This paper presents a Newton-iteration method for obtaining a J-J spectral factorisation of systems from non-parametric characterisations using identification techniques. Systems are assumed to be Fréchet differentiable discrete-time maps. The technique may be used on nonlinear nonparametric time-response representations. The scheme requires stabilised identification and stabilised inverse iden...

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Previous research has shown that sensor-motor tasks in mobile robotics applications can be modelled automatically, using NARMAX system identification, where the sensory perception of the robot is mapped to the desired motor commands using nonlinear polynomial functions, resulting in a tight coupling between sensing and acting — the robot responds directly to the sensor stimuli without having in...

Energy load forecasting for optimization of chiller operation is a topic that has been receiving increasing attention in recent years. From an engineering perspective, the methodology designing and deploying system should take into account several issues regarding prediction horizon, available data, selection variables, model adaptation. In this paper these are parsed to develop neural forecast...

A new iterative learning controller is proposed for a general unknown discrete time-varying nonlinear non-affine system represented by NARMAX (Nonlinear Autoregressive Moving Average with eXogenous inputs) model. The proposed controller is composed of an iterative learning neural identifier and an iterative learning controller. Iterative learning control and iterative learning identification ar...

In this paper, a simple neural network (NN) control scheme is developed for a class of discrete-time multi-input multi-output (MIMO) non-affine nonlinear systems with triangular form inputs and disturbances. The system studied is described by NARMAX (Nonlinear Auto Regressive Moving Average with eXogenous inputs) model. Firstly, by using implicit function theorem, the existence of the implicit ...

The lack of a theory-based design methodology for mobile robot control programs means that control programs have to be developed through an empirical trial-and-error process. This can be costly, time consuming and error prone. In this paper we show how to develop a theory of robot–environment interaction, which would overcome the above problem. We show how we can model a mobile robot’s task (so...

In mobile robotics, it is common to find different control programs designed to achieve a particular robot task. It is often necessary to compare the performance of such controllers. So far this is usually done qualitatively, because of a lack of quantitative behaviour analysis methods. In this paper we present a novel approach to compare robot control codes quantitatively, based on system iden...