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Various systems have been introduced recently in the automotive industry to improve the safety, ride and handling qualities of passenger vehicles, such as anti-lock braking system (ABS), active suspension system, four wheel steering system, traction control system, etc. Although each system has been shown to impose positive effects on the performance of a vehicle, the benefits of integrating various systems is yet to be determined. A feasibility study was conducted of a new non-linear control law for integration of anti-lock braking system and active suspension system. The control law is based on the use of a candidate Lyapunov function. Lyapunov stability theorem is applied to synthesize the control law and the adaptation law necessary to estimate the unknown parameters of the vehicle/road system. The proposed MIMO non-linear control strategy can maintain desired values of various variables while estimating the unknown parameters of the system.

In this paper a vehicle model including the steering, the tire and the suspension systems is presented. Assuming one out-of-balance wheel, the response of the system is obtained and the vibration characteristics of the steering system are analyzed. Based on the analysis conducted, two of the steering system parameters are selected and optimized. This is achieved by performing a sensitivity analysis with respect to various system parameters.

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.

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.

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.

A vehicle's response is predominately defined by the tire characteristics as they constitute the only contact between the vehicle and the road; and the surface friction condition is the primary attribute that determines these characteristics. The friction coefficient is not directly measurable through any sensor attachments in production-line vehicles. Therefore, current chassis control systems make use of various estimation methods to approximate a value. However a significant challenge is that these schemes require a certain level of perturbation (i.e. excitation by means of braking or traction) from the initial conditions to converge to the expected values; which might not be the case all the time during a regular drive.

The road profile has been shown to have significant effects on various vehicle conditions including ride, handling, fatigue or even energy efficiency; as a result it has become a variable of interest in the design and control of numerous vehicle parts. In this study, an integrated state estimation algorithm is proposed that can provide continuous information on road elevation and profile variations, primarily to be used in active suspension controls. A novel tire instrumentation technology (smart tire) is adopted together with a sensor couple of wheel attached accelerometer and suspension deflection sensor as observer inputs. The algorithm utilizes an adaptive Kalman filter (AKF) structure that provides the sprung and unsprung mass displacements to a sliding-mode differentiator, which then yields to the estimation of road elevations and the corresponding road profile along with the quarter car states.

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.