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This paper presents an integrated chassis control of 4WD(Four wheel drive), ESC(Electronic stability control), and ECS(Electronic controlled suspension) for limit handling. The proposed algorithm consists of three layers : 1) Supervisor, which determine target yaw rate and target velocity from steering wheel angle and acceleration pedal, respectively, 2) Upper level controller, which calculate generalized force to track target values in the manner of sliding mode control method, 3) Lower level controller, which optimally allocates generalized force to actuators. In this study, to achieve limit handling of vehicle, the novel cost function is proposed. The main concept of the cost function is kept the tire stable by monitoring tire saturation with slip information. The proposed algorithm is validated via Matlab/Carsim co-simulation. Compared to base and ESC/4WD module, the proposed algorithm shows stable performance at the limit. The results show that with ESC/4WD modules, the performance at the limit is enhanced, but the yaw rate is oscillated. ECS module can reduce yaw rate oscillation by allocating vertical force.
This paper describes a yaw stability control algorithm of 4WD vehicles based on model predictive torque vectoring with physical constraints. A vehicle planar model based predictive rear and all-wheel torque vectoring algorithms were developed for 4WD vehicles by considering predictive states and driver's steering wheel angle. The physical constraints applied to the model predictive control consist of three types: limitation on magnitude of tire force, change rate of tire force, and output torque of transfer case. Two types of torque vectoring algorithms, rear-wheel and all-wheel, were constructed for comparative analysis. The steady state yaw rate was derived and applied as a desired value for yaw stability of the vehicle. The algorithm was constructed in a MATLAB/Simulink environment and the performance evaluation was conducted under various test scenarios, such as step steering and double lane change, using the CarSim software. The evaluation results of the predictive torque vectoring showed sound performance based on the prediction of states and driver's steering angle.
This paper presents a vehicle motion predictor for an autonomous racing system to assure safety when driving in limit handling situation. The proposed algorithm consists of two sequential parts; (1) Tire model identifier (2) Prediction of vehicle state. In the tire model identifier, parameters of a nonlinear tire model are identified through nonlinear optimization. In the prediction of vehicle sate, future vehicle states are calculated through numerical integration of a 2- DOF bicycle model that includes previously identified tire model. Moreover, assuming normal distribution of prediction error of each state, the probability of lateral instability and track escape of the future can be calculated. The proposed algorithm has been validated through computer simulations. The results show that the algorithm well predicts future vehicle states.
This paper presents visual validation of drift controllers for steady-state cornering using phase portrait to show the stability of steady-state drifting of a rear wheel drive vehicle. Phase portraits are drawn to display the change in vehicle states based on the time derivative of states at each phase coordinate. Phase portraits of bicycle model without a controller show the existence and vehicle state of an unstable drift equilibrium point where the vehicle states are not sustained with the lapse of the time. With the activation of steering angle or front tire lateral force controller, phase portrait reveals the existence of a stable drift equilibrium point. Successful implementation of the controllers can be confirmed through the convergence of trajectories to the stable drift equilibria.
This paper presents the lateral stability criteria for integrated chassis control. To determine the intervention timing of chassis control system, the lateral stability criteria is needed. The proposed lateral stability criteria is based on velocity-yawrate gain domain to determine whether vehicle is stable. If the yawrate gain violates the proposed criteria, the stability of the vehicle is considered as unstable. Characteristic velocity and critical velocity are employed to distinguish lateral stability criteria. The inside of the two boundaries is stable and the outside is unstable. If yawrate gain of vehicle violates the lateral stability criteria, the chassis control begin to intervene. To validate the lateral stability criteria, both computer simulations and vehicle test are conducted with respect to circular turn scenario. The proposed lateral stability criteria makes it possible to reduce intervention of chassis control system.
This paper presents an optimal performance of rear wheel steering vehicle for maneuverability. The maneuverability of vehicle is evaluated in terms of yaw rate, body slip angle and driver input. The maneuverability of vehicle can be improved by rear wheel steering system. To obtain optimal performance of rear wheel steering vehicle, the optimal control history is designed. The high dimensional trajectory optimization problem is solved by formulating a quadratic program considering rear wheel steer input. To evaluate handling performance 7 degree-of-freedom vehicle model with actuation sub-models is designed. A step steer test is conducted to evaluate rear wheel steering vehicle. A response time, a TB factor, overshoot, and yaw rate gain are investigated through objective criteria, assessment webs. The handling performance of vehicle is evaluated via computer simulations. It has been shown from simulation studies that optimal controlled rear wheel steering vehicle provides improved performance compared to others.