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In this paper, a method for directional control of articulated heavy vehicles is proposed. The tractor yaw rate, the tractor lateral velocity and the articulation angle are selected as the control variables. The desired values of these states are defined in such way to improve the maneuverability and the stability of the articulated vehicle. A linear quadratic regulator controller is designed based on the linear model of the articulated vehicle to make the control variables follow the desired responses. Furthermore, a nonlinear 14 Degrees of freedom (DoF) model is developed to evaluate the proposed control method. The significant effect of the proposed method on improving the directional behavior of the articulated vehicle is proved through the simulations of the high speed lane change maneuver on a slippery road.

In this paper, an integrated vehicle dynamics control system is designed to improve vehicle yaw stability by coordinating control of Active Front Steering (AFS) and Direct Yaw-moment Control (DYC) based on a new concept. The control system has a hierarchical structure and consists of two controlling layers. A fuzzy logic controller is used in the upper layer (Yaw Rate Controller) to keep the yaw rate in its desired value. The yaw rate error and its rate of change are applied to upper controlling layer as inputs, where the direct yaw moment control signal and the steering angle correction of the front wheels are built as the outputs. In the lower layer (Fuzzy Integrator), a fuzzy logic controller is designed based on the working region of the lateral tire forces to determine percentage of usage of upper layer controlling inputs.

A novel driver-assist stability system for all-wheel-drive Electric Vehicles is introduced. The system helps drivers maintain control in the event of a driving emergency, including heavy braking or obstacle avoidance. The system comprises a Fuzzy logic that independently controls wheel torques to prevent vehicle spin. A neural network is trained to generate the required yaw rate reference. Another Fuzzy system for each wheel controls the slip to ensure vehicle stability and safety. Furthermore a new vehicle speed estimator is employed for slip estimation. The intrinsic robustness of fuzzy controllers allows the system to operate in different road conditions successfully. Moreover, the ease to implement fuzzy controllers gives a potential for vehicle stability enhancement.

This paper presents a novel method for motion control and driver stability assist system of an electric vehicle with independently driven wheels. A combination of two controllers for yaw rate and wheel slip is used to improve yaw stability of a two-wheel-drive electric vehicle. Vehicle speed is estimated using a multi-sensor data fusion method. To overcome the uncertainties in tire-road friction, a Fuzzy logic approach is employed for both controllers. The effectiveness of the proposed control method is evaluated by simulation.

The stability of a four motor-wheel drive electric vehicle is improved by independent control of wheel torques. An innovative Fuzzy Direct Yaw Control method together with a novel wheel slip controller is used to enhance the vehicle stability and safety. Also a new speed estimator is presented in this paper, which is used for slip estimation. The intrinsic robustness of fuzzy controllers allows the system to operate in different road conditions successfully. Moreover, the ease to implement fuzzy controllers gives a practical solution for vehicle stability enhancement.