This paper applies a new kind of recurrent neu ral networks (RNN) called Echo State Networks (ESN) [4] to the classical problem of motor speed control for a differential drive robot. ESNs can be trained orders of magnitude faster than other RNNs and previous simulation-based investigations showed promising results when ESNs where used as black box models in the domain of system identification or as a low-level plant controllers [11]. This paper validates for the first time the predicted superior simulation results by physical experiments. In order to compare the quality of an ESN controller fairly to the original PID based controller a complete test work flow is established. It consists of an embedded implementation of the ESN motor-controller, a trace facility, a procedure to train a new ESN motor-controller according to these traced data and a user interface to define and control test-drives of the robot. The results prove that the ESN controller shows a slightly better control quality as a PID controller with respect to various important error norms from control theory. For the experiments we used a RoboCup robot and for this special application scenario the ESN controller actually outperforms the PID.
['Matthias Salmen', 'Paul-Gerhard Plöger']
Echo State Networks used for Motor Control