Young Seop Son

Robust Multirate Control Scheme With Predictive Virtual Lanes for Lane-Keeping System of Autonomous Highway Driving

In this paper, we propose a new approach for a robust multirate lane-keeping control scheme with predictive virtual lanes. First, the multirate lane-keeping control scheme is proposed to improve the lane-keeping performance and to reduce the ripple in the yaw rate. To improve the lane-keeping performance on a curved road, the integral of the lateral offset error is added to the state feedback controller. A multirate Kalman filter (KF) has been developed to resolve the problems caused by slow lane detection due to the vision processing system. This multirate KF estimates vehicle states at a fast rate using a microprocessor. Utilizing the estimated states, the linear quadratic state feedback control operates at the same fast update rate of the microprocessor. Thus, a multirate control scheme can reduce the ripple in the yaw rate. Second, we propose a virtual lane prediction method that compensates for the momentary failure of lane detection from unexpected problems. If the camera sensor momentarily fails while obtaining lane information, the predicted virtual lane can be substituted for the lane detection using the camera sensor in the proposed control scheme. Thus, the proposed control scheme can normally operate when the lane information is momentarily unavailable. The performance of the proposed method was evaluated via experiments.

Torque-Overlay-Based Robust Steering Wheel Angle Control of Electrical Power Steering for a Lane-Keeping System of Automated Vehicles

We propose a torque-overlay-based robust steering wheel angle (SWA) control method of electrical power steering (EPS) for a lane-keeping system of automated vehicles. The proposed method consists of an augmented observer and a nonlinear damping controller to guarantee the semiglobal uniform ultimate boundedness of the SWA tracking error using only SWA feedback. The key idea of the proposed method is that the system functions with unknown parameters and external disturbance, along with their derivatives, are combined into an augmented state variable for designing the nonlinear observer in the absence of Lipschitz conditions. The augmented observer is designed to estimate the full state and disturbance. The nonlinear damping controller is developed via backstepping to suppress the angle tracking error using the input-to-state stability property when the estimation error becomes large. Since the proposed method is designed based on torque overlay, a torque integration using basic functions of the EPS for SWA control is available for driver convenience. Furthermore, no modification of the EPS is required. The performance of the proposed method was validated through experimentation with a test vehicle.

Clamping Force Control for an Electric Parking Brake System: Switched System Approach

This paper addresses the modeling, controller design, and stability analysis of an electric parking brake system in which a screw-nut self-locking mechanism is used. The system is modeled as a state-dependent switched system according to the operating mode. A nonlinear proportional (P) clamping force controller using the measured force is proposed to enhance the performance specifications. We show the uniform stability of the state-dependent switched system with the nonlinear P controller using a common Lyapunov theorem and LaSalles invariance principle. We derive the conditions to assure stable self-locking operation of the system. We also show the existence of the largest invariant set depending on the target braking force. This analysis offers a guideline as to how a nonlinear controller can be designed in view of the self-locking stability and control performance. Through simulation and experimental results, we confirm that the solution is locally uniformly ultimately bounded. Through the experimental results, we show that the nonlinear P controller outperforms a simple on/off controller in terms of the average and deviation of the braking force error. Furthermore, it is experimentally verified that the system is also able to function as a pseudo-antilock braking system.

Torque Overlay Based Robust Steering Wheel Angle Control for Lateral Control Using Backstepping Design

Abstract We propose a torque overlay based robust steering wheel angle control of electric power steering (EPS) for lateral control using backstepping design. The main contribution of this paper is that the proposed method is designed based on torque overlay and that the global uniform ultimate boundedness of the steering wheel angle tracking error is guaranteed using only steering wheel angle feedback with external disturbances. The key idea is to make the EPS dynamics be simplified. Then, the external disturbances, system function, and input gain uncertainty are regarded as a disturbance. An augmented observer is designed to estimate the full state and the disturbance. A nonlinear damping controller is developed via backstepping to suppress a position tracking error using input-to-state stability property. The proposed method uses only steering wheel angle feedback and nominal value of the input gain. The proposed method is simple to implement in real-time control and robust to the parameter uncertainties and the external disturbances. Since the proposed method is designed based on torque overlay as add-on type, it can be integrated with the conventional EPS system facilitating drivers intervention.

Predictive virtual lane using relative motions between a vehicle and lanes

In this paper, we propose a new lane estimation method to resolve poor detection performance of a camera system using the relative movement between a vehicle and lanes. In implementation of lane keep system (LKS) or lane change control (LXC), it is necessary to build a robust sensing system in obtaining the road information. When reliable road information is not available, we need virtual lane information to control the steering system. Thus we propose a predictive virtual lane (PVL) method to improve lane detection performance using the relative movement between a vehicle and lanes. The proposed PVL enhances the control performance of LKS/LXC for a while even for the detection failures of one side and/or both side lane marks. The performance of the proposed method was evaluated via simulations implemented with CarSim and Matlab/Simulink. Its performance was also validated with a test vehicle on highway system.

Sliding Mode Control for an Electric Power Steering System in an Autonomous Lane Keeping System

In this paper, we develop a sliding mode control for steering wheel angle control based on torque overlay in order to resolve the problem of previous methods for Electric Power Steering (EPS) systems in the Lane Keeping System (LKS) of autonomous vehicles. For the controller design, we propose a 2nd order model of the electric power steering system in an autonomous LKS. The desired state model is designed to prevent a rapid change of the steering wheel angle. The sliding mode steering wheel angle controller is developed for the robustness of the disturbance. Since the proposed method is designed based on torque overlay, torque integration with basic functions of the EPS system for the steering wheel angle control is available for the driver’s convenience. The performance of the proposed method was validated via experiments.

Asynchronous Sensor Fusion using Multi-rate Kalman Filter

We propose a multi-rate sensor fusion of vision and radar using Kalman filter to solve problems of asynchronized and multi-rate sampling periods in object vehicle tracking. A model based prediction of object vehicles is performed with a decentralized multi-rate Kalman filter for each sensor (vision and radar sensors.) To obtain the improvement in the performance of position prediction, different weighting is applied to each sensor’s predicted object position from the multi-rate Kalman filter. The proposed method can provide estimated position of the object vehicles at every sampling time of ECU. The Mahalanobis distance is used to make correspondence among the measured and predicted objects. Through the experimental results, we validate that the post-processed fusion data give us improved tracking performance. The proposed method obtained two times improvement in the object tracking performance compared to single sensor method (camera or radar sensor) in the view point of roots mean square error.

Model Predictive Control Using Dual Prediction Horizons for Lateral Control

Abstract In this paper, we present model predictive control having dual prediction horizons to reduce the length of prediction horizon and obtain the optimal solution rapidly. If prediction horizon is long, it is easy to get optimal solution while assuring closed-loop system stability. Realtime solution is, however, very difficult to calculate within the sample time because the system has complex formulations involving many constraints. On other hand, if prediction horizon is very short, computation overhead is reduced but the stability and performance of closed-loop system are not guaranteed. In this paper, the proposed method reduces the length of prediction horizon as well as maintains the stability and performance. The comparison of performances between the conventional method and the proposed control method are validated via simulations.

Lateral control for autonomous lane keeping system on highways

In this paper, we propose a lateral control scheme for autonomous lane keeping system on highways. Three main techniques are proposed, to improve the lane-keeping performance and and to reduce the ripple in the yaw rate on highways. First, we propose a virtual lane prediction method to cope with the momentary failure of lane detection. Second, we innovate an approach to steering wheel angle control based on torque overlay for the EPS of the LKS. Finally, the multi-rate lane-keeping control scheme is proposed to improve the lane-keeping performance and to reduce the ripple in the yaw rate. The performance of the proposed method was evaluated via experiments.