Prof. Dr. Diego Eckhard
Universidade Federal do Rio Grande do Sul
Campus do Vale - Building 43112 - Room 218 (email)
Campus do Vale - Building 43112 - Room 218 (email)
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.
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