<abstract xmlns="http://www.w3.org/1999/xhtml"><p> An algorithm for multiple model adaptive control of a time-variant plant in the presence of measurement noise is proposed. This algorithm controls the plant using a bank of PID controllers designed on the base of time invariant input/output models. The control signal is formed as weighting sum of the control signals of local PID controllers. The main contribution of the paper is the objective function minimized to determine the weighting coefficients. The proposed algorithm minimizes the sum of the square general error between the model bank output and the plant output. An equation for on-line determination of the weighting coefficients is obtained. They are determined by the current value of the general error covariance matrix. The main advantage of the algorithm is that the derived general error covariance matrix equation is the same as this in the recursive least square algorithm. Thus, most of the well known RLS modifications for the tracking timevariant parameters can be directly implemented. The algorithm performance is tested by simulation. Results with both SISO and MIMO time variant plants are obtained.</p></abstract>

An algorithm for multiple model adaptive control of a time-variant plant in the presence of measurement noise is proposed. This algorithm controls the plant using a bank of PID controllers designed on the base of time invariant input/output models. The control signal is formed as weighting sum of the control signals of local PID controllers. The main contribution of the paper is the objective function minimized to determine the weighting coefficients. The proposed algorithm minimizes the sum of the square general error between the model bank output and the plant output. An equation for on-line determination of the weighting coefficients is obtained. They are determined by the current value of the general error covariance matrix. The main advantage of the algorithm is that the derived general error covariance matrix equation is the same as this in the recursive least square algorithm. Thus, most of the well known RLS modifications for the tracking timevariant parameters can be directly implemented. The algorithm performance is tested by simulation. Results with both SISO and MIMO time variant plants are obtained.

Algorithm for Multiple Model Adaptive Control Based on Input-Output Plant Model

Department of Automatics, Technical University of Sofia, 1756 Sofia, Bulgaria

Cybernetics and Information Technologies

{"article-title":"Algorithm for Multiple Model Adaptive Control Based on Input-Output Plant Model"}

An algorithm for multiple model adaptive control of a time-variant plant in the presence of measurement noise is proposed. This algorithm controls the plant...

Algorithm for Multiple Model Adaptive Control Based on Input-Output Plant Model

Pubblicato online: 13 mar 2013

Pagine: 13 - 33

DOI: https://doi.org/10.2478/cait-2012-0002

Parole chiave
Multiple model adaptive control, input-output model, PID controllers, time variant plants

This content is open access.

Algorithm for Multiple Model Adaptive Control Based on Input-Output Plant Model

Pubblicato online: 13 mar 2013

Pagine: 13 - 33

DOI: https://doi.org/10.2478/cait-2012-0002

Parole chiaveMultiple model adaptive control, input-output model, PID controllers, time variant plants

This content is open access.

Parole chiave
Multiple model adaptive control, input-output model, PID controllers, time variant plants