A direct adaptive simultaneous perturbation stochastic approximation (DA SPSA) control system with a diagonal recurrent neural network (DRNN) controller is proposed. The DA SPSA control system with DRNN has simpler architecture and parameter vector size that is smaller than a feedforward neural network (FNN) controller. The simulation results show that it has a faster convergence rate than FNN ...

Typical production objectives in distillation process require the delivery of products whose compositions meet certain specifications. The distillation control system, therefore, must hold product compositions as near the set points as possible in faces of upset. In this project, inferential model predictive control, that utilizes an artificial neural network estimator and model predictive cont...

This paper addresses control design in networked control system by considering stochastic packet dropouts in the forward path of the control loop. The packet dropouts are modelled by mutually independent stochastic variables satisfying Bernoulli binary distribution. A sliding mode controller is utilized to overcome the adverse influences of stochastic packet dropouts in networked control system...

An adaptive truss structure with self-learning active vibration control system is developed. A fuzzy-neural network (FNN) controller with adaptive membership functions is presented. The experimental setup of a two-bay truss structure with active members is constructed, and the FNN controller is applied to vibration suppression of the truss. The controller first senses the output of the accelero...

In this paper, a novel control method by using parallel online mixing and supplying technology for glue system with multi-input, strong coupling and nonlinear was provided. The neural network controller design method is adopted to design the intelligent controller, in which one as the main controller and the other one as the identifier to revise main controller. Both advantages and disadvantage...

A fuzzy–neural sliding-mode (FNSM) control system is developed to control power electronic converters. The FNSM control system comprises a neural controller and a compensation controller. In the neural controller, an asymmetric fuzzy neural network is utilized to mimic an ideal controller. The compensation controller is designed to compensate for the approximation error between the neural contr...

A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounde...

This paper proposes a direct adaptive backstepping control scheme for a class of multi-input-multioutput nonlinear uncertain non-affine systems using output recurrent wavelet neural networks (ORWNNs), called DABCORWNN. The proposed ORWNN combines the advantages of wavelet-based neural network, fuzzy neural network (FNN), and output feedback layer. For the tracking of nonlinear non-affine system...