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...

In this paper, the use of a proposed recurrent neural network control system to control a four-legged walking robot is investigated. The control system consists of a neural controller, standard PD controller and the walking robot. The robot is a planar four-legged walking robot. The proposed Neural Network (NN) is employed as an inverse controller of the robot. The NN has three layers, which ar...

In this paper, a neural network controller for constrained robot manipulators is presented. A feedforward neural network is used to adaptively compensate for the uncertainties in the robot dynamics. Training signals are proposed for the feed-forward neural network controller. The neural network weights are tuned on-line, with no off-line learning phase required. It is shown that the controller ...

The flying around monitoring task of space tumbling target is one the key links its on-orbit service. Considering practical constraints system inertia uncertainty, external disturbance, actuator saturation, and fault in engineering practice, a robust composite controller based on radial basis function (RBF) neural network proposed. First, new line sight rotation (RLOS) coordinate system, relati...

In this paper, a neural network based predictive controller is designed for controlling the liquid level of the coupled tank system. The controlled process is a nonlinear system; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. The neural network predictive controller that is discussed in this paper uses a neural network model of a nonlinear plant...

This paper proposes an adaptive TSK-type fuzzy network control (ATFNC) system for synchronization of a coupled nonlinear chaotic system. The design of the proposed ATFNC system is comprised of a neural controller and a fuzzy compensator. The neural controller uses a Takagi-Sugeno-Kang (TSK)-type fuzzy neural network (TFNN) to online mimic an ideal controller and the fuzzy compensator is designe...

In this study, an artificial neural network (ANN) application of load frequency control (LFC) of a single area power system by using a neural network controller is presented. The study has been designed for a single area interconnected power system. The comparison between a conventional Proportional and Integral (PI) controller and the proposed artificial neural networks controller is showed th...

This paper describes four methods for robot trajectory control. These methods are a standard PID controller, a Computed Torque Method (CTM), a neural network based inverse controller and a neural network based Internal Model Controller (IMC). The IMC is investigated as an alternative to the basic inverse control scheme that is difficult to implement. The results presented show the superior abil...

In this paper, dynamic structure neural network controller based on feedback linearization is proposed. The proposed method can adapt the neural network structure dynamically while it can guarantee the stability and tracking precision of system. The dynamic structure wavelet network controller is introduced in the system simulation and the performance of the controller on a system with nonlinea...

The purpose of this study is to design an intelligent control system to guide the overtaking maneuver with a higher performance than the existing systems. Unlike the existing models which consider constant values for some of the effective variables of this behavior, in this paper, a neural network model is designed based on the real overtaking data using instantaneous values for variables. A fu...