Optimizing the convergence of data-based controller tuning

Citation:
Eckhard, D, Bazanella AS.  2012.  Optimizing the convergence of data-based controller tuning. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering. 226:563–574., Number 4

Abstract:

Data-based control design methods most often consist of iterative adjustment of the controller?s parameters towards the parameter values which minimize an Formula performance criterion. Typically, batches of input-output data collected from the system are used to feed directly a gradient descent optimization algorithm ? no process model is used. Two topics are important regarding this algorithm: the convergence rate and the convergence to the global minimum. This paper discusses these issues and provides a method for choosing the step size to ensure convergence with high convergence rate, as well as a test to verify at each step whether or not the algorithm is converging to the global minimum.

Notes:

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