Abstract: This paper investigates a novel recurrent neural network (NN)-based vector control approach for single-phase grid-connected converters (GCCs) with L (inductor), LC (inductor-capacitor) and LCL (inductor-capacitor-inductor) filters and provides their comparison study with the conventional standard vector control method. A single neural network controller replaces two current-loop PI co...

The use of a proposed recurrent neural network control system to control a four-legged walking robot is presented in this paper. The control system consists of a neural controller, a 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 are...

In this paper, the problems faced in the constrained force control is studied (uncertainties in dynamic model and the unknown constraints). A neural network (NN) controller is proposed based on the derived dynamic model of robot in the task space. The feed-forward neural network is used to adaptively compensate for the uncertainties in the robot dynamics. Training signals are proposed for the f...

The conventional PID (proportional-integralderivative) controller is widely applied to industrial automation and process control field because its structure is sample and its robust is well, but it do not work well for nonlinear system, time-delayed linear system and timevarying system. This paper provides a new style of PID controller that is based on artificial neural network and evolutionary...

In this paper, an adaptive fuzzy neural network (FNN) controller is proposed for missile guidance. The objective is for one defending missile (DM) to intercept an attacking missile (AM) in air battle scenario. The adaptive FNN controller is adopted to force the DM toward the AM under the existence of disturbance, and a monitoring controller is also designed to reduce the error between FNN contr...

Dr. Mohammed Yousif Hassan* Received on:28/6/2009 Accepted on:3/12/2009 Abstract Pneumatic circuits are widely used in industrial automation, such as drilling, sawing, squeezing, gripping, and spraying. Furthermore, they are used in motion control of materials and parts handling, packing machines, machine tools, foodprocessing industry and in robotics. In this paper, a Neural Network based Fuzz...

This paper presents a direct multivariable adaptive controller using neural network which adapts to the changing parameters of the multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. It base on the theory which a nonlinear multivariable systems to be controlled is divided a linear part and a nonlinear part. The controller parameters of the linear ...

This chapter shares with Chapter 9 the adoption of a model predictive control (MPC) framework for flight control applications, but the details differ substantially. In particular, the control feedback in this case is a superposition of a neural-network-based nonlinear mapping and a nonlinear state-dependent Riccati equation (SDRE) controller. The neural network is optimized (trained) online for...