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In this work, a sampled-data nonlinear observer is designed using a continuous-time design coupled with an inter-sample output predictor. The proposed sampled-data observer is a hybrid system. It is shown that under certain conditions, the robustness properties of the continuoustime design are inherited by the sampled-data design, as long as the sampling period is not too large. The approach is applied to linear systems and to triangular globally Lipschitz systems.Index Terms-Input-to-output stability, nonlinear observers, sampleddata observers.

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This paper studies the design of feedback controllers for trajectory tracking in single-inputlsingle-output nonlinear systems X = f(x) + g(x) u, y = h(x). A nonlinear transformation of the form z, = K ( X ) + X(x) u that transforms this nonlinear input/output system into a linear system is first constructed. On the basis of this transformation, an approach for designing control laws for trajectory tracking4s presented. The control law is robust in the sense that small changes in it do not produce large steady state errors or loss of stability. The theory provides a unified framework for treating control problems arising in nonlinear chemical processes; this is illustrated by a batch reactor control example.

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I d e n t i f i c a t i o n of spatially varying parameters in distributed parameter systems from noisy d a t a i s a n ill-posed problem.The concept of r e g u l a r i z a t i o n , widely used in solving linear Fredholm integral equat i o n s , i s developed f o r t h e i d e n t i f i c a t i o n of parameters in distributed parameter systems.A general regularizat i o n i d e n t i f i c a t i o n t h e o r y i s f i r s t p r e s e n t e d and then applied to a parabolic identification problem. Methods f o r t h e numerical implementationof the regularization i d e n t i f i c a t i o n approach are also presented.

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