Optimal sensor selection for model identification in iterative learning control of spatio-temporal systems

An approach to sensor location problem for parameter estimation of a distributed system controlled under repetitive regime is presented. In order to reduce the uncertainty of the model used for the control design, thus increasing the system performance, the iterative learning control scheme is extended with parameter estimation of mathematical model with the use of the sequential experimental design. The related sensor location problem corresponds to situation where from among all potential sites where the sensors can be placed we have to select a subset which provide the most informative measurements in order to update the system parameter estimates. Thus, in each process trial, both the control performance and process model can be substantially improved. As an illustration of the proposed approach the application to nontrivial chemical process of fuel combustion is given.

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