Intelligent exponential sliding-mode control with uncertainty estimator for antilock braking systems

Abstract

The purpose of the antilock braking system (ABS) is to regulate the wheel longitudinal slip at its optimum point in order to generate the maximum braking force; however, the vehicle braking dynamic is highly nonlinear. To relax the requirement of detailed system dynamics, this paper proposes an intelligent exponential sliding-mode control (IESMC) system for an ABS. A functional recurrent fuzzy neural network (FRFNN) uncertainty estimator is designed to approximate the unknown nonlinear term of ABS dynamics, and the parameter adaptation laws are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the stable control performance. Since the outputs of the functional expansion unit are used as the output weights of the FRFNN uncertainty estimator, the FRFNN can effectively capture the input-output dynamic mapping. In addition, a nonlinear reaching law, which contains an exponential term of sliding surface to smoothly adapt the variations of sliding surface, is designed to reduce the level of the chattering phenomenon. Finally, the simulation results demonstrate that the proposed IESMC system can achieve robustness slip tracking performance in different road conditions.

A Study on the Vehicle Antilock System Based on Adaptive Neural Network Sliding Mode Control

“…Zhang et al [13], developed a quarter-vehicle aggregate uncertainty wheel slip ratio dynamics model used RBFNN with unknown optimal weight vectors adaptively adjusted to approximate and compensate for the aggregate uncertainty, and designed a new tracking differentiator to compute the derivatives of the desired wheel slip ratio, and the proposed control strategy is able to track the desired wheel slip ratio quickly and accurately. Hsu [14] proposed an intelligent exponential sliding mode control system based on a quarter-vehicle model, designed a function recursive fuzzy neural network uncertainty estimator to approximate the unknown nonlinear term of the ABS dynamics, and derived the parameter adaptive law in the sense of projection algorithms and Lyapunov's stability theorem, which ensured the stable control performance of ABS. Chereji et al [15] simulated the relative motion between the road (lower wheels) and the quarter car model (upper wheels), presented the performance of two sliding mode control algorithms based on Lyapunov's sliding mode controller and law of arrival-based sliding mode controller, and updated the design and applied it to the ABS.…”

Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation

“…A linear uncertainty vehicle model with steering rigidity uncertainty was built [25]. A function recursive fuzzy neural network uncertainty estimator [26] and a self-organizing function-link fuzzy cerebellar model articulation controller [27] were used to approximate the unknown nonlinear terms of vehicle dynamics during ABS control. Nonlinear adaptive backstepping control algorithms based on model parameter adaptive controller with slide mode observer [28] and on dynamic recursive radial basis function network uncertainty observer [29] were designed and used to observe the nonlinear uncertainty of permanent magnet direct-current generators.…”

Robust Backstepping Sliding Mode Control with L2-Gain Performance for Reference Input Wheel Slip Tracking of Vehicle

“…Pasillas-Lépine [19] adopted wheel deceleration logic-based switching and wheel dynamic model to design the five-phase anti-lock brake algorithm, and proved the existence and stability of limit cycles by the Poincaré map. Hsu [7] proposed an intelligent exponential sliding-mode control strategy for ABS, and a functional recurrent fuzzy neural network uncertainty estimator was designed to reduce the chattering of the exponential sliding-mode control strategy by approximating and compensating the unknown nolinear term of ABS dynamics on-line. Jing et al [8] presented a switched control strategy for the anti-lock brake system and then analyzed the stability condition of the closed-loop system by Lyapunov-based method in the Filippov framework.…”

“…Jing et al [8] presented a switched control strategy for the anti-lock brake system and then analyzed the stability condition of the closed-loop system by Lyapunov-based method in the Filippov framework. The proposed control strategy in [7][8][9]19] may only regulate the wheel slip at its optimum point to generate the maximum braking force. However, the continuous wheel slip tracking control is the basis of active safety control systems and intelligent driver assistance systems.…”