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Dynamic game theory brings together different features that are keys to many situations in control design: optimization behavior, the presence of multiple agents/players, enduring consequences of decisions and robustness with respect to variability in the environment, etc. In the presented methodology, vehicle stability is represented by a cooperative dynamic/difference game such that its two agents (players), namely, the driver and the direct yaw controller (DYC), are working together to provide more stability to the vehicle system. While the driver provides the steering wheel control, the DYC control algorithm is obtained by the Nash game theory to ensure optimal performance as well as robustness to disturbances. The common two-degree of freedom (DOF) vehicle handling performance model is put into discrete form to develop the game equations of motion.

Vehicle stability is maintained by proper interactions between the driver and vehicle stability control system. While driver describes the desired target path by commanding steering angle and acceleration/deceleration rates, vehicle stability controller tends to stabilize higher dynamics of the vehicle by correcting longitudinal, lateral, and roll accelerations. In this paper, a finite-horizon optimal solution to vehicle stability control is introduced in the presence of driver's dynamical decision making structure. The proposed concept is inspired by Nash strategy for exactly known systems with more than two players, in which driver, commanding steering wheel angle, and vehicle stability controller, applying compensated yaw moment through differential braking strategy, are defined as the dynamic players of the 2-player differential linear quadratic game.

The increasing use and implementation of yaw and roll stability control in heavy trucks has contributed to an increased level of safety for truck drivers and other motorists. It has been shown that the combination of the stability control systems with a predictive model-based stability index can dramatically improve the truck stability and hence road safety. In this respect the authors introduced a new Total Safety Margin (TSM) using a fuzzy logic-based stability index. That methodology utilized a smoothed step and provided acceptable results; however, continuing development has shown that a right sigmoid membership function distribution would provide more complete coverage of the fuzzy space. Compared to the more common triangular membership function which is discontinuous when the membership grade equals one, sigmoid functions facilitate obtaining smooth, continuously differentiable surfaces of a fuzzy model.

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.

Dynamic “Game Theory” brings together different features that are keys to many situations in control design: optimization behavior, the presence of multiple agents/players, enduring consequences of decisions and robustness with respect to variability in the environment, etc. In previous studies, it was shown that vehicle stability can be represented by a cooperative dynamic/difference game such that its two agents (players), namely, the driver and the vehicle stability controller (VSC), are working together to provide more stability to the vehicle system. While the driver provides the steering wheel control, the VSC command is obtained by the Nash game theory to ensure optimal performance as well as robustness to disturbances. The common two-degree of freedom (DOF) vehicle handling performance model is put into discrete form to develop the game equations of motion. This study focus on the uncertainty in the inputs, and more specifically, the driver's steering input.

This paper addresses a common issue in producing desktop simulations for production environments in which the most current version of the algorithm is available only in the form of the production code. The part of the system we are interested in is often called performance software (for example, slip control systems, transmission control, and engine control). From a control algorithm design perspective the ideal environment is a reference model in a high level language, which supports code generation. The complete environment would support templatized code generation so that the fixed-point code for the embedded controller can be generated directly. Many organizations have a significant investment in legacy C code, and the cost of remodeling the entire system may be unacceptably high (at least in the short term). In such an environment simulation tools are often underused because of the impression that the entire system must be remodeled.

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.