Comparison of extremum seeking control algorithms for robotic applications

The purpose of this paper is to help engineers and researches to choose among the extremum seeking control (ESC) techniques for robotic applications such as object grasping, active object recognition and viewpoint optimization. These techniques are categorized into five main groups: Sliding mode ESC, neural network ESC, approximation based ESC, perturbation based ESC and adaptive ESC. These groups are explained briefly by stressing their working principles and the effect of the parameters. Then, the techniques are compared with respect to their robustness to noise and system dynamics by simulations. In conclusion, we propose the usage of the approximation based methods when the noise level is negligible. When noise is present, the neural network based optimizers are a better choice thanks to their hysteresis functions. However, if the system has both high noise and dynamic effects, then the perturbation based method is preferable since large motions provide robustness to noise and smooth references generated by the algorithm are less likely to cause instability. An application example is also given on texture density maximization.