This paper presents a preview steering control algorithm and its closed-loop system analysis and experimental validation for accurate, smooth, and computationally inexpensive path tracking of automated vehicles. The path tracking issue is formulated as an optimal control problem with dynamic disturbance, i.e., the future road curvature. A discrete-time preview controller is then designed on the top of a linear augmented error system, in which the disturbances within a finite preview window are augmented as part of the state vector. The obtained optimal steering control law is in an analytic form and consists of two parts: 1) a feedback control responding to tracking errors and 2) a feedforward control dealing with the future road curvatures. The designed control’s nature, capacity, computation load, and underlying mechanism are revealed by the analysis of system responses in the time domain and the frequency domain, theoretical steady-state error, and comparison with the model predictive control (MPC). The algorithm was implemented on an automated vehicle platform, a hybrid Lincoln MKZ. The experimental and simulation results are then presented to demonstrate the improved performance in tracking accuracy, steering smoothness, and computational efficiency compared to the MPC and the full-state feedback control.

Design, Analysis, and Experiments of Preview Path Tracking Control for Autonomous Vehicles

Adaptive-neural-network-based robust lateral motion control for autonomous vehicle at driving limits

As a typical example of cyber-physical systems, intelligent vehicles are receiving increasing attention, and the obstacle avoidance problem for such vehicles has become a hot topic of discussion. This paper presents a simultaneous trajectory planning and tracking controller for use under cruise conditions based on a model predictive control (MPC) approach to address obstacle avoidance for an intelligent vehicle. The reference trajectory is parameterized as a cubic function in time and is determined by the lateral position and velocity of the intelligent vehicle and the velocity and yaw angle of the obstacle vehicle at the start point of the lane change maneuver. Then, the control sequence for the vehicle is incorporated into the expression for the reference trajectory that is used in the MPC optimization problem by treating the lateral velocity of the intelligent vehicle at the end point of the lane change as an intermediate variable. In this way, trajectory planning and tracking are both captured in a single MPC optimization problem. To evaluate the effectiveness of the proposed simultaneous trajectory planning and tracking approach, joint veDYNA-Simulink simulations were conducted in the unconstrained and constrained cases under leftward and rightward lane change conditions. The results illustrate that the proposed MPC-based simultaneous trajectory planning and tracking approach achieves acceptable obstacle avoidance performance for an intelligent vehicle.

Simultaneous Trajectory Planning and Tracking Using an MPC Method for Cyber-Physical Systems: A Case Study of Obstacle Avoidance for an Intelligent Vehicle

Model predictive path following control for autonomous cars considering a measurable disturbance: Implementation, testing, and verification

It is a striking fact that the characteristics of parametric uncertainties, external disturbance, time-varying and nonlinearities are available in the constructed model of autonomous independent-drive vehicles; therefore, in this paper, the robust model predictive control (MPC) with the finite time horizon is proposed to realize the coordinated path tracking and direct yaw moment control (DYC) for autonomous four in-wheel motor independent-drive electric vehicles (AMIDEV). Firstly, considering the time-varying and uncertain feature of the tire cornering stiffness and the vehicle velocity in the state space equation constructed by 2 degrees of freedom (DoF) vehicle model and the path tracking preview model, the linear parameter varying (LPV) discrete model with four polytypic vertexes is constructed. Then, based on the linear matrix inequality (LMI) method, the novel robust MPC theory with the finite time horizon is put forward to solve the min-max optimization problem after updating four polytypic vertexes in real time, which could deal with the inevitable model mismatch problem caused by the time-varying, uncertain vehicle dynamic characteristics and external disturbance. Finally, the simulation and experimental results have verified that the proposed novel robust MPC theory could emerge from the stranglehold exercised by the conservativeness of the traditional robust MPC theory with the infinite time horizon, which strengthens the robustness of this control system as well as achieves better path tracking accuracy and handling ability of AMIDEV.

Path Tracking and Direct Yaw Moment Coordinated Control Based on Robust MPC With the Finite Time Horizon for Autonomous Independent-Drive Vehicles

Dual-envelop-oriented moving horizon path tracking control for fully automated vehicles

The invention discloses a regional path tracking control method for an autonomous land vehicle. A problem that collision of a vehicle with a road boundary or surround obstacle occurs because shapes and sizes of the vehicle and the road are not considered by the existing tracking control method can be solved. The method comprises the following steps: establishing a two-dimensional road model; establishing a mathematical model associated with a vehicle path tracking problem; calculating a roadable region boundary line within a certain distance in front of the vehicle; establishing a vehicle system model; carrying out a design of a regional path tracking control model and selecting a controlled quantity being an optimum front wheel steering angle at current time; according to the optimum front wheel steering angle, controlling a steering execution mechanism to make motion to enable the controlled vehicle to be driven in a roadable region, provided by a vehicle sensing system, within a certain distance in front of the vehicle. When the two-dimensional road model is established, the shapes and sizes of the vehicle and roads are taken into consideration, thereby reducing the possibility of collision of the vechile with the road boundary and thus improving safety of the autonomous land vehicle.

Regional path tracking control method for autonomous land vehicle

Path tracking issues of autonomous ground vehicles (AGVs) have attracted more attention in recent years with the intelligent and electrified development of vehicles. In order to make AGVs path tracking problem more flexible, regional path tracking problem is discussed in this manuscript based on model predictive control (MPC) method, where the front wheel steering angle is regarded as the control variable. The feasible region for AGVs running is determined first according to the detected road boundaries. In the following, AGVs running in this region is considered using kinematic model. Then, in order to make the actual trajectory of AGVs keep in the region and satisfy the safety requirements, MPC method is employed to design path tracking controller considering the vehicle dynamics, the actuator and state constraints. In order to verify the effectiveness of the proposed algorithm, simulations under various test conditions are carried out using a high fidelity vehicle simulator veDYNA, where the Hongqi vehicle HQ430 parameters are matched. The results obtained from the simulation illustrate that the proposed algorithm obtains good performance in dealing with the regional path tracking problem.

MPC-Based Regional Path Tracking Controller Design for Autonomous Ground Vehicles

A novel regional path tracking description is presented in this manuscript, and the moving horizon control method that is model predictive control (MPC) is proposed to discuss the regional path tracking issue which could avoid colliding road boundary when tracking a more complex road effectively. The feasible region for autonomous ground vehicles (AGVs) running is determined first according to the detected road boundaries. Then, in order to keep the actual trajectory of AGVs in the region and satisfy the safety requirements, MPC method is employed to design the path tracking controller considering actuator and road boundary constraints. In order to verify the effectiveness of the proposed method, experiments based on Hongqi AGV HQ430 are carried out, and the results illustrate that the presented method could be successfully applied to Hongqi AGV vehicle HQ 430.

Regional path moving horizon tracking controller design for autonomous ground vehicles

Wheel skidding or spinning is dangerous for driving vehicles, and compared to conventional vehicles, electric vehicles are more easily to slip during driving on slippery road. In this paper, a novel traction control method based on wheel-slip control is proposed to improve the safety and stability of electric vehicles. Considering the high nonlinearity and uncertainties of the traction system, this paper takes use of the feedback linearization methodology to design a nonlinear controller to achieve the tracking control for wheel slip. Meanwhile, to enhance the robustness of the controller, a integral action is introduced. As the controlled variable, the desired value of the wheel slip is required in the traction control system, this paper adopts the so-called Burckhardt's model to make online calculation for it. In order to demonstrate the effectiveness of the proposed method, simulations were conducted in different road conditions, and all the simulation results showed that the wheel-slip controller could produce a fairly good control performance.

Ru Yu

Papers

Dual-envelop-oriented moving horizon path tracking control for fully automated vehicles

Regional path tracking control method for autonomous land vehicle

MPC-Based Regional Path Tracking Controller Design for Autonomous Ground Vehicles

Regional path moving horizon tracking controller design for autonomous ground vehicles

Optimal slip based traction control for electric vehicles using feedback linearization