Abstract. Differential repetitive processes are a distinct class of continuousdiscrete 2D linear systems of both systems theoretic and applications interest. The feature which makes them distinct from other classes of such systems is the fact that information propagation in one of the two independent directions only occurs over a finite interval. Applications areas include iterative learning control and iterative solution algorithms for classes of dynamic nonlinear optimal control problems based on the maximum principle, and the modelling of numerous industrial processes such as metal rolling, and long-wall cutting etc. The new results in is paper solve a general optimal problem in the presence of non-stationary dynamics.

This paper introduces a model for the dynamics of a sorption process from the industrial water supply and sewage treatment industries that is a continuous version of the Roesser state-space model for 2D discrete systems. Conditions for unique solvability and the representation formula are then developed together with the solution of an optimization problem using boundary control. The solution of this optimization problem by state feedback is also developed.

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Differential linear repetitive processes are a class of continuous-discrete 2D systems of both systems theoretic and applications interest. The feature which makes them distinct from other classes of such systems is the fact that information propagation in one of the two independent directions only occurs over a finite interval. In this paper we develop an operator theory approach for the study of basic systems theoretic structural and control properties of these processes. In particular, we first develop a characterization of the range space of an operator generated by dynamics of the processes under consideration and use it to characterize a controllability property. Also we extend this operator setting to produce new results for a (again physically relevant) linear-quadratic optimization problem for these processes and the resulting optimal feedback control law.