Being difficult to attain the precise mathematical models, traditional control methods such as proportional integral (PI) and proportional integral differentiation (PID) cannot meet the demands for real time and robustness when applied in some nonlinear systems.The neural network controller is a good replacement to overcome these shortcomings. However, the performance of neural network controll...

This paper presents a control scheme of a neural network for a DC-DC Cuk converter. The proposed neural network control (NNC) strategy is designed to produce regulated variable DC output voltage. The mathematical model of Cuk converter and artificial neural network algorithm is derived. Cuk converter has some advantages compared to other type of converters. However the nonlinearity characterist...

This paper presents a neural network approach to controlling nonlinear system. Trial-and-error correlation learning, which is a generally useful method for optimizing parameters, is applied to training a neural controller to balance an inverted pendulum. The controller is simplified by automatically pruning the hidden neurons to only two. Computer simulation shows that the trained neural contro...

This paper presents a method for real-time switching among a pool of controllers to achieve desired performance. The algorithm uses a set of neu-ral networks to evaluate the future performance of each controller to make the selection. Results are shown in simulation cases. Theoretical issues are discussed and future lines of research are stated.

The wing rock motion is mathematically described by a nonlinear differential equation with coefficients varying with angle of attack. In this paper, a neural-network-based adaptive control system is developed for the wing rock motion control. The adaptive controller comprises a neural network controller and a compensation controller. The neural network controller using a recurrent neural networ...

This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control ADNNC system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF DRBF network to online approximate an ideal controller. The DRBF network can create new hidden neuron...

In this paper, a second order plant is considered for identification using NN-predictive control technique. The neural network based predictive controller is configured based on MATLAB 7.0. This neural network controller uses a neural network model of plant. It predicts the future performance of the actual plant. The controller uses to calculate the control input. The control input will optimiz...

As a first step to the control of paraplegic gait by functional electrical stimulation (FES), the control of the swinging lower leg is being studied. This paper deals with a neural control system, that has been developed for this case. The control system has been tested for a model of the swinging lower leg using computer simulations. The neural controller was trained by supervised learning (SL...

Trajectory tracking is an essential capability of robotics operation in industrial automation. In this article, an artificial neural controller is proposed to tackle trajectory-tracking problem of an autonomous ground vehicle (AGV). The controller is implemented based on fractional order proportional integral derivative (FOPID) control that was already designed in an earlier work. A non-holonom...

In this paper, a class of uncertain chaotic systems preceded by unknown backlash nonlinearity is investigated. Combining backstepping technique with fuzzy neural network identifying, an adaptive backstepping fuzzy neural controller (ABFNC) for uncertain chaotic systems with unknown backlash is proposed. The proposed ABFNC system is comprised of a fuzzy neural network identifier (FNNI) and a rob...