On the Experimental Analysis of Integral Sliding Modes for Yaw Rate and Sideslip Control of an Electric Vehicle with Multiple Motors

With the advent of electric vehicles with multiple motors, the steady-state and transient cornering responses can be designed and implemented through the continuous torque control of the individual wheels, i.e., torque-vectoring or direct yaw moment control. The literature includes several papers on sliding mode control theory for torque-vectoring, but the experimental investigation is so far limited. More importantly, to the knowledge of the authors, the experimental comparison of direct yaw moment control based on sliding modes and typical controllers used for stability control in production vehicles is missing. This paper aims to reduce this gap by presenting and analyzing an integral sliding mode controller for concurrent yaw rate and sideslip control. A new driving mode, the Enhanced Sport mode, is proposed, inducing sustained high values of sideslip angle, which can be limited to a specified threshold. The system is experimentally assessed on a four-wheel-drive electric vehicle. The performance of the integral sliding mode controller is compared with that of a linear quadratic regulator during step steer tests. The results show that the integral sliding mode controller significantly enhances the tracking performance and yaw damping compared to the more conventional linear quadratic regulator based on an augmented singletrack vehicle model formulation. © 2018, The Korean Society of Automotive Engineers and Springer-Verlag GmbH Germany, part of Springer Natur

Modular design of artificial potential field and nonlinear model predictive control for a vehicle collision avoidance system with move blocking strategy

The collision avoidance (CA) system is a pivotal part of the autonomous vehicle. Ability to navigate the vehicle in various hazardous scenarios demands reliable actuator interventions. In a complex CA scenario, the increased nonlinearity requires a dependable control strategy. For example, during collisions with a sudden appearing obstacle (i.e. crossing pedestrian, vehicle), the abrupt increment of vehicle longitudinal and lateral forces summation during the CA maneuver demands a system with the ability to handle coupled nonlinear dynamics. Failure to address the aforementioned issues will result in collisions and near-miss incidents. Thus, to solve these issues, a nonlinear model predictive control (NMPC)-based path tracking strategy is proposed as the automated motion guidance for the host vehicle CA architecture. The system is integrated with the artificial potential field (APF) as the motion planning strategy. In a hazardous scenario, APF measures the collision risks and formulates the desired yaw rate and deceleration metrics for the path replanning. APF ensures an optimal replanned trajectory by including the vehicle dynamics into its optimization formulation. NMPC then acts as the coupled path and speed tracking controller to enable vehicle navigation. To accommodate vehicle comfort during the avoidance, NMPC is constrained. Due to its complexity as a nonlinear controller, NMPC can be time-consuming. Therefore, a move blocking strategy is assimilated within the architecture to decrease the system’s computational burden. The modular nature of the architecture allows each strategy to be tuned and developed independently without affecting each others’ performance. The system’s tracking performance is analyzed by computational simulations with several CA scenarios (crossing pedestrian, parked bus, and sudden appearing moving vehicle at an intersection). NMPC tracking performance is compared to the nominal MPC and linear controllers. The effect of move blocking strategies on NMPC performance are analyzed, and the results are compared in terms of mean squared error values. The inclusion of nonlinear tracking controllers in the architecture is shown to provide reliable CA actions in various hazardous scenarios. The work is important for the development of a reliable controller strategy for multi-scenario CA of the fully autonomous vehicle

Piecewise trajectory replanner for highway collision avoidance systems with safe-distance based threat assessment strategy and nonlinear model predictive control

This paper proposes an emergency Trajectory Replanner (TR) for collision avoidance (CA) which works based on a Safe-Distance Based Threat Assessment Strategy (SDTA). The contribution of this work is the design of a piecewise-kinematic based TR, where it replans the path by avoiding the invisible rectangular region created by SDTA. The TR performance is measured by assessing its ability to yield a maneuverable path for lane change and lane keeping navigations of the host vehicle. The reliability of the TR is evaluated in multi-scenario computational simulations. In addition, the TR is expected to provide a reliable replanned path during the increased nonlinearity of high-speed collisions. For this reason, Nonlinear Model Predictive Control (NMPC) is adopted into the design to track the replanned trajectory via an active front steering and braking actuations. For path tracking strategy, comparisons with benchmark controllers are done to analyze NMPC’s reliability as multi-actuators nonlinear controller of the architecture to the CA performance in high-speed scenario. To reduce the complexity of the NMPC formulation, Move Blocking strategy is incorporated into the control design. Results show that the CA system performed well in emergency situations, where the vehicle successfully replanned the obstacle avoidance trajectory, produced dependable lane change and lane keeping navigations, and at the same time no side-collision with the obstacle’s edges occurred. Moreover, the multi-actuators and nonlinear features of NMPC as the PT strategy gave a better tracking performance in high-speed CA scenario. Assimilation of Move Blocking strategy into NMPC formulation lessened the computational burden of NMPC. The system is proven to provide reliable replanned trajectories and preventing multi-scenario collision risks while maintaining the safe distance and time constraints