PSO-Self-Organizing Interval Type-2 Fuzzy Neural Network for Antilock Braking Systems
- 112 Downloads
Nowadays, the antilock braking system (ABS) is the standard in all modern cars. The function of ABS is to optimize the maximize wheel traction by preventing wheel lockup during braking, so it can help the drivers to maintain steering maneuverability. In this study, a self-organizing interval type-2 fuzzy neural network (SOT2FNN) control system is designed for antilock braking systems. This control system comprises a main controller and a robust compensation controller; the SOT2FNN as the main controller is used to mimic an ideal controller, and the robust compensation controller is developed to eliminate the approximation error between the main controller and the ideal controller. To guarantee system stability, adaptive laws for adjusting the parameters of SOT2FNN based on the gradient descent method are proposed. However, in control design, the learning rates of adaptive law are very important and they significantly affect control performance. The particle swarm optimization method is therefore applied to find the optimal learning rates for the weights in reduction layer and also for the means, the variances of the Gaussian functions in the input membership functions. Finally, the numerical simulations of ABS response in different road conditions are provided to illustrate the effectiveness of the proposed approach.
KeywordsType-2 fuzzy logic system Antilock braking system Particle swarm optimization Self-organizing learning algorithm
The authors appreciate the financial support in part from the Nation Science Council of Republic of China under Grant NSC 101-2221-E-155-026-MY3.
- 16.Tao, C.W., Chang, C.W., Taur, J.S.: A simplify type reduction for interval type-2 fuzzy sliding controllers. Int. J. Fuzzy Syst. 15(4), 460–470 (2013)Google Scholar
- 17.Chang, Y.H., Chen, C.L., Chan, W.S., Lin, H.W.: Type-2 fuzzy formation control for collision-free multi-robot systems. Int. J. Fuzzy Syst. 15(4), 435–451 (2013)Google Scholar
- 21.Pratama, M., Zhang, G., Er, M.J., Anavatti, S.: An incremental type-2 meta-cognitive extreme learning machine. IEEE Trans. Cybern. 47(2), 339–353 (2016)Google Scholar
- 31.Kennedy, J., Eberhart, R.C.: Particle swarm optimization. Proc. IEEE Conf. Neural Netw. 4, 1942–1948 (1995)Google Scholar
- 38.Lin, C.M., Chen, Y.M., Hsueh, C.S.: A self-organizing interval type-2 fuzzy neural network for radar emitter identification. Int. J. Fuzzy Syst. 16(1), 20–30 (2014)Google Scholar
- 39.Harned, J., Johnston, L., Scharpf, G.: Measurement of tire brake force characteristics as related to wheel slip (antilock) control system design. SAE Pap. 78, 909–925 (1969)Google Scholar
- 40.Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic System: Introduction and New Directions. IEEE Computational Intelligence Magazine. Prentice-Hall, Upper Saddle River (2001)Google Scholar
- 41.Slotine, J.J.E., Li, W.P.: Applied Nonlinear Control, Englewood Cliffs. Prentice-Hall, Englewood Cliffs (1991)Google Scholar