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Iterative Learning Control for uncertain systems: Robust monotonic convergence analysis 总被引:1,自引:0,他引:1
Jeroen van de Wijdeven Author Vitae Author Vitae Okko Bosgra Author Vitae 《Automatica》2009,45(10):2383-1666
In this paper, we present a novel Robust Monotonic Convergence (RMC) analysis approach for finite time interval Iterative Learning Control (ILC) for uncertain systems. For that purpose, a finite time interval model for uncertain systems is introduced. This model is subsequently used in an RMC analysis based on μ analysis. As a result, we can handle additive and multiplicative uncertainty models in the RMC problem formulation, analyze RMC of linear time invariant MIMO systems controlled by any linear trial invariant ILC controller, and formulate additional straightforward RMC conditions for ILC controlled systems. To illustrate the derived results, we analyze the RMC properties of linear quadratic (LQ) norm optimal ILC. 相似文献
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A new predictive control framework for chemical processes is presented, that has a number of fundamental differences to classical MPC. Both future disturbances and future process measurements are explicitly introduced in the model prediction, while back-off prevents violation of the inequality constraints. A feedforward trajectory, used for constraint pushing, is optimized simultaneously with a linear time-varying feedback controller, used to minimize the back-off. No feedback is generated by the receding horizon implementation itself. Via several transformations, the resulting optimization problem is rendered convex. For nonlinear processes, this applies to the sub-problem in a sequential conic optimization approach. A two stage LQG approach reduces the complexity even further for large scale systems. The method is illustrated on a HDPE reactor example and compared to a LTV-MPC. 相似文献
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A state-space algorithm for the calculation of a normalized coprime factorization of continuous-time generalized dynamical systems is presented. It is shown that two Riccati equations have to be solved to obtain this normalized coprime factorization 相似文献
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A generalized orthonormal basis for linear dynamical systems 总被引:1,自引:0,他引:1
Heuberger P.S.C. Van den Hof P.M.J. Bosgra O.H. 《Automatic Control, IEEE Transactions on》1995,40(3):451-465
In many areas of signal, system, and control theory, orthogonal functions play an important role in issues of analysis and design. In this paper, it is shown that there exist orthogonal functions that, in a natural way, are generated by stable linear dynamical systems and that compose an orthonormal basis for the signal space l2n . To this end, use is made of balanced realizations of inner transfer functions. The orthogonal functions can be considered as generalizations of, for example, the pulse functions, Laguerre functions, and Kautz functions, and give rise to an alternative series expansion of rational transfer functions. It is shown how we can exploit these generalized basis functions to increase the speed of convergence in a series expansion, i.e., to obtain a good approximation by retaining only a finite number of expansion coefficients. Consequences for identification of expansion coefficients are analyzed, and a bound is formulated on the error that is made when approximating a system by a finite number of expansion coefficients 相似文献
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Dick De Roover Okko H. Bosgra Maarten Steinbuch 《International journal of control》2013,86(10):914-929
Repetitive and iterative learning control are two modern control strategies for tracking systems in which the signals are periodic in nature. This paper discusses repetitive and iterative learning control from an internal model principle point of view. This allows the formulation of existence conditions for multivariable implementations of repetitive and learning control. It is shown that repetitive control can be realized by an implementation of a robust servomechanism controller that uses the appropriate internal model for periodic distrubances. The design of such controllers is discussed. Next it is shown that iterative learning control can be implemented in the format of a disturbance observer/compensator. It is shown that the resulting control structure is dual to the repetitive controller, and that both constitute an implementation of the internal model principle. Consequently, the analysis and design of repetitive and iterative learning control can be generalized to the powerful analysis and design procedure of the internal model framework, allowing to trade-off the convergence speed for periodic-disturbance cancellation versus other control objectives, such as stochastic disturbance suppression. 相似文献
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A dynamic programming formulation is used to derive efficient expressions for the gradient of the identification criterion with respect to the parameters of a linear time-invariant system. These expressions reduce the computational costs of the parameter optimization step in prediction error identification. 相似文献
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Simple Water Level Controller for Irrigation and Drainage Canals 总被引:1,自引:0,他引:1
J. Schuurmans A. Hof S. Dijkstra O. H. Bosgra R. Brouwer 《Canadian Metallurgical Quarterly》1999,125(4):189-195
A simple water level controller for irrigation and drainage canals is proposed; the proposed controller has a master-slave structure where the slaves control the flow rates through the control structures. The master controller consists of PI-based controllers for feedback, and a decoupler and feedforward controller that are based on the inversion of a simple dynamic model of the canal system. The applicability of the controller is demonstrated in field experiments. 相似文献