A new statistical approach to enhance the performance of model-free optimal control algorithms
This paper presents a new statistical approach to enhance the performance of Extremum Seeking Control algorithm. Extremum seeking controls is a model-free optimization algorithm that uses dither and demodulation signals to obtain the derivative information of an input for directional search. To improve the performance and stability of the algorithm, it is needed to simplify the input signal to the system since unsteadiness in the input signal causes the algorithm to be less efficient and fail. Time-averaging and non-statistical interpolation schemes of the input change the statistical characteristics of the signal, and they are of limited help. In this research, we propose a more accurate and representative extension of the algorithm based on the Probability Density Function to preserve the statistical characteristic of the original signal. The results show that the generated signal using an interpolation based on the Probability Density Function enhances the performance of extremum seeking control algorithm in finding the optimal states of wind energy systems. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
2018 Multidisciplinary Analysis and Optimization Conference
Ashuri, T., Mayen, E. V., & Hamidi, R. (2018). A new statistical approach to enhance the performance of model-free optimal controls algorithms. AIAA 2018-3421. 2018 Multidiciplinary Analysis and Optimal Conference Proceedings. doi: 10.2514/6.2018-3421