CarSim-Simulink Co-simulation Research Articles

Intelligent vehicle lateral control strategy research based on feedforward + predictive LQR algorithm with GA optimisation and PID compensation

Targeting the lateral motion control problem in the intelligent vehicle autopilot structural system, this paper proposes a feedforward + predictive LQR algorithm for lateral motion control based on Genetic Algorithm (GA) parameter optimisation and PID steering angle compensation. Firstly, based on the vehicle dynamics tracking error model, the intelligent vehicle LQR lateral motion controller as well as the feedforward controller are designed, and upon which the predictive controller is added to eliminate the system lag.Subsequently, exploiting the advantage that the PID algorithm is not model-based, a PID steering angle compensation controller that can directly control and correct the lateral error is designed. Second, a LQR controller based on path tracking deviation is designed by using the parameter rectification method of genetic algorithm (GA), which optimizes the control parameters of the lateral motion controller and improves the adaptivity of the control accuracy. Finally, Based on the Carsim-Simulink co-simulation platform, the simulation validation and analysis of double lane change (DLC) test and circular condition test (CCT) are carried out, and the results indicate that compared with the other two LQR controllers, the optimised controllers improved more than 50% in lateral error and heading error control, and the vehicle sideslip angle and vehicle yaw rate are in the range of −0.05° to 0.05° and − 0.15 rad/s to 0.10 rad/s, and it showed improved performance in tracking accuracy and satisfied vehicle stability constrains.

Adaptive multi-mode control system for stability and tracking control of distributed drive automated electric vehicle

Conventional tracking control algorithms of Distributed Drive Automated Electric Vehicles (DDAEVs) are typically designed for a unique control mode, which may lead to poor lateral stability under various driving conditions. Aimed this problem, an Adaptive Multi-mode Control System (AMCS) is proposed. Firstly, according to the adhesion utilization between tires and road surface, the driving condition is classified into three categories, and three control modes corresponding to them are proposed. Secondly, an Adaptive Neuro-fuzzy Inference-based Mode Matching Control System (ANFI-MMCS) is constructed to calculate the matching degree between vehicle states and control modes. Then, in order to achieve the ideal distribution of road wheel angle and yaw moment while avoiding approaching the limit speed corresponding to the reference trajectory, a model predictive control trajectory tracking controller and a speed controller with limitations were built. Finally, to achieve smooth switching between control modes, the outputs of AMCS are weighted according to the matching degree. In the experimental section, AMCS is evaluated on Carsim-Simulink co-simulation platform, and the results under different tests reveal that the proposed controller is more adaptable.

Cooperative Strategy of Trajectory Tracking and Stability Control for 4WID Autonomous Vehicles Under Extreme Conditions

Trajectory tracking and stability control are two essential functions of autonomous vehicles, and there is inevitable mutual interference between them, especially under extreme conditions. Towards addressing this challenge, this paper proposes a novel cooperative strategy of trajectory tracking and stability control for four-wheel independent drive (4WID) autonomous vehicles. An adaptive trajectory tracking controller is designed based on the model predictive control (MPC) theory, in which the tire cornering stiffness is modified in real-time leverages the lateral force estimated by the square-root cubature Kalman filter (SCKF). Then, the vehicle stability controller is designed based on the sliding mode control (SMC), in which the tire force saturation constraint and the relative weight of yaw rate and sideslip angle are considered. Furthermore, a weight adaptive criterion with normalized stability index is defined to develop the cooperative strategy of trajectory tracking and stability control. The feasibility and adaptability of the proposed method to different extreme conditions are verified by hardware-in-the-loop (HIL) test and CarSim-Simulink co-simulation. Finally, an experimental case is presented to demonstrate the realizability of the proposed method.

Optimal robust path tracking control for multi-constrained underactuated vehicles based on uncertainty orthogonal decomposition

In this article, an optimal robust constraint-following control is proposed for the underactuated vehicle path tracking problem. First, an underactuated dynamic model is established according to Udwadia–Kalaba (UK) equation, and the system uncertainty is decomposed into matching and mismatching portions based on the system geometric characteristics. The mismatching uncertainty is orthogonal to task space; thus, its influence on system stability is eliminated. Second, the diffeomorphism method is used to creatively set inequality and equality constraints into new equality constraints, and a robust control method for front steering vehicles with parameter-tunable is proposed. Third, an optimal design scheme is proposed for the tunable parameters to minimize the comprehensive index of system performance and control cost. Carsim-Simulink co-simulation shows the effectiveness of the proposed optimal robust control. This article creatively solves the problem of optimal robust control for path tracking of multi-constrained underactuated vehicles.

Robust path following control of 4WID autonomous vehicle with driving condition adaptive mechanism

This paper proposes a novel integrated path following control scheme for a 4-Wheel Independent Drive (4WID) autonomous vehicle that can adaptively change its mechanism according to the driving conditions. The proposed integrated system handles the lateral steering controller, longitudinal speed, and yaw moment controls considering tire force capacity of each corner. For the lateral controller, the cornering stiffness uncertainties and the transient performance are considered and combined into an H∞ robust controller based on linear matrix inequality (LMI) theory. A super-twisting sliding mode controller (STSMC) based longitudinal controller is designed to deal with disturbances and suppress chattering. When encountering extreme conditions, the active yaw moment controller with hierarchical structure is adaptively activated to prevent large deviation from the reference path and maintain the stability of vehicle. For the tire force allocation, an optimization algorithm is proposed, which has flexible equality constraints to coordinate the longitudinal and lateral motions according to the driving conditions. Simulations based on Carsim-Simulink co-simulation platform show that the proposed method is effective and has excellent performance in both normal and extreme driving conditions.

Adaptive coordination control strategy for path-following of DDAEV with neural network based moving weight coefficients

This paper presents an adaptive coordination control strategy for path-following of distributed drive autonomous electric vehicles (DDAEV). A model predictive control (MPC) algorithm is used to realize path-following through autonomous steering, where the prediction time is adaptive in relation to different driving conditions. Due to the dynamic characteristic of distributed drive vehicle, the differential torque control is also utilized based on the deviation of path to realize path-following. In order to make full use of the advantages of these two path-following methods, coordination control of autonomous steering and differential steering is adopted to improve the transient response speed, flexibility of steering system and path-following performance by setting moving weight coefficients based on neural network. Results of CarSim-Simulink co-simulation and real vehicle experiment both verify that the proposed coordination control can obtain steering flexibility, as well as tracking accuracy and reliability under various driving conditions.

A Novel Combined Decision and Control Scheme for Autonomous Vehicle in Structured Road Based on Adaptive Model Predictive Control

In the research of autonomous vehicles, most existing studies treat the decision/planning and control as two separate problems. This idea originates from robotics. But since there are essential differences between robot and autonomous vehicle, the structure in Robotics may not be suitable for autonomous vehicles. Considering decision/planning and control separately may affect the performance of autonomous vehicle under complex driving conditions. To fill in the research gap, this paper proposes a novel scheme which considers the local motion planning and control in a combined manner. Firstly, the local motion planning is transformed into the longitudinal control problem based on the proposed scenario adaptive MPC, by which the motion behavior (driving along the global path, car-following, lane-change) can be automatically decided. Then, the lateral MPC controller is designed to track the global path and conduct the local motion commands. To ensure the performance of the path tracking control and a smooth lane-change process simultaneously, an adaptive weight mechanism is introduced in the lateral controller. Comprehensive case studies including both straight and curve road are conducted based on Carsim-Simulink co-simulation platform. The results show that the proposed algorithm can not only ensure the vehicle safety in complex driving conditions, but also ensure that the vehicle can drive at its desired velocity as much as possible by intelligently judging the most proper motion behaviors.

Rendering bounded error in adaptive robust path tracking control for autonomous vehicles

For the sake of safety, the vehicle path tracking control should not only ensure the stability of the path tracking error containing the lateral offset and the orientation error but also guarantee that both the transient and steady states of the lateral offset are within a specified safe boundary. However, the time-varying uncertainties of a vehicle system make the control design a tough task. This paper develops an adaptive robust control (ARC) which guarantees both the tracking stability and the bounded error property for autonomous vehicles. First, to handle the bounded error requirement, a barrier function based state transformation which converts the constrained lateral offset into an unconstrained state is proposed. Then, the path tracking control task is cast into an equality constraint of the system state. On this basis, a novel adaptive robust constraint-following controller is developed to make the transformed system follow the proposed equality constraint. Through Lyapunov minimax analysis, it is proved that the resulting control guarantees the approximate constraint-following performance and the bounded error property despite the presence of system uncertainties. Finally, the main theoretical results are verified through CarSim-Simulink co-simulation results.

Active Obstacle Avoidance Control Strategy Based on Four-Wheel Steering

Active obstacle avoidance control strategy is the main question of intelligent vehicles, and active four-wheel steering is gradually used in the intelligent control system. Considering both tracking performance and driving characters, an active controller with four-wheel steering (4WS) and active rear steering (ARS) based on adaptive model predictive theory (AMPC) is designed. The control architecture is composed a supervisor and an AMPC controller. The supervisor is used to select the appropriate control mode and the AMPC is used to calculate the expected steering angle when the stability indexes over the safety threshold. Finally, the proposed control strategy is simulated via Carsim-Simulink co-simulation. The results show that the integrated controller can track the obstacle avoidance path and has good driving performance.

Path tracking control strategy for the intelligent vehicle considering tire nonlinear cornering characteristics in the PWA form

To improve the path tracking control performance of the intelligent vehicle under critical maneuvers, a novel control strategy is introduced in this work. Considering that the tire cornering characteristics show high nonlinearities and uncertainties under those special driving conditions, a three-dimensional piecewise affine (PWA) identification method is proposed to realize the nonlinear modeling of the tire cornering characteristics for the first time. On this basis, the PWA model of the vehicle lateral dynamics is established. To obtain the vehicle target yaw rate for path tracking, a driver direction control model with adaptive preview time is put forward. Then, the linear quadratic optimal control method is further adopted to design multiple path tracking controllers for different working areas of the affine subsystems, thus the optimal steering angles of the front wheels can be generated to guarantee the path tracking performance for the intelligent vehicle under a wide range of driving conditions. Finally, to evaluate the performance of the proposed path tracking control strategy which considers the tire nonlinear cornering characteristics in the PWA form, the CarSim-Simulink co-simulation work is conducted. The co-simulation results show that the proposed control strategy presents significant performance advantages over the other two methodologies and demonstrates satisfactory path tracking control performance.

Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter

The distributed drive arrangement form has better potential for cooperative control of dynamics, but this drive arrangement form increases the parameter acquisition workload of the control system and increases the difficulty of vehicle control accordingly. In order to observe the vehicle motion state accurately and in real-time, while reducing the effect of uncertainty in noise statistical information, the vehicle state observer is designed based on interacting multiple model theory with square root cubature Kalman filter (IMM-SCKF). The IMM-SCKF algorithm sub-model considers different state noise and measurement noise, and the introduction of the square root filter reduces the complexity of the algorithm while ensuring accuracy and real-time performance. To estimate the vehicle longitudinal, lateral, and yaw motion states, the algorithm uses a three degree of freedom (3-DOF) vehicle dynamics model and a nonlinear brush tire model, which is then validated in a Carsim-Simulink co-simulation platform for multiple operating conditions. The results show that the IMM-SCKF algorithm’s fusion output results can effectively follow the sub-model with smaller output errors, and that the IMM-SCKF algorithm’s results are superior to the traditional SCKF algorithm’s results.

Estimation of Vehicle Longitudinal Speed Based on Improved Kalman Filter

Estimation of vehicle longitudinal acceleration is very important in vehicle active safety control system. In this paper, two driving conditions of a 4WD off-road vehicle are divided by vehicle signals such as steering angle. Under different working conditions, different estimation algorithms are adopted. In the straight driving condition, the longitudinal speed was estimated by adjusting the variance weight of acceleration Kalman observation noise based on kinematics method. For steering conditions, in order to obtain the longitudinal velocity at the center of mass, by dynamic method, a lateral state estimator was designed and tire sideslip dynamics was modeled. The CarSim-Simulink co-simulation results show that the proposed algorithm has high accuracy and strong practicability.

Adaptive authority allocation-based driver-automation shared control for autonomous vehicles

To avoid crashes caused by driver error in avoiding obstacles, a driver-centered steering assist controller with an adaptive authority allocation system is proposed for cooperative control purposes. To begin with, a concept of space collision risk (SCR) is introduced creatively for assessing the vehicle’s safety status in which the relative distance and the relative angle between the vehicle and the obstacle are taken into consideration. Meanwhile, the SCR-based authority allocation system is addressed to allocate steering authorities between the human driver and the assist controller adaptively to reduce SCR to zero as soon as possible. After that, an autonomous steering controller based on the model predictive control (MPC) technique and the artificial potential field (APF) method, considering not only the vehicle stability constraints but also roads and obstacles constraints, is developed to aid the human driver when necessary. In the end, the proposed algorithm is simulated in the CarSim-Simulink co-simulation platform under a series of typical scenarios, which shows the feasibility and effectiveness of the presented shared steering control method.

Design and Optimization of Robust Path Tracking Control for Autonomous Vehicles With Fuzzy Uncertainty

Uncertainty is a major concern in vehicle path tracking control design. The coefficients of the uncertainty bound are unknown. They are assumed to lie within prescribed fuzzy sets. First, based on the path tracking kinematic model, this paper innovatively formulates the vehicle path tracking task as a constraint-following problem. Second, we put forward a deterministic adaptive robust control law with a tunable parameter to ensure the uniform boundedness and ultimate uniform boundedness of the closed-loop system. Third, an optimal scheme for the tunable parameter is proposed based on the fuzzy uncertainty. The resulting optimal robust control (ORC) minimizes a comprehensive fuzzy performance index that involves the fuzzy system performance and the control cost. The results of the CarSim-Simulink co-simulation and the Hardware-in-loop (HIL) experiment together show that the proposed optimal robust control exhibits a superior path tracking performance.

Holistic Adaptive Multi-Model Predictive Control for the Path Following of 4WID Autonomous Vehicles

This paper presents a novel adaptive multiple-model predictive control (MPC) scheme for Four-Wheel Independent Drive (4WID) autonomous vehicles with a holistic structure. Firstly, the combined vehicle-path model is established. To ensure the real-time performance of MPC, the coupling relationships in the control output and the longitudinal/lateral motions are well decoupled, in this way a linear integrated model is utilized as the internal model of the controller. Then, the holistic MPC is proposed to acquire the steering angle and the force on each corner. Based on the advantages of the proposed structure, a weight adaptive mechanism is introduced to improve the handling ability of the controller to various driving conditions, especially some extreme conditions. For the uncertainties in tire cornering stiffness, a multiple-model adaptive law is designed with its convergence proved by Lyapunov theory. Numerical results based on Carsim-Simulink co-simulation platform demonstrate the effectiveness and superiority of the proposed control method in both normal and extreme driving conditions.

Model reference adaptive LQT control for anti-jerk utilizing tire-road interaction characteristics

Sudden vehicle propulsion torque change under tip-in/out maneuver often leads to low-frequency longitudinal vibration due to the flexibility in the half-shaft and tire slip, which greatly affects vehicle drivability. Note that the vibration frequency is between 1 and 10 Hz and is difficult to be absorbed by the vehicle mechanical system. To optimize the vehicle drivability under tip-in maneuver, an Adaptive Linear Quadratic Tracking (ALQT) anti-jerk traction controller is proposed in this paper. Based on the experimental data, a Carsim-Simulink co-simulation model is developed for assessing control performance. A control-oriented model, considering the nonlinear characteristics of the tire-road friction coefficient and slip ratio, is then proposed. A reference model with rigid axle is used to provide the equilibrium points and reference velocity trajectory. Jacobi linearization method is then used to linearize the model along the desired trajectory and a linear deviation model based on equilibrium points is obtained. Finally, the deviation compensation receding horizon LQT controller is designed along with the Kalman state estimation. The effectiveness of the designed controller is assessed via simulation studies under different road surfaces and compared with PID and LQR controllers. The LQT controller is able to track the desired velocity profile with minimum jerk while increasing road safety. Furthermore, the effect of LQT weighting coefficients under different road surfaces are discussed. Simulation results show that the ALQT controller is able to optimize vehicle drivability under different road surfaces and the weighting matrices shall be selected based on the road condition for optimal drivability.

Trajectory tracking control of steer-by-wire autonomous ground vehicle considering the complete failure of vehicle steering motor

This paper investigates the trajectory tracking control problem of steer-by-wire autonomous ground vehicle considering the complete failure of vehicle steering motor. In order to achieve the trajectory tracking control of autonomous ground vehicle in presence of the complete failure of vehicle steering motor, the mechanical transmission mechanism of vehicle steering system is analyzed with the steering motor fault being considered, and the differential steering is used to actuate the turning of vehicle in case of emergency. A model predictive controller with the constraints of tire cornering angle and road adhesion is designed to compute the expected front-wheel steering angle for vehicle trajectory tracking control, and a nonsingular terminal sliding mode controller is proposed to track the expected front-wheel steering angle using differential-steering moment, in which a novel observer design method is presented to estimate the actual front-wheel steering angle. Two case studies are implemented in a CarSim-Simulink co-simulation platform, and the simulation results have verified that the proposed method have satisfactory trajectory tracking control effectiveness in presence of the complete failure of vehicle steering motor.

An adaptive SMC controller for EVs with four IWMs handling and stability enhancement based on a stability index

In order to make full use of the direct yaw control (DYC) in electric vehicles with four in-wheel motors (IWMs), this paper presents an adaptive SMC control scheme to improve the handling and stability based on a novel stability index proposed to quantitatively represent stability level. Firstly, a vehicle stability evaluation method is designed based on the front and rear tire slip angles phase plane and the quantitative stability index is derived according to the area where the states stay and the changing trend of the states. Secondly, a sliding mode controller (SMC) is designed to track the reference obtained by a two degrees of freedom (DoF) vehicle model as the desired vehicle motion. To enhance both vehicle handling and stability, the weight of the sliding surface is adaptively scheduled according to this stability index. The torque of each wheel is then distributed by solving the optimal adhesion rate consumption problem. Finally, the effectiveness of the proposed method is verified by CarSim–Simulink co-simulation. The simulation results illustrate that the proposed method can effectively improve vehicle handling and stability and has good robustness for the uncertainties of vehicle sideslip angle and road friction coefficient.

Acceleration slip regulation control for four-wheel independently drive electric vehicle based on fuzzy control

To improve the acceleration performance and stability of the four-wheel independent drive (4WID) electric vehicle on low-adhesion road, a fuzzy control that doesn’t depend on accurate vehicle models is proposed. Taking the driving torque of one side wheel as a reference the slip rate is controlled by controlling the torque errors between the left and right wheels to a certain range. Carsim-Simulink co-simulation is used to analyze the acceleration stability of 4WID electric vehicle on low-adhesion road and μ-split road. The simulation results show that the wheel slip rate can be controlled within a reasonable range through proposed method, and the stability and safety of the vehicle can be effectively improved on the basis of ensuring the power performance of the vehicle.

Research on Active Obstacle Avoidance Control Strategy for Intelligent Vehicle Based on Active Safety Collaborative Control

Aiming at the issue of active obstacle avoidance control for intelligent vehicles under emergency, an active safety collaborative control system integrated with adaptive cruise control (ACC), rear steering control (RSC), and rollover braking control (RBC) is developed. The proposed novel control architecture consists of a supervisor, an upper controller, and a lower controller. The supervisor decides which control mode to be used based on multi-sensors information of the driving vehicle. Active rear wheel steering based on lateral acceleration feedback and active braking control strategies based on fuzzy PID are added into the control model to enhance driving stability. To make the vehicle motion control model suitable for highway path tracking with obstacle avoidance under emergency, a priority is given to the RBC controller even though the vehicle speed is lower than the expected speed when the rollover index exceeds the safety threshold. A nonlinear vehicle simulation model is established to validate the proposed control scheme based on Carsim. Finally, a Carsim-Simulink co-simulation model is constructed, and simulation results show that the proposed integrated control strategy based on active safety collaborative control has good performance in both path tracking and driving stability, and has better stability of rollover under emergency.