We describe a model reduction algorithm which is well-suited for the reduction of large linear interconnect models. It is an orthogonal projection method which takes as the projection space the sum of the approximate dominant controllable subspace and the approximate dominant observable subspace. These approximate dominant subspaces are obtained using the Cholesky Factor ADI (CF-ADI) algorithm. We describe an improvement upon the existing implementation of CF-ADI which can result in significant savings in computational cost. We show that the new model reduction method matches moments at the negative of the CF-ADI parameters, and that it can be easily adapted to allow for DC matching, as well as for passivity preservation for multi-port RLC circuit models which come from modified nodal analysis.
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This contribution deals with different concepts of nonlinear control for mechatronic systems. Since most physical systems are nonlinear in nature, it is quite obvious that an improvement in the performance of the closed loop can often be achieved only by means of control techniques that take the essential nonlinearities into consideration. Nevertheless, it can be observed that industry often hesitates to implement these nonlinear controllers, despite all advantages existing from the theoretical point of view. On the basis of three different applications, a PWM-controlled dc-to-dc converter, namely the 'Cuk-converter, the problem of hydraulic gap control in steel rolling, and the design of smart structures with piezolelectric sensor and actuator layers, we will demonstrate how one can overcome these problems by exploiting the physical structure of the mathematical models of the considered plants.
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