Purpose – This paper seeks to present a feedback error learning neuro-controller for an unstable research helicopter. Design/methodology/approach – Three neural-aided flight controllers are designed to satisfy the ADS-33 handling qualities specifications in pitch, roll and yaw axes. The proposed controller scheme is based on feedback error learning strategy in which the outer loop neural contro...

In this paper, a self-tuning adaptive neural controller strategy for unknown nonlinear system is presented. The system considered is described by an unknown NARMA-L2 model and a feedforward neural network is used to learn the model with two stages. The first stage is learned off-line with two configuration serial-parallel model & parallel model to ensure that model output is equal to actual out...

This paper discusses the systematic design of an adaptive feedback linearizing neurocontroller for a high-order model of the synchronous machine/infinite bus power system. The power system is first modelled as an input-output nonlinear discrete-time system approximated by two neural networks. The approach allows a simple linear pole-placement controller (which is itself not a neural network) to...

In this work, a new dynamical on-line learning algorithm for robust model-free neuro-adaptive control of a class of nonlinear systems with uncertain dynamics is proposed and experimentally tested in order to evaluate its performance and practical feasibility in industrial settings. The control application studied is the trajectory tracking control task for the first three joints of an open arch...

In this article we describe a methodology for evolving neurocontrollers of autonomous mobile robots without human intervention. The presentation, which spans from technological and methodological issues to several experimental results on evolution of physical mobile robots, covers both previous and recent work in the attempt to provide a unified picture within which the reader can compare the e...

A preference is not located anywhere in the agent’s cognitive architecture, but it is rather a constraining of behaviour which is in turn shaped by behaviour. Based on this idea, a minimal model of behavioural preference is proposed. A simulated mobile agent is modelled with a plastic neurocontroller, which holds two separate high dimensional homeostatic boxes in the space of neural dynamics. A...

A new semiactive control strategy for seismic response reduction using a neurocontroller and a magnetorheological (MR) fluid damper is proposed. The proposed control system adopts a clipped algorithm which induces the MR damper to generate approximately the desired control force. The improved neuro-controller, which was developed by employing the training algorithm based on a cost function and ...

The design of closed-loop structural control systems necessitates a certain level of robustness to cope with system uncertainties. Neurocontrollers, a type of adaptive control system, have been proposed to cope with those uncertainties. However, the performance of neural networks can be substantially influenced by the choice of the input space, or the hyperspace in which the representation lies...