Integrated control of braking and steering systems to improve vehicle stability based on optimal wheel slip ratio estimation

This paper presents an integrated control of longitudinal, yaw and lateral vehicle dynamics using active front steering (AFS) and active braking systems. The designed active braking system based on sliding mode controller includes two kinds of working modes. It is activated as an anti-locked brake system (ABS) and an electronic stability control (ESC) with active differential braking strategy (DBS) for hard braking in straight line and unstable situation in cornering, respectively. The AFS is proposed based on fuzzy controller. In addition, a nonlinear estimator utilizing unscented Kalman filter (UKF) is applied to estimate the vehicle dynamics variables that cannot be measured in a cost-efficient way such as wheel slip, yaw rate, longitudinal and lateral velocities. According to the estimated values and Dugoff tire model, the tire-road friction coefficients are calculated. As the ABS performance for shortening the stopping distance depends on the optimal tire slip ratios, an adaptive neuro-fuzzy inference system (ANFIS) is proposed to obtain their optimum values. The tire-road friction coefficients, longitudinal velocity, and the vertical wheel load are considered as the ANFIS inputs. In the simulation part, the hard-braking action in straight line on the roads with various friction coefficients and split-μ roads is investigated. The results demonstrate high precision of the estimation of road friction coefficient and optimum wheel slip ratio, greatly reduction of the distance and stopping time, as well as improvement of the lateral and yaw stability in comparison with the vehicle without estimator.