Neural Network Control of Nonlinear Systems

Dynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks

Dynamic sliding mode control (DSMC) of nonlinear systems using neural networks is proposed. In DSMC the chattering is removed due to the integrator which is placed before the input control signal of the plant. However, in DSMC the augmented system is one dimension bigger than the actual system i.e. the states number of augmented system is more than the actual system and then to control of such ...

Neural Network Control of Robot Manipulators and Nonlinear Systems Neural Network Control of Robot Manipulators and Nonlinear Systems

Adaptive Neural Network Control of Uncertain State-Constrained Nonlinear Systems

In this paper, an adaptive neural network control scheme is developed for a class of uncertain nonlinear systems in strict-feedback form subject to the constraints on the full states and unknown system drift dynamics. The Integral Barrier Lyapunov Functionals (iBLF) are utilized to handle the state constraints directly, leading to the relax of the feasibility conditions compared with pure track...

Neural Network Based Bicriterial Dual Control of Nonlinear Systems

A bicriterial dual controller for nonlinear stochastic systems is suggested. Two separate criterions are designed and used to introduce one of opposing aspects between estimation and control; caution and probing. A system is modelled using a multilayer perceptron network. Parameters of the network are estimated by the Gaussian sum method which allows to determine conditional probability density...

Neural Network-based Mrac Control of Dynamic Nonlinear Systems

This paper presents direct model reference adaptive control for a class of nonlinear systems with unknown nonlinearities. The model following conditions are assured by using adaptive neural networks as the nonlinear state feedback controller. Both full state information and observer-based schemes are investigated. All the signals in the closed loop are guaranteed to be bounded and the system st...