Expert Systems with Applications: An International Journal
An error-based on-line rule weight adjustment method for fuzzy PID controllers
Expert Systems with Applications: An International Journal
A fuzzy aid rear-end collision warning/avoidance system
Expert Systems with Applications: An International Journal
Adaptive fuzzy control for inter-vehicle gap keeping
IEEE Transactions on Intelligent Transportation Systems
Cascade Architecture for Lateral Control in Autonomous Vehicles
IEEE Transactions on Intelligent Transportation Systems
A new approach to adaptive fuzzy control: the controller output error method
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Membership function modification of fuzzy logic controllers withhistogram equalization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On stability of fuzzy systems expressed by fuzzy rules with singleton consequents
IEEE Transactions on Fuzzy Systems
Evolutionary learning of fuzzy logic controllers and their adaptation through perpetual evolution
IEEE Transactions on Fuzzy Systems
Online global learning in direct fuzzy controllers
IEEE Transactions on Fuzzy Systems
IEEE Spectrum
Hierarchical Singleton-Type Recurrent Neural Fuzzy Networks for Noisy Speech Recognition
IEEE Transactions on Neural Networks
A New Methodology for the Online Adaptation of Fuzzy Self-Structuring Controllers
IEEE Transactions on Fuzzy Systems
Cooperative Adaptive Cruise Control: A Reinforcement Learning Approach
IEEE Transactions on Intelligent Transportation Systems
A high speed railway control system based on the fuzzy control method
Expert Systems with Applications: An International Journal
Advanced overtaking behaviors for blocking opponents in racing games using a fuzzy architecture
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Usually, vehicle applications need to use artificial intelligence techniques to implement control strategies able to deal with the noise in the signals provided by sensors, or with the impossibility of having full knowledge of the dynamics of a vehicle (engine state, wheel pressure, or occupants' weight). This work presents a cruise control system which is able to manage the pedals of a vehicle at low speeds. In this context, small changes in the vehicle or road conditions can occur unpredictably. To solve this problem, a method is proposed to allow the on-line evolution of a zero-order TSK fuzzy controller to adapt its behaviour to uncertain road or vehicle dynamics. Starting from a very simple or even empty configuration, the consequents of the rules are adapted in real time, while the membership functions used to codify the input variables are modified after a certain period of time. Extensive experimentation in both simulated and real vehicles showed the method to be both fast and precise, even when compared with a human driver.