Vehicle ABS Model-Reference Self-Adaptive Control Strategy Based On Fuzzy Nural Network

This paper aims at discussing the controlling of anti-skid brake system. The model reference adaptive control and FNN model reference adaptive control which are both based on the slip-ratio control are proposed.Firstly, various of vehicle-model are introduced. The single-wheel mathematical model of the process of vehicle brake was set up. Secondly, the simple model reference adaptive control is proposed. This strategy is based on the direct-adaptive control. Through the analyzing of simulations, the simulation results are not perfect and this system is not convergence on all the roads. Thirdly, the improved model reference adaptive control is proposed. The simulation results show that the output of the system can well track the optimal slip-ratio. The strategy can guarantee the convergence on three different roads. The optimal wheel slip ratio for most road surfaces is between 0.1 and 0.3. Most ABS control strategies aim at maintaining the wheel slip ratio at 0.2. However, the difference between road surfaces is big, such as dry asphalt or icy road. Therefore, the optimal slip ratio which is given as a constant value such as 0.2 will affect the control effects of ABS. In this paper, a strategy of slip-ratio self-tuning control is proposed which is based on adding operation-cell A. The simulations to model reference adaptive control and FNN model reference adaptive control indicate that both control methods are feasible. Simulation results show that vehicle can brake at the optimal slip-ratio on different surfaces. The safety performance required by ABS is realized.