Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019) |
| Pages | 2100-2111 |
| Number of pages | 12 |
| ISBN (electronic) | 0936406232, 9780936406237 |
| Publication status | Published - 2019 |
| Event | 32nd International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2019 - Miami, United States Duration: 16 Sept 2019 → 20 Sept 2019 |
Publication series
| Name | Proceedings of the Satellite Division's International Technical Meeting |
|---|---|
| Publisher | Institute of Navigation |
| ISSN (electronic) | 2331-5954 |
Abstract
In the last decade, Model Predictive Control has drawn much attention from both academia and industry to be a successful guidance and control algorithm in autonomous driving because of its ability to handle complex nonlinear constrained systems, especially with the rapidly expanding technologies that enable fast implementation of its optimization problem. This paper considers Non-Cooperative Distributed Nonlinear Model Predictive Control (NMPC) for simultaneous trajectory tracking and collision avoidance of connected autonomous/semi-autonomous vehicles. The connected vehicles are considered as a Network Control System (NCS) of dynamically decoupled agents with only coupling constraints, and it is formulated as a distributed Optimal Control Problem (OCP). It is assumed that the connected vehicles have a communication link to exchange their intentions. The coordination among the agents is achieved by Priority-Based techniques where a priority is assigned for each one to satisfy the prediction consistency. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize it with respect to a sequence of control inputs. The objective function of the OCP is to track the planned trajectory. In order to achieve a normal driving behavior, comfort driving, and provide consistency of the simplified kinematic model with the actual complex vehicle model, constraints are added to the control inputs and their rate of change. In order to achieve collision avoidance among the networked vehicles, a geometric shape must be selected to represent the vehicle geometry. In this paper, an elliptic disk is selected for that as it represents the geometry of the vehicle better that the traditional circular one. A separation condition between each pair of elliptic disks is formulated as time-varying state constraints for the OCP, and is proved by developing sufficient conditions for the separation. The algorithm is validated using MATLAB simulation with the aid of ACADO toolkit.
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Social Sciences(all)
- Communication
- Computer Science(all)
- Information Systems
- Engineering(all)
- Electrical and Electronic Engineering
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Computer Science Applications
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Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019). 2019. p. 2100-2111 (Proceedings of the Satellite Division's International Technical Meeting).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research
}
TY - GEN
T1 - Distributed Nonlinear Model Predictive Control for Connected Vehicles Trajectory Tracking and Collision Avoidance with Ellipse Geometry
AU - Abdelaal, Mohamed Elsayed Hasan
AU - Schön, Steffen
N1 - Funding Information: This work was supported by the German Research Foundation (DFG) as a part of the Research Collaboration in dynamic SENSor networks (i.c.sens) [GRK2159].
PY - 2019
Y1 - 2019
N2 - In the last decade, Model Predictive Control has drawn much attention from both academia and industry to be a successful guidance and control algorithm in autonomous driving because of its ability to handle complex nonlinear constrained systems, especially with the rapidly expanding technologies that enable fast implementation of its optimization problem. This paper considers Non-Cooperative Distributed Nonlinear Model Predictive Control (NMPC) for simultaneous trajectory tracking and collision avoidance of connected autonomous/semi-autonomous vehicles. The connected vehicles are considered as a Network Control System (NCS) of dynamically decoupled agents with only coupling constraints, and it is formulated as a distributed Optimal Control Problem (OCP). It is assumed that the connected vehicles have a communication link to exchange their intentions. The coordination among the agents is achieved by Priority-Based techniques where a priority is assigned for each one to satisfy the prediction consistency. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize it with respect to a sequence of control inputs. The objective function of the OCP is to track the planned trajectory. In order to achieve a normal driving behavior, comfort driving, and provide consistency of the simplified kinematic model with the actual complex vehicle model, constraints are added to the control inputs and their rate of change. In order to achieve collision avoidance among the networked vehicles, a geometric shape must be selected to represent the vehicle geometry. In this paper, an elliptic disk is selected for that as it represents the geometry of the vehicle better that the traditional circular one. A separation condition between each pair of elliptic disks is formulated as time-varying state constraints for the OCP, and is proved by developing sufficient conditions for the separation. The algorithm is validated using MATLAB simulation with the aid of ACADO toolkit.
AB - In the last decade, Model Predictive Control has drawn much attention from both academia and industry to be a successful guidance and control algorithm in autonomous driving because of its ability to handle complex nonlinear constrained systems, especially with the rapidly expanding technologies that enable fast implementation of its optimization problem. This paper considers Non-Cooperative Distributed Nonlinear Model Predictive Control (NMPC) for simultaneous trajectory tracking and collision avoidance of connected autonomous/semi-autonomous vehicles. The connected vehicles are considered as a Network Control System (NCS) of dynamically decoupled agents with only coupling constraints, and it is formulated as a distributed Optimal Control Problem (OCP). It is assumed that the connected vehicles have a communication link to exchange their intentions. The coordination among the agents is achieved by Priority-Based techniques where a priority is assigned for each one to satisfy the prediction consistency. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize it with respect to a sequence of control inputs. The objective function of the OCP is to track the planned trajectory. In order to achieve a normal driving behavior, comfort driving, and provide consistency of the simplified kinematic model with the actual complex vehicle model, constraints are added to the control inputs and their rate of change. In order to achieve collision avoidance among the networked vehicles, a geometric shape must be selected to represent the vehicle geometry. In this paper, an elliptic disk is selected for that as it represents the geometry of the vehicle better that the traditional circular one. A separation condition between each pair of elliptic disks is formulated as time-varying state constraints for the OCP, and is proved by developing sufficient conditions for the separation. The algorithm is validated using MATLAB simulation with the aid of ACADO toolkit.
UR - http://www.scopus.com/inward/record.url?scp=85075270385&partnerID=8YFLogxK
U2 - 10.33012/2019.16911
DO - 10.33012/2019.16911
M3 - Conference contribution
T3 - Proceedings of the Satellite Division's International Technical Meeting
SP - 2100
EP - 2111
BT - Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)
T2 - 32nd International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2019
Y2 - 16 September 2019 through 20 September 2019
ER -