Design and implementation of an adaptive critic-based neuro-fuzzy controller on an unmanned bicycle
This work addresses the challenge of controlling an unmanned bicycle with limited feedback, offering a practical solution for autonomous vehicle control.
The paper presents an adaptive critic-based neuro-fuzzy controller for an unmanned bicycle, using only system feedback as the critic's input. Simulations and experiments show improved transient response, robustness to model uncertainty, and fast online learning.
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.