Journal ArticleDOI
A direct adaptive neural-network control for unknown nonlinear systems and its application
J. R. Noriega,Hong Wang +1 more
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TLDR
It is shown that the control signals obtained can also make the real system output close to the set point, and the applicability of the proposed method is demonstrated.Abstract:
In this paper a direct adaptive neural-network control strategy for unknown nonlinear systems is presented. The system considered is described by an unknown NARMA model, and a feedforward neural network is used to learn the system. Taking the neural network as a neural model of the system, control signals are directly obtained by minimizing either the instant difference or the cumulative differences between a set point and the output of the neural model. Since the training algorithm guarantees that the output of the neural model approaches that of the actual system, it is shown that the control signals obtained can also make the real system output close to the set point. An application to a flow-rate control system is included to demonstrate the applicability of the proposed method and desired results are obtained.read more
Citations
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A comprehensive review for industrial applicability of artificial neural networks
TL;DR: An organized and normalized review of the industrial applications of artificial neural networks, in the last 12 years, is presented to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.
Journal ArticleDOI
Review of the applications of neural networks in chemical process control : simulation and online implementation
TL;DR: The review reveals the tremendous prospect of using neural networks in process control and shows the multilayered neural network as the most popular network for such process control applications and also shows the lack of actual successful online applications at the present time.
Journal ArticleDOI
Neural Network Control of a Flexible Robotic Manipulator Using the Lumped Spring-Mass Model
Changyin Sun,Wei He,Jie Hong +2 more
TL;DR: Adaptive neural networks (NNs) are employed for control design to suppress vibrations of a flexible robotic manipulator via the lumped spring-mass approach, and uniform ultimate boundedness of the closed-loop system is ensured.
Journal ArticleDOI
New Chaotic PSO-Based Neural Network Predictive Control for Nonlinear Process
TL;DR: A novel nonlinear neural network (NN) predictive control strategy based on the new tent-map chaotic particle swarm optimization (TCPSO) is presented to enhance the convergence and accuracy of the TCPSO.
Journal ArticleDOI
Adaptive CMAC-based supervisory control for uncertain nonlinear systems
Chih-Min Lin,Ya-Fu Peng +1 more
TL;DR: The adaptive laws of the control system are derived in the sense of Lyapunov function, so that the stability of the system can be guaranteed.
References
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Book
Nonlinear Control Systems
Alberto Isidori,M. Thoma,Eduardo D. Sontag,B. W. Dickinson,A. Fettweis,J. L. Massey,J. W. Modestino +6 more
TL;DR: In this paper, a systematic feedback design theory for solving the problems of asymptotic tracking and disturbance rejection for linear distributed parameter systems is presented, which is intended to support the development of flight controllers for increasing the high angle of attack or high agility capabilities of existing and future generations of aircraft.
Journal ArticleDOI
Identification and control of dynamical systems using neural networks
TL;DR: It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems and the models introduced are practically feasible.
Journal ArticleDOI
Generalized predictive control—Part I. The basic algorithm
TL;DR: A novel method—generalized predictive control or GPC—is developed which is shown by simulation studies to be superior to accepted techniques such as generalized minimum-variance and pole-placement and to be a contender for general self-tuning applications.
Journal ArticleDOI
30 years of adaptive neural networks: perceptron, Madaline, and backpropagation
Bernard Widrow,Michael A. Lehr +1 more
TL;DR: The history, origination, operating characteristics, and basic theory of several supervised neural-network training algorithms (including the perceptron rule, the least-mean-square algorithm, three Madaline rules, and the backpropagation technique) are described.
Journal ArticleDOI
Actuator fault diagnosis: an adaptive observer-based technique
Hong Wang,S. Daley +1 more
TL;DR: A novel approach for the fault diagnosis of actuators in known deterministic dynamic systems by using an adaptive observer technique under the assumption that the system state observer can be designed such that the observation error is strictly positive real (SPR).