Path Tracking Control Based on Model Predictive Control With Adaptive Preview Characteristics and Speed-Assisted Constraint

Abstract

As one of the research focuses in the field of intelligent driving, improving the performance of path tracking has become a goal for many scholars. Among many path tracking control algorithms, model predictive control (MPC) controllers are widely used due to their excellent control performance. However, the traditional MPC control has shortcomings because it does not consider the particularity of the driving car with preview driving characteristics, i.e., it is only directly controlling from the vehicle state. To fully retain the advantages of the MPC controller and simultaneously exert the preview characteristic of the intelligent vehicle to improve the path tracking performance, this work proposes a model predictive control with adaptive preview characteristics and an algorithm of longitudinal vehicle speed-assisted constraint for the path tracking algorithm. The algorithm mainly consists of two parts: The MPC controller with adaptive preview characteristic is proposed based on the lateral error and target curvature; The longitudinal vehicle speed-assisted constraint algorithm based on the largest ideal lateral acceleration becomes a supplementary part of the algorithm as a supplementary constraint of the MPC controller. Series of simulations based on the Simulink/CARSIM software individually verified the model predictive control with adaptive preview characteristics and an algorithm of longitudinal vehicle speed-assisted constraint for the path tracking algorithm. The proposed adaptive preview strategy is suitable for the path tracking algorithm controlled by the MPC controller and improves the path tracking performance. INDEX TERMS path tracking, model predictive control (MPC), adaptive preview characteristics, assisted constraint

A Review of Autonomous Vehicle Path Tracking Algorithm Research

“…The MPC algorithm also has many applications in low-speed steering conditions [45]. For example, in the literature [46][47][48][49][50], the MPC algorithm was improved to improve the path tracking accuracy and driving stability of driverless cars under right-angle turns, continuous curves and arc curves, and MPC-based integrated control algorithm was proposed and the tracking accuracy and driving stability were verified. There are also studies combining MPC with other algorithms, for example, Shi et al proposed a path tracking algorithm based on MPC and PID, added front wheel side bias constraints on the basis of traditional MPC and introduced relaxation factors, and designed hybrid PID controllers for different road conditions to improve the accuracy of vehicle speed control, and simulation results proved that the algorithm greatly improved the stability and tracking accuracy of vehicle control [ 51].…”

Path tracking of autonomous vehicle based on adaptive preview trajectory planning with the consideration of vehicle stability

“…For the path tracking problem, the fitting problems is unconstrained, so constraint (20) is the empty set. Therefore, the optimization problem can be solved directly by calculating the extreme point of equation ( 21), namely to calculate the coefficients a i making the derivative of equation ( 21) equal to 0, which is For the avoidance problem, no contact with obstacles the constraints for the fitting problems.…”

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

“…Given that the main objective is that of following a predefined trajectory, the state variables are the difference between the actual position and/or velocity to the references (i.e., the errors), whereas the control input is the vehicle acceleration. When the lateral dynamic is considered, the steering angle and the yaw rate are added as control input and state variable, respectively [60,[65][66][67][77][78][79][80][81][82][83][84][85][86][87][88][89]. It is worth mentioning that no further analyses on the works just cited is brought up, owing to the fact that all the studies use the same state variables and control inputs.…”

“…Referring to the path tracking (both longitudinal and lateral), the constraints are often set to ensure stability, thus requiring the sideslip angle and the yaw rate to be below a certain value (i.e., a value that changes accordingly to the vehicle characteristics). Moreover, it happens that bounded values for acceleration and steering angle are set to comply with the physics of the problem [60,[65][66][67][78][79][80][81][82][83][85][86][87][88][89].…”