Self-Tuning Control of Nonlinear Armax Models

A control weighted self-tuning minimum-variance controller with a nonlinear difference equation structure is described. An extended recursive least-squares estimation algorithm is employed to provide the adaptiveness. Performance analysis of the controller id discussed in terms of a cumulative loss function and high-order correlation functions of the system input, output and residual sequences. Simulation results from an experiment using a model identified from a real system are also provided

Minimum Variance Control of Nonlinear Systems

A minimum-variance self tuning controller with a nonlinear difference equation structure is described with the adaptiveness being provided by an extended least-squares estimation algorithm. Performance analysis is discussed in terms of a cumulative loss function and high order correlation functions that have been derived specifically for use in conjunction with nonlinear ARMAX models. Simulation results using the model of a practical process are described

PiEcewise-Linear Output-Error Methods for Parameter Estimation in Direction-Dependent Processes

In direction-dependent processes, the dynamic responses depend on the direction of the system input. The parameter estimation of these processes under noisy conditions can be somewhat problematic in terms of predictor choice and asymptotic behaviour. For parameter estimation, a convenient way to model direction dependence is to use a piecewise-linear model formulation, whose switching depends on the input direction. This paper analyses a prediction-error minimisation method for direction-dependent processes in terms of piecewise-linear dynamics. In particular, the asymptotic convergence properties are investigated and relevant conditions for the utilisation of the estimation method are given. Further, it is demonstrated that a piecewise-linear output-error predictor is preferable in situations where the impact of disturbances is predominant. The main reason for this is that it separates the disturbances from the process model