Ensure Vehicle Stability Research Articles

Vehicle Stability Control Based on Vehicle Motion State and Tire Force Estimation

The direct yaw moment control system is able to greatly enhance the vehicle stability when driving on challenging road surfaces. This paper introduces a direct control approach for managing the vehicle yaw moment, utilizing terminal sliding mode control and a Square Root Cubature Kalman Filter (SRCKF). Initially, the longitudinal and lateral forces acting on the vehicle's tires are estimated through a sliding mode observer. Subsequently, the SRCKF algorithm and the four-wheel tire force are employed to accurately estimate the yaw rate and sideslip angle. Based on these estimations, additional yaw moments are determined using the terminal sliding mode control algorithm, and a combined control of yaw rate and sideslip angle is achieved through a threshold method. A rule-based braking force distribution strategy is then implemented to ensure vehicle stability control. Simulation results demonstrate that the tire force estimations from the sliding mode observer and the vehicle yaw rate and sideslip angle estimations from the SRCKF algorithm closely align with the output results from Carsim, with an error margin of less than 15%. The braking force distribution strategy, based on the direct yaw moment control using the terminal sliding mode algorithm, effectively tracks the desired yaw rate and sideslip angle.

Research on dynamic constraint mechanism and motion control of intelligent vehicles based on preview distance optimization under complex road conditions

Aiming at the coupling and conflict issues between intelligent vehicle dynamics characteristics and path tracking system under complex road conditions, this paper investigates the dynamics constraint mechanism and motion control of intelligent vehicles, and proposes an intelligent vehicle lateral–vertical cooperative control method based on optimized preview distance. To begin with, an intelligent vehicle path tracking control system based on expected yaw velocity has been designed by establishing a three-degree-of-freedom dynamic model for intelligent vehicle. Then, we analyzed the mechanism by which changes in vehicle speed, road curvature, and preview distance affect the accuracy of vehicle path tracking and handling stability. Considering the “human-vehicle-road” system in intelligent transportation systems, critical values for collision and instability were set. Furthermore, we designed a proactive optimization method for preview distance under different working conditions, using an optimization algorithm to improve path tracking accuracy while ensuring vehicle stability, based on the lateral displacement deviation and lateral orientation deviation representing the accuracy of path tracking, as well as the lateral acceleration representing handling stability. Finally, hardware-in-the-loop platform test was conducted. The simulation and test results show that the optimized path tracking algorithm reduces lateral deviation to as low as 0.05 m, and the stability constraint control in the algorithm can be triggered promptly even under extreme conditions.

Active suspension control using novel HB3C optimized LQR controller for vibration suppression and ride comfort enhancement

This article addresses the optimization of a Vehicle Active Suspension System (VASS) through the application of a Linear Quadratic Regulator (LQR) controller. The primary objective is to enhance ride comfort and ensure vehicle stability by addressing the divergent needs of vibration control. The research identifies key issues in existing optimization algorithms, namely, the exploration stage inefficiency in Big Bang Big Crunch Optimization (B3C) and the slow convergence rate in Coyote Optimization (CO). To overcome these challenges, a novel hybrid algorithm, Hybrid Coyote optimization based Big Bang Big Crunch (HB3C), is proposed. The research objective is to optimize the LQR weighting matrices using the HB3C algorithm, aiming for improved ride comfort and vehicle safety. The problem statement involves the inadequacies of existing algorithms in addressing the exploration and convergence issues. The motivation lies in enhancing the efficiency of VASS through optimal control, leading to better ride comfort and safety. The methodology involves integrating CO within a loop with B3C to compute the optimum reduction rate for the algorithm. Since, B3C algorithm’s success is highly dependent on selecting the ideal reduction rate. This hybrid approach is then applied to optimize the existing LQR weighting matrices. The results are evaluated in terms of time domain and frequency domain response analysis, with a focus on ride comfort based on ISO 2631-1 standards. The study demonstrates a maximum reduction of approximately 74% achieved by the optimized HB3C-LQR controllers.

Structural Analysis of an Off-Road Vehicle Suspension System Using CATIA and ANSYS

This project delves into the structural analysis of an off-road vehicle suspension system, employing cutting-edge software tools such as CATIA and ANSYS. Off-road vehicles necessitate robust suspension systems capable of withstanding harsh terrain conditions while ensuring vehicle stability, manoeuvrability, and passenger comfort. The study focuses on evaluating the structural integrity, performance, and behaviour of the suspension system under diverse load conditions and terrain scenarios. This project focuses on conducting a comprehensive structural analysis of an off-road vehicle suspension system using advanced engineering software tools, specifically CATIA and ANSYS. CATIA is a powerful computer-aided design (CAD) software known for its capabilities in creating detailed 3D models, while ANSYS is a leading finite element analysis (FEA) software used for simulating structural behaviour and analyzing mechanical systems. Keywords: suspension system, CATIA v5, ANSYS, iterations, FEA.

АНАЛИЗ ВЛИЯНИЯ ОСНОВНЫХ ВНЕШНИХ СИЛ И РЕАКЦИЙ НА ВЕЛИЧИНУ БОКОВОГО УВОДА УПРАВЛЯЕМЫХ И НЕУПРАВЛЯЕМЫХ КОЛЕС ПРИ КРИВОЛИНЕЙНОМ ДВИЖЕНИИ ДВУХОСНОГО АВТОМОБИЛЯ

Pneumatic tires must have elasticity, strength, wear resistance and, taking vertical, tangential and lateral loads, must ensure vehicle stability when cornering. Optimization of their parameters and properties, often of a contradictory nature, is an urgent task, for the solution of which hypotheses were put forward based on the information and analytical research carried out and which made it possible to specify the purpose and objectives of subsequent research.

Design of lateral dynamic control objectives for multi-wheeled distributed drive electric vehicles

The lateral dynamic control system for electric vehicles aims to improve vehicle performance, making it critical to propose reasonable lateral dynamic control objectives. Therefore, in this study, a novel lateral dynamic control objectives is proposed for multi-wheeled distributed drive electric vehicles. Initially, utilizing the classical two-degrees-of-freedom (2DOF) vehicle model, two initial yaw rate references were established for handling characteristics of manoeuvrability and stability, respectively. The yaw rate references consider the vehicle velocity, steering wheel angle and road friction coefficient limitations. Considering the vital role of the sideslip angle in ensuring vehicle stability, a sophisticated weighting factor was developed based on a detailed analysis of the vehicle dynamics within the sideslip angle-sideslip angle rate phase plane. The weighting factor was determined based on the position of the vehicle state in the phase plane. This factor was adeptly applied to harmonise our dual control objectives, culminating in an integrated control aim that encapsulates the individual benefits of each. Finally, the lateral dynamic control objective was realised using a direct yaw moment control (DYC) system. The effectiveness of the proposed control objective was validated through hardware-in-the-loop (HIL) simulations.

A New Adaptive Cruise Control Considering Crash Avoidance for Intelligent Vehicle

To enhance adaptive cruise control (ACC) in avoiding crashes with arbitrary cut-in vehicles from different lanes, a hierarchical ACC considering crash avoidance is proposed in this paper. A new variable time to collision (TTC) step based on elliptical vehicle geometry is proposed to estimate the TTC with vehicles from different directions. The hierarchical structure involves an upper planner and a lower dynamic controller. A switching mechanism based on TTC is designed to select the upper planner mode to avoid collision and maintain tracking ability. Moreover, the lower dynamics controller applies a new sliding mode control to track the desired acceleration and utilize the maximal longitudinal tire force. A driver-in-the-loop (DiL) platform is established, and four scenarios are designed to verify both the effectiveness and real-time performance. The results indicate that the new structure tracks the desired acceleration and avoids crashing the vehicle from different directions while ensuring vehicle stability.

A Human-Like Shared Driving Strategy in Lane-Changing Scenario Using Cooperative LPV/MPC

Linear quadratic (LQ) game has been widely used in shared driving to resolve conflicts between drivers and driving automation systems (DAS). However, the system model in LQ game is assumed to be linear time-invariant, which does not consider the effect of changing longitudinal speed on the lateral control. A novel cooperative LPV/MPC approach is proposed, which characterizes the longitudinal and lateral coupled vehicle control in a dynamic game. The stability of the cooperative LPV/MPC system is analyzed in the presence of inconsistent targets of driver and DAS. Driver behavior is studied using 2,701 lane-changing cases extracted from naturalistic driving data (NDD). A human-like risk assessment method is achieved according to the driver behavior characteristics. Finally, a shared driving strategy in lane-changing scenarios is presented based on the cooperative LPV/MPC algorithm and the human-like risk assessment method. Simulation validations show that the shared driving strategy can ensure vehicle stability when longitudinal control, lateral control and dynamic game are performed simultaneously. In addition, the shared driving strategy can realize the driving weight gradual handover, which can help to better assist the driver in risk avoidance.

MILE: Multiobjective Integrated Model Predictive Adaptive Cruise Control for Intelligent Vehicle

Adaptive cruise control (ACC) systems currently face the challenge of balancing tracking performance and avoiding collisions with arbitrary cut-in vehicles from different lanes. The multi-objective ACC proposed in this paper is based on a novel integrated structure. The novel integrated ACC structure consists of an adaptive controller and the associated switching mechanism. The controller combines the upper and lower layers, which are common in today's hierarchical controllers. The switching mechanism is designed to switch between different modes to avoid collisions and maintain tracking capability in complex driving scenarios. Complex scenarios are designed to validate the integrated structure's effectiveness, real-time performance, and robustness, and a driver-in-the-loop (DiL) platform is established. The results indicate that the novel integrated structure is capable of tracking the preceding vehicle accurately while avoiding colliding with the surrounding vehicle from various directions, thereby ensuring vehicle stability under varying road adhesion and system uncertainties.

Optimizing Energy Harvesting: A Gain-Scheduled Braking System for Electric Vehicles with Enhanced State of Charge and Efficiency

Recycling braking energy is crucial in increasing the overall energy efficiency of an electric vehicle. Regenerative braking system (RBS) technology makes a significant contribution, but it is quite challenging to design an optimal braking force distribution while ensuring vehicle stability and battery health. In this study, a parallel-distribution braking system that transfers as much energy as possible from the wheel to the battery was investigated. An integrated braking force distribution with gain-scheduling super-twisting sliding mode control (GSTSMC) was proposed to capture the maximum kinetic energy during braking and convert it into electrical energy. Parallel friction and regenerative braking ratios dominate the design of the braking component, which is based on the speed of the vehicle. A GSTSMC was implemented and incorporated into the vehicle dynamics model developed in the ADVISOR environment. Simulation was utilized to rigorously validate the efficacy of the proposed control strategy, ensuring its potential to perform optimally in practical applications. Consideration was given to the vehicle’s slip ratio on dry asphalt to maintain vehicle stability. Simulation results were used to validate the performance of the proposed design in terms of the state of charge (SOC), transmitted energy, motor efficiency, battery temperature, and slip ratio. Based on the results, the proposed control strategy is capable of increasing the SOC value to 54%, overall efficiency to 25.98%, energy transmitted to 14.27%, and energy loss to 87 kJ while considering the vehicle’s speed-tracking ability, battery temperature, and stability.

Integrated Control of Steering and Braking for Path Tracking Using Multi-Point Linearized MPC

In this study, we propose an integrated braking and steering model predictive controller for stable and accurate path tracking. To perform model-based longitudinal control and lateral control at the same time, it is necessary to consider nonlinear characteristics caused by changing vehicle speed and nonlinear tire forces. This paper proposes a multipoint linearization method to minimize the linearization error, and a controller with good computational efficiency and accurate consideration of nonlinear vehicle behavior is also introduced. In addition, the proposed model predictive controller (MPC) actively utilizes the road friction limit constraint for each tire force to ensure vehicle stability. Through this, the proposed controller generates optimal braking and steering inputs for situations such as high-speed turns in which braking must be involved. Due to the proposed linearized model, the controller achieves a significant improvement in computational efficiency and good control performance similar to that of the nonlinear MPC. Comparison with other control methods and performance verification for various road conditions are performed through simulations, and the results show very efficient calculation while performing accurate path tracking.

Lateral Shared Sliding Mode Control for Lane Keeping Assist System in Steer-by-Wire Vehicles: Theory and Experiments

The lane-keeping assistance design for steer-by-wire road vehicles is a multi-objective control problem that addresses lane tracking, improvement of driver comfort and ensuring vehicle stability. The designed architecture must be sensitive to various driver behaviors, provide assistance to drivers whenever required and ensure a smooth transition of authority between manual and automated driving modes. In this work, we propose a novel shared lane-keeping controller-based on quasi-continuous high-order sliding mode control dealing with the above challenges. The parameters of the controller are characterized by an upper bound estimate of the lateral winds force and the road curvature which acts as disturbances. Using the sharing parameter dependent on the monitored driver attribute, the authority is smoothly transitioned between various modes while accounting for driver behaviors. Experimental results on the SHERPA vehicle simulator shows the real-time implementation feasibility. Extensive results and discussions are provided to demonstrate the effectiveness and the applicability of the proposed controller.

The Steering Actuator System to Improve Driving of Autonomous Vehicles based on Multi-Sensor Data Fusion

In autonomous vehicles, the control unit must be based on two main goals, first maintains the stability of the car second follows the desired path. All things considered, the controller's effectiveness is heavily dependent on the details of the steering system actuators. The necessary steering is set by a higher-order controller. The time delay of the steering actuator is one of the main features affecting the performance of the controller. While the artificial intelligence and artificial ethic are new apparatuses in autonomous vehicles but their ICs and electrical component are exposed to fusion. This paper primarily presents a more reliable system work during the fusion of multi-sensor information. We design the requirements of the steering system and the sureness of stability control in autonomous vehicles, also finding the suitable parameters for high-level control algorithms to compensate for time delay and ensure vehicle stability. The vehicle's steering angle response was obtained by testing the actuator of electric power steering (EPS) undergoing different speeds. In fact, using the identification of the system has been beneficial because obtaining the transfer function is easier than the actual methods which need the implementation of a mathematical model of the system. The system response of the Input-output has been defined via MATLAB. Full vehicle model simulation results indicate that the found adjustment parameter improves lane-tracking performance in a basic architecture by reducing oscillation and lateral error relative to other instances. The simplified steering system is the primary improvement brought by this effort.

Coordination Control of Maneuverability and Stability for Four-Wheel-Independent-Drive EV Considering Tire Sideslip

Four-wheel-independent-drive electric vehicle (EV) can generate the active yaw moment by the differential torque to change the steering characteristics such as oversteer or understeer. However, the utilization of the same actuators implies that the stability and flexibility will interfere with each other. This article proposes a coordination control strategy to simultaneously improve the low-speed flexibility and high-speed stability of the vehicle. First, the geometry of the vehicle steering and the steady-state steering characteristics under active yaw moments are analyzed. Subsequently, a gain scheduling controller is designed to adjust the magnification of the yaw rate based on the driver’s intention. Next, a feedforward and an enhanced sliding mode controller are applied to quickly track the target motion. Due to the overlapping nature of the actuators in the differential drive-assisted steering (DDAS) and vehicle stability control (VSC) system, the coordination strategy is developed based on the phase plane theory. Moreover, a torque distribution algorithm derived from the modified extreme value method with lesser calculation is proposed. Finally, simulations and experiments under relevant conditions are conducted to verify the effectiveness and the reliability of the designed control strategy. The results suggest that the proposed strategy can significantly improve maneuverability and ensure vehicle stability.

Integrated Lateral and Roll Stability Control of Multi-Actuated Vehicles Using Prioritization Model Predictive Control

This paper presents a prioritization model predictive control for integrated lateral and roll stability control of all-wheel-drive vehicles. The vehicle’s powertrain includes an electric motor and an open differential per axle, which enables generating a corrective yaw moment by front/rear torque shifting. In addition, the brakes can be used in differential mode to further enhance the corrective yaw moment when needed. To ensure vehicle stability, safety limits are defined on the vehicle’s yaw rate, sideslip, and roll motions, considering road angle effects. The controller prioritizes the control actions and objectives based on, respectively, their advantages and their importance, and then combines the priorities such that differential braking, which is defined as the low priority actuation, will not kick in unless the stability limits are violated. The performance of the designed controller is thoroughly evaluated through numerical and full vehicle experimental studies in different driving scenarios on flat and non-flat roads.

Adaptive Coordinated Path Tracking Control Strategy for Autonomous Vehicles with Direct Yaw Moment Control

It is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions. In this study, an adaptive path tracking control strategy that coordinates active front wheel steering and direct yaw moment is proposed based on model predictive control algorithm. The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the path tracking system prediction model. To adaptively adjust the priorities of path tracking accuracy and vehicle stability, an adaptive strategy based on fuzzy rules is applied to change the weight coefficients in the cost function. An adaptive control strategy for coordinating active front steering and direct yaw moment is proposed to improve the path tracking accuracy under high-speed and large-curvature conditions. To ensure vehicle stability, the sideslip angle, yaw rate and zero moment methods are used to construct optimization constraints based on the model predictive control frame. It is verified through simulation experiments that the proposed adaptive coordinated control strategy can improve the path tracking accuracy and ensure vehicle stability under high-speed and large-curvature conditions.

Switched model predictive controller for path tracking of autonomous vehicle considering rollover stability

Lateral stability and rollover stability are two most crucial concerns for path tracking of an autonomous vehicle under handling limit conditions. To improve path tracking accuracy and ensure vehicle stability under handling limit conditions, this paper proposes a force-driven switched MPC path tracking control strategy that coordinates active front-wheel steering with an external yaw moment. Firstly, a linear time-varying tire model is modelled using the affine approximate linearisation method to describe rear tire dymamics. And vehicle dynamics is modelled to predict vehicle states in the prediction horizon. Then, a force-driven switched MPC control strategy is proposed based on the system prediction model. The vehicle lateral stability and rollover stability are restricted using maximum tire lateral force and the zero-point moment method, respectively. Finally, according to the simulation results under normal speed and limiting speed conditions, a force-driven switched MPC control strategy can improve control accuracy and ensure vehicle stability.

Incorporated vehicle lateral control strategy for stability and enhanced energy saving in distributed drive hybrid bus

Vehicle stability and energy efficiency are important considerations in vehicle engineering. In this context, the current paper presents an energy saving strategy for hybrid electric vehicles that incorporates vehicle lateral dynamic control in conjunction with energy efficiency. To this end, we first model the nonlinear vehicle lateral dynamics of a hybrid electric bus via a Takagi–Sugeno approach and combine this model with an H_{\\infty } state-feedback controller via parallel distributed compensation. The controller matrices are obtained using linear matrix inequalities through an optimal energy-to-energy performance norm of the nonlinear vehicle model. Second, we propose a reference side-slip angle generating method and a set of tire force distribution rules, which under the premise of ensuring vehicle stability, minimize the overall energy consumption of the vehicle. Finally, we put forward a new speed prediction method based on vehicle lateral dynamics for hybrid electric vehicle energy saving. Human-in-the-loop simulated driving experiments are conducted where the bus performs lane-changing maneuvers with enhanced control properties under various driving conditions, demonstrating the reliability of the proposed energy-saving performance measures.

A coupled force predictive control of vehicle stability using front/rear torque allocation with experimental verification

This paper investigates the handling control and stability of an all-wheel-drive vehicle whose axles are individually equipped with an electric motor connected to an open differential. This could offer a potential configuration for the mass production of electric all-wheel-drive vehicles because of reduced cost and complexity. Although there is no torque vectoring or direct yaw moment control in this configuration, considerable handling improvement can be achieved by optimised front/rear torque distribution due to the longitudinal and lateral tire force coupling. In this study, a model predictive control design is presented with a coupled force prediction model for vehicle handling dynamics. The controller optimises the front/rear torque allocation to track the desired handling response and ensure vehicle stability. This study also compensates for actuator delay by incorporating the actuator dynamics into the control design. The performance of the proposed controller is evaluated through software simulations and experimental tests conducted on an electric all-wheel-drive Chevrolet Equinox.

Integrated nonlinear model predictive control for automated driving

This work presents a Nonlinear Model Predictive Control (NMPC) scheme to perform evasive maneuvers and avoid rear-end collisions. Rear-end collisions are among the most common road fatalities. To reduce the risk of collision, it is necessary for the controller to react as quickly as possible and exploit the full vehicle maneuverability (i.e., combined control of longitudinal and lateral dynamics). The proposed design relies on the simultaneous use of steering and braking actions to track the desired reference path and avoid collisions with the preceding vehicle. A planar vehicle model was used to describe the vehicle dynamics. In addition, the dynamics of the brake system were included in the NMPC prediction model. Furthermore, the controller incorporates constraints to ensure vehicle stability and account for actuator limitations. In this respect, the constraints were defined on Kamm circle and Ideal Brake Torque Distribution (IBD) logic for optimal tire force and brake torque distribution. To evaluate the design, the performance of the proposed NMPC was compared with two ”more classical” MPC designs that rely on: (i) a linear bicycle model, and (ii) a nonlinear bicycle model. The performance of these three controller designs was evaluated in simulation (using a high-fidelity vehicle simulator) via relevant KPIs, such as reference tracking Root Mean Square (RMS) error, controller’s rise/settling time, and Distance to Collision (i.e., the lateral distance by which collision was avoided safely). Different single-lane-change maneuvers were tested and the behavior of the controllers was evaluated in the presence of lateral wind disturbances, road friction variation, and maneuver aggressiveness.