Lateral controller design for an unmanned vehicle via Kalman filtering

Lee, M. H.; Lee, K. S.; Park, H. G.; Cha, Y. C.; Kim, D. J.; Kim, B.; Hong, Seong-Soo; Chun, Ho Hwan

doi:10.1007/s12239-012-0080-x

Cited by 12 publications

(3 citation statements)

References 13 publications

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“…where Q y , Q ẏ and Q ṡ are weights. In the cost function (5), the first term penalises lateral deviations from the path, the second term promotes the vehicle's alignment with the path, and the third term penalises deviations from a target velocity V g . Finally, the optimised variables are defined by…”

Section: A Reference Generationmentioning

confidence: 99%

Fast Tube Model Predictive Control for Driverless Cars Using Linear Data-Driven Models

Vicente

Trodden

Anderson

2023

IEEE Trans. Contr. Syst. Technol.

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Model predictive control (MPC) has been widely applied to different aspects of autonomous driving, typically employing nonlinear physically derived models for prediction. However, feedback control systems inherently correct for model errors, thus in many applications it is sufficient to use a linear time-invariant (LTI) model for control design, especially when using robust control methods. This philosophy of approach appears to have been neglected in current driverless car research, and is the research gap that we aim to address here. Namely, instead of deriving meticulous nonlinear physical models of vehicle dynamics, and solving a correspondingly complex optimal control problem, we identify a low-order data-driven LTI model and handle its uncertainty via robust linear MPC methods. We develop a two-step control scheme for driverless cars based on tube MPC (TMPC), which introduces structural robustness, ensuring constraint compliance despite modelling error in the data-driven prediction model. Furthermore, we employ fast optimisation methods designed to exploit the special structure of the linear MPC problem. We evaluate the proposed control scheme using a vehicle model identified from real-world data, and simulations in IPGCarmaker, where the model of the vehicle under control is inherently nonlinear and uses detailed 3D physics. Our results show that an LTI model can be effectively employed for the task of lane-keeping, that TMPC can prevent lane departure and possible collisions due to model uncertainty, and that linear models allow for several algorithmic improvements that can decrease computation time by an order of magnitude compared to naive MPC implementations.

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Section: A Reference Generationmentioning

confidence: 99%

Fast Tube Model Predictive Control for Driverless Cars Using Linear Data-Driven Models

Vicente

Trodden

Anderson

2023

IEEE Trans. Contr. Syst. Technol.

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“…Chen 6 has proposed a target selection algorithm of ACC system to distinguish a valid target on the curve road. Lee et al 7 have presented a lateral system for an unmanned vehicle with the use of a proportional-derivative (PD) control. As a result, it is of great significance for improving or perfecting the control performances of ACC system to identify accurately the key target and avoid effectively rear-end collisions owing to the goal confusion or missing on the curve road and prejudge the dangerous changing-lane objects on the straight road.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy control for vehicle status estimation considering roll stability and its application in target recognition of automobile cruise system

Chen

Zhang

Yue-Zhou

et al. 2017

Advances in Mechanical Engineering

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In order to resolve the goal confusion or missing on curve road, a target recognition method on automobile cruise system is put forward based on filtering and fuzzy control theory. A vehicle model with 3 degrees of freedom on the yaw rate, sideslip angle, and body roll angle is discretized and an extended Kalman filter is proposed for estimating the road curvature. For the key target on the straight road, a multi-object changing-lane fuzzy controller is presented to prejudge the dangerous changing-lane objects. For the recognition of key target on the curve road, a recognition model is set up to calculate the lateral offset between the estimated road curvature radius and the target trajectory radius. In order to validate the feasibility of the key target recognition method, the simulation cases are given for multi-objects on the curve road and straight road adopting Carsim and MATLAB/Simulink co-simulation. The simulation results show that designed fuzzy controller has good robustness for prejudging the dangerous changing-lane objects on straight road. The extended Kalman filter has realized the efficient estimation on road curvature and the recognition model has identified the key target accurately to prevent the goal confusion or missing on curve road. The research results can offer the theory reference for improving or perfecting the control performances of automobile intelligent cruise system.

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“…[13][14][15] Also, more fundamentally, identification of the vehicle attitudes facilitates estimation of other vehicle states by increasing the vehicle model accuracy for the studies in which the roll and pitch effects are neglected. [16][17][18] The problem related to this field, however, is the need for a high-precision sensor to measure the required vehicle states. This is a critical hindrance for vehicles with a wide range of costs to have such technologies mounted and, without a solution, vehicle safety may only remain reachable for the affluent minority.…”

Section: Introductionmentioning

confidence: 99%

Vehicle roll and pitch angle estimation using a cost-effective six-dimensional inertial measurement unit

Choi

2012

Proceedings of the Institution of Mechanical Engineers, Part D:

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The purpose of this paper is to estimate accurately the vehicle attitudes, i.e. the vehicle roll and pitch angles. It is assumed that a set of data obtained from a low-price six-dimensional inertial measurement unit is available. This includes the linear acceleration of the vehicle and the angular rates of all axes. In addition, the observer exploits the data from the wheel speed sensors, and the steering-wheel angle, which are already available for recent production cars. Using the above, based on the combination of the velocity kinematics and pseudointegration of the angle kinematics, a novel scheme for reference angle selection dependent on the cornering-stiffness adaptation is adopted to observe the angles. The stability of each component of the proposed observer is investigated, and a set of assessments to confirm the performance of the entire system is arranged via experiments using a real production sport utility vehicle.

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Lateral controller design for an unmanned vehicle via Kalman filtering

Cited by 12 publications

References 13 publications

Fast Tube Model Predictive Control for Driverless Cars Using Linear Data-Driven Models

Fast Tube Model Predictive Control for Driverless Cars Using Linear Data-Driven Models

Fuzzy control for vehicle status estimation considering roll stability and its application in target recognition of automobile cruise system

Vehicle roll and pitch angle estimation using a cost-effective six-dimensional inertial measurement unit

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