Iterative learning control for distributed parameter systems using sensor-actuator network

The paper discusses an effective approach to iterative learning control synthesis for an important class of distributed parameter systems described by partial differential equations of diffusion-advection type. Such systems are frequently encountered in mathematical modelling of numerous physical processes. The optimal tracking problem is formulated in the spirit of the optimum experimental design theory for a measuring procedure provided with a sensor network over the multidimensional spatial domain. For such a general experimental setup, the convergence of the iterative control law is provided and decentralized control synthesis discussed as well. The study is illustrated with the application of the developed numerical scheme to the process of laser heating of a silicon wafers example.

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