Iterative controller optimization for nonlinear systems

The links between identification and control are examined. The main trends in this research area are summarized, with particular focus on the design of low complexity controllers from a statistical perspective. It is argued that a guiding principle should be to model as well as possible before any model or controller simplifications are made as this ensures the best statistical accuracy. This does not necessarily mean that a full-order model always is necessary as well designed experiments allow for restricted complexity models to be near-optimal. Experiment design can therefore be seen as the key to successful applications. For this reason, particular attention is given to the interaction between experimental constraints and performance specifications.

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“…Hjalmarsson (1998) and Sjöberg and De Bruyne (1999). See also Sjöberg et al (2003) for an algorithm tailored especially for non-linear systems.…”

Section: Iterative Feedback Tuningmentioning

confidence: 99%

From experiment design to closed-loop control

2005

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“…Secondly it is not restricted by the type of process. Even though the theory is developed for linear systems, the references [11,35] states that …”

Section: Characteristics Of Iterative Feedback Tuningmentioning

confidence: 99%

“…The Iterative Feedback Tuning method does generate the true first order approximation of the gradients for a nonlinear process. The bias can be expected to be small for many practical applications [35] and successful tuning of PID loops for industrial processes has been reported in [23,16]. The theory has furthermore been extended to cover optimization of multivariable processes, which implies that more experiments in each iteration are necessary [14,12,19] and to cover non-minimum phase and time delay systems [22].…”

Section: Fig 2 Schematic Representation Of the Iterative Feedback Tmentioning

confidence: 99%

Improving convergence of Iterative Feedback Tuning

Huusom

Poulsen

Jørgensen

2009

Journal of Process Control

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Iterative Feedback Tuning constitutes an attractive control loop tuning method for processes in the absence of an accurate process model. It is a purely data driven approach aiming at optimizing the closed loop performance. The standard formulation ensures an unbiased estimate of the loop performance cost function gradient with respect to the control parameters. This gradient is important in a search algorithm. The extension presented in this paper further ensures informative data to improve the convergence properties of the method and hence reduce the total number of required plant experiments especially when tuning for disturbance rejection. Informative data is achieved through application of an external probing signal in the tuning algorithm. The probing signal is designed based on a constrained optimization which utilizes an approximate black box model of the process. This model estimate is further used to guarantee nominal stability and to improve the parameter update using a line search algorithm for determining the iteration step size. The proposed algorithm is compared to the classical formulation in a simulation study of a disturbance rejection problem. This type of problem is notoriously difficult for Iterative Feedback Tuning due to the lack of excitation from the reference.

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“…Two strategies were adopted in order to deal with this heavy friction: The one was to separate the tuning of the feedback and feed forward controllers and the other was to employ the Broyden-Fletcher-Glodfard-Shanno (BFGS) method as a quasi-Newton method in a parameter update law. (3) In [7] the data-driven model free control design method that was introduced by Hjalmarsson in 1994 was extended to a case where both the plant and the controller are allowed to be nonlinear. In this paper it was shown that one can obtain an estimate of the model of the plant experimentally by using the closed loop measured data (input and output signals).…”

Section: Ift and Muc Applicationsmentioning

confidence: 99%