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A novel data based predictive control method is proposed by introducing the notion of neural network based predictive control to a model-free control method based on Simultaneous Perturbation Stochastic Approximation (SPSA). The controller is constructed through use of a Function Ap-proximator (FA), which is fixed as a neural network here. In the novel approach, the ability of the controller has been...
This paper presents an Adaptive Predictive Control strategy based on Neural Networks for nonlinear systems. In order to train the Neural Network controller, an identification of the system is carried out by the Neural Network Identifier. This second Neural Network provides the training terms related to the nonlinear system dynamics. In this way it is possible to train the Neural Network controller...
In communication channel of a tele-operation control task, there always exists a time-delay problem which causes poor performance and even instability of the system. To overcome the time-delay problem, in this paper, the Smith predictor configuration is considered. The Smith predictor can stabilize the system by taking the time delay term out of the characteristic equation when the system model is...
In order to improve fuel economy and emission of liquefied petroleum gas bus in driving condition, this paper researches a receding horizon control method of gas mass flow for liquefied petroleum gas bus engine based on neural network. Firstly, the relation between the valve opening angle, gas pressure and gas mass flow of liquefied petroleum gas bus engine is according to gas mass flow dynamics....
This paper describes predictive control of pneumatic actuator. Pneumatic cylinder is a complex nonlinear object because of friction and compressibility of the air. A precision and fast control such an object using traditional methods of control is very difficult. Predictive control with neural networks model of the plant is one of the modern approaches to control complex nonlinear objects.
This paper presents a new control approach for nonlinear network-induced time delay systems using online reset control, neural networks, and dynamic Bayesian networks. We construct a state-feedback based nominal control to develop a linearized system model. The reset control and neural network are employed to compensate for system error due to time delay effect. Finally, we achieve probabilistic modeling...
A nonlinear multivariable adaptive decoupling PID control strategy based on multiple models and neural network is proposed for a class of uncertain discrete time nonlinear dynamical systems. The adaptive decoupling PID controller is composed of a linear adaptive PID decoupling controller, a neural network nonlinear adaptive PID decoupling controller and a switch mechanism. The PID parameters of such...
In the practical application of decouple control for MIMO system, precise mathematical model of controlled object is greatly depended on and cause unsatisfactory control effects. Complexity of algorithms based on large-scale neural network affects the practicability and real-time performance of control algorithms. A kind of MIMO decouple control system based on double-neuron adaptive predictive and...
A special single neural network predictive control algorithm aimed at practical application of specific large time-delay nonlinear system is presented. This algorithm is based on a variable structure objective optimization controller to decouple the multiple control variables of the system. Consequently, the multiple single neural network predictive controller with a multi-step-ahead differential...
A new adaptive nonlinear state predictor (ANSP) is presented for a class of nonlinear systems with input time-delay. High-order neural network (HONN) incorporating with a special identification model is first employed to identify the unknown nonlinear time-delay system. The predicted weight updating law of neural network is calculated based on the identification, which can be used to predict the future...
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