Integrated Path tracking control of steering and braking based on holistic MPC

Guo-dong, Wang; Liu, Li; Meng, Yu; Gu, Qing; Bai, Guoxing

doi:10.1016/j.ifacol.2021.06.007

Cited by 12 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The balance equations of an intelligent vehicle along the , and axis can be obtained according to Newton's second law, as given below. (6) In equation ( 6) above, is the mass of the intelligent vehicle, and are the distance between the center of mass of the intelligent vehicle and the front and rear axles, respectively, and is the moment of inertia of the intelligent vehicle around the axis.…”

Section: A Other Recommendationsmentioning

confidence: 99%

“…Path tracking control enables intelligent vehicles to drive accurately, quickly, and safely in accordance with a path planned by higher-level control [1]. Many control theories have been developed to perform path tracking control in automated systems designed to drive intelligent vehicles, the most important of which include preview control (a form of model predictive control (MPC)) [2], fuzzy control [3], LQR control [4], PID control [5], and model predictive control [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

Shi

et al. 2022

IEEE Access

View full text Add to dashboard Cite

In this study, to improve the accuracy of path tracking in intelligent vehicles, we propose an intelligent vehicle path-tracking control method based on improved model predictive control (MPC) combined with hybrid proportional-integral-derivative (PID) control theory. In the lateral control, a constraint on the side deflection of the front wheel is added based on traditional MPC and a relaxation factor is introduced to improve the stability of vehicle control for the driving stability. In longitudinal control, a hybrid PID controller is designed for different road conditions to improve the accuracy of control of vehicle speed. We present the results of a co-simulation using CarSim and MATLAB/Simulink and a test with a sample vehicle, which show that the proposed path tracking controller can greatly improve the path tracking accuracy and stability of an intelligent vehicle. The model-based prediction, rolling optimization solution, feedback control, and the addition of a constraint on the side deflection of the front wheel as well as a relaxation factor can ensure the lateral driving stability of an intelligent vehicle. The proposed approach achieved a lateral error of less than 1%, and the yaw angle was controlled within 4°. The longitudinal speed control based on hybrid PID controller can improve the response speed of the system and meet the real-time requirements of vehicle driving.

show abstract

Section: A Other Recommendationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

Shi

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In detail, fitness calculation is defined as the reciprocal of objective function because minimization of the objective function corresponds to maximum coincidence. e fitness of ith individual is defined as (19). e selection is usually driven by fitness-ranking evaluation by means of a specific mathematical method.…”

Section: Iterative Optimizationmentioning

confidence: 99%

“…In recent years, some new control theories and applications have been extensively developed, such as model predictive control [15], model-free control (MFC) [16], and static output feedback control (SOF) [17]. More and more scholars put forward the MPC into autonomous vehicle [18,19]. MPC controller design is based on an identified model from experimental data in open loop.…”

Section: Introductionmentioning

confidence: 99%

Path Tracking Method Based on Model Predictive Control and Genetic Algorithm for Autonomous Vehicle

Wang¹,

Chen²,

Yang

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Model predictive control (MPC) is often used for controlling the autonomous vehicle tracking the target path. But to apply MPC schemes, the nonlinear model of vehicle kinematics needs to be approximately converted to a linear format, and the path tracking problem has to be converted into certain formats in order to implement the solver of convex quadratic programming. To solve these issues, a control strategy combining MPC and genetic algorithm (GA) is put forward. The nonlinear predictive model is adopted to predict the future movement of a controlled vehicle. The objective function is established according to the future movement and target path. Instead of using a convex quadratic programming solver, GA is applied to solve the optimization problem. The proposed MPC-GA method can handle the arbitrary nonlinear problem and make the objective function more comprehensible and flexible. This method is applied in solving the path tracking problem of an autonomous vehicle. Both simulations and on-field tests are conducted. The results validate the efficiency of the proposed MPC-GA path tracking method in comparison with traditional methods. With the MPC-GA controller, the automatic driving on the park road is basically realized. The control strategy can be considered as an alternative method to solve the path tracking problem for an autonomous vehicle.

show abstract

“…Zhao [ 22 ] proposed a preview time adaptive control method for path tracking based on the MPC algorithm and vehicle dynamics model, and used a differential torque control method based on reference heading angle to improve the reliability and accuracy of path tracking under constant torque demand. Wang [ 23 ] designed a combined steering and braking path tracking controller based on the MPC algorithm. By linearizing the nonlinear vehicle and tire models, a linear time-varying MPC was designed to improve the real-time performance of the system.…”

Section: Introductionmentioning

confidence: 99%

Research on Six-Wheel Distributed Unmanned Vehicle Path Tracking Strategy Based on Hierarchical Control

et al. 2022

View full text Add to dashboard Cite

For the multi-objective control problem of tracking effect and vehicle stability in the path tracking process of six-wheel distributed unmanned vehicles, a control strategy based on hierarchical control (HC) theory is proposed. A hierarchical kinematic model is designed considering the structural advantages of independent steering and independent driving of the unmanned vehicle, and this model is applied to the path tracking strategy. The strategy is divided into two levels of control. The upper level of control is to use the upper-level kinematic model as the prediction model of model predictive control (MPC), and to convert the solution problem of future control increments into the optimal solution problem of quadratic programming by setting the optimal objective function and constraints. The lower level of control is to map the optimal control quantities obtained from the upper level control to the six-wheel speeds and the four-wheel turning angles through the lower-level kinematics, and to design the six-wheel torque distribution rules based on deterministic torque and stability-based slip rate control for executing the control requirements of the upper level controller to prevent the unmanned vehicle from generating sideslip and precisely generating transverse moment to ensure the stable driving of the unmanned vehicle. Experiments were conducted on the Trucksim/Simulink simulation platform for a variety of road conditions, and the results showed that hierarchical control improved the accuracy of tracking the desired path and the driving stability on complex road surfaces more than MPC.

show abstract

Integrated Path tracking control of steering and braking based on holistic MPC

Cited by 12 publications

References 12 publications

Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

Path Tracking Method Based on Model Predictive Control and Genetic Algorithm for Autonomous Vehicle

Research on Six-Wheel Distributed Unmanned Vehicle Path Tracking Strategy Based on Hierarchical Control

Contact Info

Product

Resources

About