This paper describes learning rules using simultaneous perturbation for a neurocontroller that controls an unknown plant. When we apply a direct control scheme by a neural network, the neural network must learn an inverse system of the unknown plant. In this case, we must know the sensitivity function of the plant using a kind of the gradient method as a learning rule of the neural network. On ...

This paper presents a survey on the generic evolution of mobile robot’ neurocontrollers with a particular focus on the capacity to adapt these controllers in several environments. Several experiments on the example of the grasping task (autonomous vacuum cleaner for example) are performed and the results show that the produced neurocontroller is dedicated to the trained conditions and cannot be...

In this paper, Artificial Neural Network, one of the Artificial Intelligence (AI) techniques, for the Volt / Var control in power distribution systems with dispersed generation (DG) is proposed. Artificial neural networks have been considered due to their ability for real time control, simpler calculations and adaptability to different operating conditions. Neuro-controllers are much more effec...

Abstract—This paper focuses on a critical component of the situational awareness (SA), the control of autonomous vertical flight for vectored thrust aerial vehicle (VTAV). With the SA strategy, we proposed a neural network motion control procedure to address the dynamics variation and performance requirement difference of flight trajectory for a VTAV. This control strategy with using of NARMAL2...

The interaction of brain, body and environment can result in complex behavior with rich dynamics even for relatively simple agents. Such dynamics are, however, often notoriously difficult to analyze. In this paper we explore the case of a simple simulated robotic agent, equipped with a reactive neurocontroller and an energy level, that the agent has been evolved to re-charge. A dynamical system...

Received Jan 2, 2015 Revised Feb 10, 2015 Accepted Mar 23, 2015 The main objective of this paper is to present the design of an adaptive neuro-controller for series connected FACTS devices like Thyristor Controlled Series Capacitor (TCSC) and Thyristor controlled Power Angle Regulator (TCPAR). This control scheme is suitable for non-linear system control, in which the exact linearised mathemati...

A common t.echnique in neurocontrol is that. of controlling a plant by static state feedback using the plant's inverse dynamics , which is approximated t.hrough a learning proce:flfl. It is ,"vdl knu\vn that in t.his control mode: e:ve:n small approximation errors or, which is the same, small perturbations of the plant may lead to insta­ bility. Here, a novel approach is proposed to overcome th...

Neurocontroller minimization is beneficial for constructing small parsimonious networks that permit a better understanding of their workings. This paper presents a novel, Evolutionary Network Minimization (ENM) algorithm which is applied to fully recurrent neurocontrollers. ENM is a simple, standard genetic algorithm with an additional step in which small weights are irreversibly eliminated. EN...

One of the biggest hurdles to developing neurocontrollers is the difficulty in establishing good training data for the neural network. We propose a hybrid approach to the development of neurocontrollers that employs both evolutionary computation (EC) and neural networks (NN). EC is used to discover appropriate control actions for specific plant states. The survivors of the evolutionary process ...