In this paper, an intelligent controller to control a smart base-isolated masonry structure is proposed. The smart base isolation consists of rubber bearings and a magnetorheological damper. This semi-active scheme takes advantage of the low power requirement of magnetorheological devices to achieve structural control. The controller is capable of mapping the behaviour of the controlled structu...

This paper develops novel neural networks suitable for direct embedment within a feedback loop. The structure of the embedded system is inspired by the proportional plus derivative control with moving average excitations. A parameter vector based uniform description of the problem of neural network design is presented. Difficulties associated with traditional mathematicallyguided design methods...

Robotic controllers take advantage from neural network learning capabilities as long as the dimensionality of the problem is kept moderate. This paper explores the possibilities offered by the combination of several neural networks to design more complex modular controllers. This modularity is based on an internal partitioning of the problem. The partitioning must remain hidden, and should not ...

Many recent motor control studies such as the synthesis of robotic controllers have been inspired from biological systems. In this paper, a new neural network approach, inspired from neurophysiological theories, is proposed to control multidimensional redundant systems. The main goal of this approach is to perform pinch or grip movement by modeling the cerebro-cerebellar learning of visuomotor ...

This study presents an extended model of homeostatic adaptation designed to exploit the internal dynamics of a neural network in the absence of sensory input. In order to avoid typical convergence to asymptotic states under these conditions plastic changes in the network are induced in evolved neurocontrollers leading to a renewal of dynamics that may favour sensorimotor adaptation. Other measu...

Evolutionary autonomous agents whose behavior is determined by a neurocontroller “brain” are a promising model for studying neural processing. Nevertheless, they are missing an important quality prevalently found in all levels of natural systems, fault-tolerance, the lack of which results in overly simplistic neurocontrollers. We present a way of modifying a given evolutionary process for encou...

A direct adaptive neurocontroller is proposed to reduce structure response to earth vibrations by actively creating an equal but opposite force to that of the first mode force of the structure. While earthquake forces are generally considered to be unpredictable, the short-term predictions by the proposed neurocontrol architecture significantly reduce structure vibrations. To demonstrate its ge...

. ABSTRACT. Artificial Neural Networks (ANN) are an emerging technology, yet, in continuous dynamic behavior, much work has been done to attempt to generate a formal method to design a controller based on this technology. Based on this fact we are starting to work towards contributing to the generation of a formal method of the application of neurocontrol and thus present several control scheme...

Whilst the necessity of finding an intelligent-based controlling method for a two-leg walking robot increases, the balance of the under-actuated leg consisting of two links is emphasized in this study. This is not only a nonlinear structure, but also a single-input double-output system. However, the problem becomes concrete through the proposed diagonal recurrent neural networks (DRNN) method. ...

The problem of obtaining the error at the output of a neuro sliding mode controller is analyzed in this paper. The controller operates in discrete time and the method presented describes an error measure that can be used if the task to be achieved is to drive the system under control to a predefined sliding regime. Once the task specific output error is calculated, the neurocontroller parameter...