Neuroevolution with CMA-ES for the tuning of a PID controller of nonholonomic car-like mobile robot
Résumé
In the field of mobile robotics, finding an optimal control policy is a challenging task. PID controllers have been widely used in the industry. However, tuning a PID controller is not easy, especially to take into consideration the fluctuation in the precision of the perception. We propose a neuroevolution algorithm to find the optimal parameters of the controller in real time. The controller is tuned by a neural network which is trained by with the covariance matrix adaption evolution strategy (CMA-ES). The neural network takes into account both the error and the uncertainty of the measurement the tuning of the parameters. The level of uncertainty in the measurement is given by the covariance matrix of the Kalman filter.
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