We present a neuro-fuzzy controller for intelligent cruise control of semiautonomous vehicles. This paper addresses the problem of longitudinal control that aims at regulating the speed of the controlled vehicle in order to maintain constant time headway with respect to the vehicle in front. A fuzzy radial basis function network (FRBFN) longitudinal controller is designed to incorporate the merits of fuzzy logics as well as neural networks. The FRBFN is prestructured, and its parameters are configured such that they are associated with their physical meaning. The parameters of the output layer are learned online via gradient algorithm. An attractive feature of the proposed method is that it does not require the training data and the vehicle longitudinal dynamic model. Simulation results on a vehicle theoretical model are provided to demonstrate the effectiveness of this controller. In order to investigate the proposed control algorithms in real-life situations, a small-scaled vehicle with computer and sensors onboard is developed. Experimental results of a conventional PID controller and the FRBFN controller are provided for comparison.
- Autonomous vehicle
- Fuzzy neural networks
- Longitudinal control
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering