| 1. |
- Jalalmaab, M., et al.
(författare)
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Guaranteeing persistent feasibility of model predictive motion planning for autonomous vehicles
- 2017
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Ingår i: 28th IEEE Intelligent Vehicles Symposium, IV 2017, Redondo Beach, United States, 11-14 June 2017. - 9781509048045 ; , s. 843-848
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Konferensbidrag (refereegranskat)abstract
- Model predictive control (MFC) approach is prone to loss of feasibility due to the limited prediction horizon for decision making. For autonomous vehicle motion planning, many of detected obstacles, which are beyond the prediction horizon, cannot be considered in the instantaneous decisions, and late consideration of them may cause infeasibility. The conditions that guarantee persistent feasibility of a model predictive motion planning scheme are studied in this paper. Maintaining the systems states in a control invariant set of the system guarantees the persistent feasibility of the corresponding MPC scheme. Therefore, the persistent feasibility concern can be expressed as the problem of computing an effective control invariant set of the system and maintaining the system states inside it. In this paper, two approaches are presented to compute control invariant sets for the motion planning problem, the linearization-convexification approach and the brute-force search approach. The control invariant sets calculated via these two approaches are numerically analyzed and compared.
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| 2. |
- Jalalmaab, M., et al.
(författare)
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Model predictive path planning with time-varying safety constraints for highway autonomous driving
- 2015
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Ingår i: 17th International Conference on Advanced Robotics, ICAR 2015, Istanbul, Turkey, 27-31 July 2015. - 9781467375092 ; , s. 213-217
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Konferensbidrag (refereegranskat)abstract
- A model predictive controller (MPC) with time-varying safety constraints for highway path planning with collision avoidance is developed in this paper. The collision avoidance constraints with the surrounding vehicles are integrated with the road geometry constraints, leading to a set of convex time-varying safety constraints. The surrounding vehicles' dynamics and decisions are also incorporated in the prediction model to predict the trajectories. The proposed controller finds the best combination of longitudinal and lateral acceleration commands to guide the vehicle while avoiding collision with surrounding vehicles by possibly overtaking. Simulation results verify successful performance of this predictive control strategy for different scenarios.
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| 3. |
- Pirani, Mohammad, et al.
(författare)
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Resilient Estimation and Control on k-Nearest Neighbor Platoons : A Network-Theoretic Approach
- 2018
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Ingår i: IFAC-PapersOnLine. - : Elsevier B.V.. ; , s. 22-27
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Konferensbidrag (refereegranskat)abstract
- This paper is concerned with the network-theoretic properties of so-called k-nearest neighbor intelligent vehicular platoons, where each vehicle communicates with k vehicles, both in front and behind. The network-theoretic properties analyzed in this paper play major roles in quantifying the resilience and robustness of three generic distributed estimation and control algorithms against communication failures and disturbances, namely resilient distributed estimation, resilient distributed consensus, and robust network formation. Based on the results for the connectivity measures of the k-nearest neighbor platoon, we show that extending the traditional platooning topologies (which were only based on interacting with nearest neighbors) to k-nearest neighbor platoons increases the resilience of distributed estimation and control algorithms to both communication failures and disturbances.
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| 4. |
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