  Fuzzy critic-based learning forms a reinforcement learning method based on
dynamic programming. In this paper, an adaptive critic-based neuro-fuzzy system
is presented for an unmanned bicycle. The only information available for the
critic agent is the system feedback which is interpreted as the last action
performed by the controller in the previous state. The signal produced by the
critic agent is used along with the error back propagation to tune (online)
conclusion parts of the fuzzy inference rules of the adaptive controller.
Simulations and experiments are conducted to evaluate the performance of the
proposed controller. The results demonstrate superior performance of the
developed controller in terms of improved transient response, robustness to
model uncertainty and fast online learning.
