This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences...

Abstract. Aim at the limitation of traditional PID controller has certain limitation, the traditional PID control is often difficult to obtain satisfactory control performance, and the RBF neural network is difficult to meet the requirement of real-time control system. To overcome it, an adaptive PID control strategy based on (RBF) neural network is proposed in this paper. The results show that...

In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and u...

In this paper, the combination of RBF (Radial Basis Function) neural network and sliding mode control, which is used for vibration control, is examined. The approach is based on a sliding mode control methodology which drives the system towards a sliding surface by tuning the parameters of the controller using Gaussian radial basis function neural network. The input and output of RBF neural net...

This paper presents a model reference adaptive PD control scheme based on RBF neural network for the greenhouse climate control problem. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is used to validate the proposed control scheme. Compared with the conventional adaptive PD control scheme based on RBF neural netw...

Abstract In this thesis, a fuzzy RBF neural network PID (RBFfnn-pid) control method is designed for temperature deviation concerning the steam curing system in kettle of aerated concrete block production line. The (TCS) models are established respectively under and RBFfnn-pid. Then, effect analyzed by simulation different situations. results show that, condition step signal inputting, RBFfnn-pi...

The current fault diagnosis methods based on conventional BP neural network and RBF neural network exist long training time, slow convergence speed and low judgment accuracy rate and so on. In order to improve the ability of fault diagnosis, this paper puts forward a kind of fault diagnosis method based on RBF Neural Network improved by PSO algorithm. By using particle swarm algorithm’s heurist...

Adaptive PID controller based on real base function (RBF) network identification by optimal tuning of proportional– integral–derivative (PID) controller parameter is necessary for thematic factory operation of an automatic voltage regulator (AVR) system. This study presents a combined genetic algorithm (GA) and real base function network (RBF) identification control approach to determine the op...