Search Results (9)

To improve the handling and stability of four-wheel independent drive electric vehicles (FWID EVs), this paper introduces a hierarchical architecture lateral stability control system. The upper-level controller is responsible for generating the additional yaw moment required by the vehicle. This includes a control strategy based on feedforward control and a Linear Quadratic Regulator (LQR) for handling assistance control, an LQR-based stability control, a PID controller-based speed-following control, and a stability assessment method. The lower-level controller uses Quadratic Programming (QP) to optimally distribute the additional yaw moment to the four wheels. A “normalized” method was proposed to determine vehicle stability. After comparing it with the existing double-line method, diamond method, and curved boundary method through the open-loop Sine with Dwell test and the closed-loop Double Lane Change (DLC)test simulation, the results demonstrate that this method is more sensitive and accurate in determining vehicle stability, significantly enhancing vehicle handling and stability. Full article

Direct yaw moment control (DYC) can effectively improve the yaw stability of four-wheel distributed drive electric vehicles (4W-DDEVs) under extreme conditions, which has become an indispensable part of active safety control for 4W-DDEVs. This study proposes a novel hierarchical DYC architecture for 4W-DDEVs to enhance vehicle stability during ever-changing road conditions. Firstly, a vehicle dynamics model is established, including a two-degree-of-freedom (2DOF) vehicle model for calculating the desired yaw rate and sideslip angle as the control target of the upper layer controller, a DDEV model composed of a seven-degree-of-freedom (7DOF) vehicle model, a tire model, a motor model and a driver model. Secondly, a hierarchical DYC is designed combining the upper layer yaw moment calculation and low layer torque distribution. Specifically, based on Matlab/Simulink, improved linear quadratic regulator (LQR) with weight matrix optimization based on inertia weight cosine-adjustment particle swarm optimization (IWCPSO) is employed to compute the required additional yaw moment in the upper-layer controller, while quadratic programming (QP) is used to allocate four motors’ torque with the optimization objective of minimizing the tire utilization rate. Finally, a comparative test with double-lane-change and sinusoidal conditions under a low and high adhesion road surface is conducted on Carsim and Matlab/Simulink joint simulation platform. With IWCPSO-LQR under double-lane-change (DLC) condition on a low adhesion road surface, the yaw rate and sideslip angle of the DDEV exhibits improvements of 95.2%, 96.8% in the integral sum of errors, 94.9%, 95.1% in the root mean squared error, and 78.8%, 98.5% in the peak value compared to those without control. Simulation results indicate the proposed hierarchical control method has a remarkable control effect on the yaw rate and sideslip angle, which effectively strengthens the driving stability of 4W-DDEVs. Full article

Intelligentization is the development trend of the future automobile industry. Intelligentization requires that the dynamic control of the vehicle can complete the trajectory tracking according to the trajectory output of the decision planning the driving state of the vehicle and ensure the driving safety and stability of the vehicle. However, trajectory limit planning and harsh road conditions caused by emergencies will increase the difficulty of trajectory tracking and stability control of unmanned vehicles. In view of the above problems, this paper studies the trajectory tracking and stability control of distributed drive unmanned vehicles. This paper applies a hierarchical control framework. Firstly, in the upper controller, an adaptive prediction time linear quadratic regulator (APT LQR) path following algorithm is proposed to acquire the desired front-wheel-steering angle considering the dynamic stability performance of the tires. The lateral stability of the DDAUV is determined based on the phase plane, and the sliding surface, in the improved sliding mode control (SMC), is further dynamically adjusted to obtain the desired additional yaw moment for coordinating the path following and lateral stability. Then, in the lower controller, considering the slip and the working load of four tires, a comprehensive cost function is established to reasonably distribute the driving torque of four in-wheel motors (IWMs) for producing the desired additional yaw moment. Finally, the proposed control algorithm is verified by the hardware-in-the-loop (HIL) experiment platform. The results show the path following and lateral stability can be coordinated effectively under different driving conditions. Full article

To enhance the safety and stability of lane change maneuvers for autonomous vehicles in adverse weather conditions, this paper proposes a quadratic programming−based trajectory planning algorithm for lane changing in rainy weather. Initially, in order to mitigate the risk of potential collisions on wet and slippery road surfaces, we incorporate the concept of road adhesion coefficients and delayed reaction time to refine the establishment of the minimum safety distance. This augmentation establishes constraints on lane change safety distances and delineates the boundaries of viable lane change domains within inclement weather contexts. Subsequently, adopting a hierarchical trajectory planning framework, we incorporate visibility cost functions and safety distance constraints during dynamic programming sampling to ensure the safety of vehicle operation. Furthermore, the vehicle lane change sideslip phenomenon is considered, and the optimal lane change trajectory is obtained based on the quadratic programming algorithm by introducing the maneuverability objective function. In conclusion, to verify the effectiveness of the algorithm, lateral linear quadratic regulator (LQR) and longitudinal double proportional−integral−derivative (DPID) controllers are designed for trajectory tracking. The results demonstrate the algorithm’s capability to produce continuous, stable, and collision−free trajectories. Moreover, the lateral acceleration varies within the range of ±1.5 m/s², the center of mass lateral deflection angle varies within the range of ±0.15°, and the yaw rate remains within the ±0.1°/s range. Full article

For multi-axle distributed drive (MADD) vehicles, the complexity of the longitudinal dynamics control system increases with the number of driven wheels, which presents a huge challenge to control the multi-motor drive vehicle with more than four wheels. To reduce the control system complexity, this paper proposes a coordinated slip control algorithm using the hierarchical linear quadratic regulator (HLQR) scheme for a 12 × 12 MADD vehicle. The 12-wheel driving system is decoupled based on the wheel load and simplified to a double local subsystem. First, the 12 × 12 MADD vehicle dynamics model is established. Then, the optimal slip ratio is obtained on the basis of the road friction coefficient estimation through a fuzzy control algorithm when the wheel slips. Afterwards, the wheel slip ratio is controlled based on the HLQR program for anti-slip regulation. Furthermore, the driving torque control allocation based on quadratic programming (QR) is coordinated with the anti-slip control. Simulink results show that the proposed coordinated slip control based on HLQR can improve slip control accuracy by more than 30% and greatly reduce the calculation load. The torque control allocation is also limited by the slip control results to ensure wheel dynamic stability and smoothly satisfy the driver’s demand. Full article

Most of the existing research in the field of autonomous vehicles (AVs) addresses decision making, planning and control as separate factors which may affect AV performance in complex driving environments. A hierarchical framework is proposed in this paper to address the problem mentioned above in environments with multiple lanes and surrounding vehicles. Firstly, high-level decision making is implemented by a finite-state machine (FSM), according to the relative relationship between the ego vehicle (EV) and the surrounding vehicles. After the decision is made, a cluster of quintic polynomial equations is established to generate the path connecting the initial position to the candidate target positions, according to the traffic states of the EV and the target vehicle. The optimal path is selected from the cluster, based on the quadratic programming (QP) framework. Then, the speed profile is generated, based on the longitudinal displacement–time graph (S–T graph), considering the vehicle motion state constraints and collision avoidance. The smoothed speed profile is created through another QP formulation, in the space created by the dynamic-programming (DP) method. Finally, the planned path and speed profile are combined and sent to the lower level control module, which consists of the linear quadratic regulator (LQR) for lateral trajectory tracking control, and a double PID controller for longitudinal control. The performance of the proposed framework was validated in a co-simulation environment using PreScan, MATLAB/Simulink and CarSim, and the results demonstrate that the proposed framework is capable of addressing most ordinary scenarios on a structured road, with reasonable decisions and controlling abilities. Full article

This paper presents a lateral and longitudinal coupling controller for a trajectory-tracking control system. The proposed controller can simultaneously minimize lateral tracking deviation while tracking the desired trajectory and vehicle speed. Firstly, we propose a hierarchical control structure composed of upper and lower-level controllers. In the upper-level controller, the linear quadratic regulator (LQR) controller is designed to compute the desired front wheel steering angle for minimizing the lateral tracking deviation, and the model-predictive controller is developed to compute the desired acceleration for maintaining the planed vehicle speed. The lower-level controller enables the achievement of the desired steering angle and acceleration via the corresponding component devices. Furthermore, an observer based on the Extended Kalman Filter (EKF) is proposed to update the vehicle driving states, which are sensitive to the trajectory-tracking control and difficult to measure directly using the existing vehicle sensors. Finally, the Co-simulation (CarSim-MATLAB/Simulink) results demonstrate that the proposed coupling controller is able to robustly realize the trajectory tracking control and can effectively reduce the lateral tracking error. Full article

This paper deals with longitudinal motion control of electric vehicles (EVs) driven by in-wheel-motors (IWMs). It shows that the IWM-EV is fundamentally a multi-agent system with physical interaction. Three ways to model the IWM-EV are proposed, and each is applicable to certain control objectives. Firstly, a nonlinear model with hierarchical structure is established, and it can be used for passivity-based motion control. Secondly, a linearized model with rank-1 interconnection matrix is presented for stability analysis. Thirdly, a time-varying state-space model is proposed for optimal control using linear quadratic regulator (LQR). The proposed modellings contribute the new understanding of IWM-EV dynamics from the view point of multi-agent-system theory. By choosing the suitable control theory for each model, the complexity level of system design is maintained constant, no matter what the number of IWMs installed to the vehicle body. The effectiveness of three models and their design approaches are discussed by several examples with Matlab/Carsim co-simulator. Full article

Vision-based sensors are widely used in lateral control of autonomous vehicles, but the large computational cost of the visual algorithms often induces uneven time delays. In this paper, a hierarchical vision-based lateral control scheme is proposed, where the upper controller is designed by robust H_∞-based linear quadratic regulator (LQR) algorithm to compensate sensor-induced delays, and the lower controller is based on logic threshold method, in order to achieve strong convergence of the steering angle. Firstly, the vehicle lateral model is built, and the nonlinear uncertainties induced by time delays are linearized with Taylor expansion. Secondly, the state space of the system is augmented to describe such uncertainties with polytopic inclusions, which is controlled by an H_∞-based LQR controller with a low cost of online computation. Then, a lower controller is designed for the control of the steering motor. According to the results of the vehicle experiment as well as the hardware-in-the-loop (HIL) experiment, the proposed control scheme shows good performance in vehicle’s lateral control task, and exhibits better robustness compared with a conventional LQR controller. The proposed control scheme provides a feasible solution for the lateral control of autonomous driving. Full article