  During a multi-speed transmission development process, the final calibration
of the gearshift controller parameters is usually performed on a physical test
bench. Engineers typically treat the mapping from the controller parameters to
the gearshift quality as a black-box, and use methods rooted in experimental
design -- a purely statistical approach -- to infer the parameter combination
that will maximize a chosen gearshift performance indicator. This approach
unfortunately requires thousands of gearshift trials, ultimately discouraging
the exploration of different control strategies. In this work, we calibrate the
feedforward and feedback parameters of a gearshift controller using a
model-based reinforcement learning algorithm adapted from Pilco. Experimental
results show that the method optimizes the controller parameters with few
gearshift trials. This approach can accelerate the exploration of gearshift
control strategies, which is especially important for the emerging technology
of multi-speed transmissions for electric vehicles.
