Introduction to Grey system theory
The Journal of Grey System
A study of Grey forecasting and its control analysis of grain yield
The Journal of Grey System
Enhancing MLP networks using a distributed data representation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The hybrid grey-based models for temperature prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Temperature prediction using fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ratio-based lengths of intervals to improve fuzzy time series forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive Sliding-Mode Control for NonlinearSystems With Uncertain Parameters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural-network hybrid control for antilock braking systems
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
The control of an antilock braking system (ABS) is a difficult problem due to its strongly nonlinear and uncertain characteristics. To overcome this difficulty, the integration of gray-system theory and sliding-mode control is proposed in this paper. This way, the prediction capabilities of the former and the robustness of the latter are combined to regulate optimal wheel slip depending on the vehicle forward velocity. The design approach described is novel, considering that a point, rather than a line, is used as the sliding control surface. The control algorithm is derived and subsequently tested on a quarter vehicle model. Encouraged by the simulation results indicating the ability to overcome the stated difficulties with fast convergence, experimental results are carried out on a laboratory setup. The results presented indicate the potential of the approach in handling difficult real-time control problems.