This paper proposes an intelligent proportional integral (PI) control based on an ultra-local model and a Kalman filter with automatic parameter tuning for vehicle yaw-rate control. A good vehicle yaw-rate controller is essential for autonomous driving. The traditional PI controller proposed in the conventional method cannot realize the desired yaw-rate response because the vehicle velocity yaw-rate characteristics change with vehicle velocity. Although an intelligent model-free PI control can improve the yaw-rate response, it does not account for the measurement noise, so the noise amplified by feedback control is added to the control input. This problem is crucial because a large control input velocity (i.e. a high steering-angle velocity) may cause wheel damage. A Kalman filter was introduced to conventional intelligent PI control to address this problem. In addition, we automatically tune the design parameters of the Kalman filter by Bayesian optimization. The effectiveness of the proposed method was investigated in a vehicle simulator. The experimental results confirm higher control performance of the proposed method than of conventional methods in noisy cases. 

Intelligent PI control based on the ultra-local model and Kalman filter for vehicle yaw-rate control

This paper presents a sliding mode control (SMC)-based path-tracking algorithm for autonomous vehicles by considering model-free adaptive feedback actions. In autonomous vehicles, safe path tracking requires adaptive and robust control algorithms because driving environment and vehicle conditions vary in real time. In this study, the SMC was adopted as a robust control method to adjust the switching gain, taking into account the sliding surface and unknown uncertainty to make the control error zero. The sliding surface can be designed mathematically, but it is difficult to express the unknown uncertainty mathematically. Information of priori bounded uncertainties is needed to obtain closed-loop stability of the control system, and the unknown uncertainty can vary with changes in internal and external factors. In the literature, ongoing efforts have been made to overcome the limitation of losing control stability due to unknown uncertainty. This study proposes an integrated method of adaptive feedback control (AFC) and SMC that can adjust a bounded uncertainty. Some illustrative and representative examples, such as autonomous driving scenarios, are also provided to show the main properties of the designed integrated controller. The examples show superior control performance, and it is expected that the integrated controller could be widely used for the path-tracking algorithms of autonomous vehicles.

/pdf/development-of-a-sliding-mode-control-based-path-tracking-3ew51dfj.pdf

Development of a Sliding-Mode-Control-Based Path-Tracking Algorithm with Model-Free Adaptive Feedback Action for Autonomous Vehicles

Abstract. In order to solve the problems of low path-tracking accuracy, poor safety, and stability of intelligent vehicles with variable speeds and obstacles on the road, a double-layer adaptive model predictive controller (MPC) is
designed. A vehicle point mass model is used in an obstacle avoidance planning controller, and the safety collision distance model is established according to the distance relationship between the vehicle and the obstacle to improve the driving safety of the vehicle. The design of the path-tracking controller is based on the three-degrees-of-freedom dynamics model. According to
the relationship between the predictive horizon and vehicle speed in the MPC algorithm, an adaptive path-tracking control strategy which can update the prediction horizon in real time is proposed to improve the accuracy of vehicle path tracking. To increase the vehicle stability, a sideslip angle and an acceleration control variable are added to the vehicle
dynamics model as soft constraint conditions. The proposed method is simulated based on a CarSim and MATLAB/Simulink co-simulation platform. The simulation results show that the maximum lateral path deviation and the
maximum centroid sideslip angle of the designed controller are 0.13 m and
0.4∘, respectively. Compared with the traditional MPC, the adaptive MPC maximum lateral path deviation
and the maximum centroid sideslip angle are reduced by 0.51 m and
1.57∘, respectively, which proves the effectiveness of the
proposed method.


/pdf/intelligent-vehicle-obstacle-avoidance-path-tracking-control-2rmp9p5g.pdf

Intelligent vehicle obstacle avoidance path-tracking control based on adaptive model predictive control

The vehicle particle model was built to compare and analyze the effectiveness of three different collision avoidance methods. The results show that during vehicle high-speed emergency collision avoidance, lane change collision avoidance requires a smaller longitudinal distance than braking collision avoidance and is closer to that with a combination of lane change and braking collision avoidance. Based on the above, a double-layer control strategy is proposed to avoid collision when vehicles change lanes at high speed. The quintic polynomial is chosen as the reference path after comparing and analyzing three polynomial reference trajectories. The multiobjective optimized model predictive control is used to track the lateral displacement, and the optimization objective is to minimize the lateral position deviation, yaw rate tracking deviation, and control increment. The lower longitudinal speed tracking control strategy is to control the vehicle drive system and brake system to track the expected speed. Finally, the lane changing conditions and other speed conditions of the vehicle at 120 km/h are verified. The results show that the control strategy can track the longitudinal and lateral trajectories well and achieve effective lane change and collision avoidance.

/pdf/longitudinal-and-lateral-control-strategies-for-automatic-2c0h780d.pdf

Yanchuan Xu

Papers

Robust Hierarchical Model Predictive Control for Trajectory Tracking with Obstacle Avoidance