@article {Eckhard:Bazanella:2012b,
	title = {Optimizing the convergence of data-based controller tuning},
	journal = {Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering},
	volume = {226},
	number = {4},
	year = {2012},
	note = {n/a},
	pages = {563{\textendash}574},
	abstract = {<p>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.</p>
},
	issn = {0959{\textendash}6518},
	doi = {10.1177/0959651811426062},
	author = {D. Eckhard and A. S. Bazanella}
}
