This paper presents a strategy for controlling inverter-interfaced DERs within a microgrid using an artificial neural network. The neural network implements a dynamic programming algorithm and is trained with a new Levenberg-Marquardt backpropagation algorithm. Hardware experiments were conducted to evaluate the performance of the neural network vector control method. They showed that the neura...

Neural network has good nonlinear function approximation ability. It can be widely used to identify the model of controlled plant. In this chapter, the theories of modeling uncertain plant by using two kinds of neural networks: feed-forward neural network and recurrent neural network are introduced. And two adaptive control strategies for robotic tracking control are developed. One is recurrent...

This paper presents the hardware implementation of a neural network controller for a nonlinear system. As a learning algorithm for a neural network, the reference compensation technique has been implemented on a low cost micro-controller unit (MCU), while PID controllers with counters and PWM generators are implemented on an FPGA chip. Interface between an MCU and a field programmable gate arra...

If nonlinearities such as saturation of the amplifier gain and motor torque, gear backlash, and shaft compliancesjust to name a few are considered in the position control system of marine helm, traditional control methods are no longer sufficient to be used to improve the performance of the system. In this paper an alternative approach to traditional control methods a neural network reference c...

Considering complex factors of affecting ambient temperature in Aircraft cabin, and some shortages of traditional PID control like the parameters difficult to be tuned and control ineffective, this paper puts forward the intelligent PID algorithm that makes fuzzy logic method and neural network together, scheming out the fuzzy neural net PID controller. After the correction of the fuzzy inferen...

We present a tool to estimate the performance of the neural network in a neural network based adaptive controller. Using a Bayesian approach, this tool supports verification and validation of the adaptive controller as well as on-line monitoring. In this paper, we discuss our approach and present simulation results using the adaptive controller developed for NASA’s IFCS (Intelligent Flight Cont...

This paper presents a neural network predictive controller for three phase inverter fed induction machine speed control. Thus the three phase inverter fed induction motor drive has been simulated with and without step change using NNP controller. The performance comparisons of neural network predictive controller are achieved with the help of IAE (Integral absolute error) and ITAE (Integral tim...

A robust self-organizing fuzzy-neural control (RSOFNC) system is proposed in this paper. The RSOFNC system is comprised of a self-structuring fuzzy neural network (SFNN) controller and a robust controller. The SFNN controller is the principal controller and the robust controller is designed to achieve tracking performance. In the SFNN controller design, a SFNN with the asymmetric Gaussian membe...

This paper presents an on-line learning failure-tolerant neural controller capable of controlling buildings subjected to severe earthquake ground motions. In the proposed scheme the neural controller aids a conventional H controller designed to reduce the response of buildings under earthquake excitations. The conventional H controller is designed to reduce the structural responses for a suite ...

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...