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2020 – today
- 2024
[j23]Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson:
Uncertainty quantification in neural network classifiers - A local linear approach. Autom. 163: 111563 (2024)- [j22]Magnus Malmström
, Anton Kullberg, Isaac Skog, Daniel Axehill, Fredrik Gustafsson:
Extended Target Tracking Utilizing Machine-Learning Software-With Applications to Animal Classification. IEEE Signal Process. Lett. 31: 376-380 (2024) - [j21]Daniel Arnström, David Broman, Daniel Axehill:
Exact Worst-Case Execution-Time Analysis for Implicit Model Predictive Control. IEEE Trans. Autom. Control. 69(10): 7190-7196 (2024) - [j20]Abhishek Dhar, Carl Hynén Ulfsjöö, Johan Löfberg, Daniel Axehill:
Disturbance-Parametrized Robust Lattice-Based Motion Planning. IEEE Trans. Intell. Veh. 9(1): 3034-3046 (2024) - [c41]Anja Hellander, Kristoffer Bergman, Daniel Axehill:
Improved Task and Motion Planning for Rearrangement Problems using Optimal Control*. IV 2024: 2033-2040 - [i14]Daniel Arnström, Daniel Axehill:
A High-Performant Multi-Parametric Quadratic Programming Solver. CoRR abs/2404.05511 (2024) - 2023
- [c40]Shamisa Shoja, Daniel Axehill:
Exact Complexity Certification of Start Heuristics in Branch-and-Bound Methods for Mixed-Integer Linear Programming. CDC 2023: 2292-2299 - [c39]Anja Hellander, Kristoffer Bergman, Daniel Axehill:
On Integrated Optimal Task and Motion Planning for a Tractor-Trailer Rearrangement Problem. CDC 2023: 6116-6123 - [i13]Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson:
Uncertainty quantification in neural network classifiers - a local linear approach. CoRR abs/2303.07114 (2023) - [i12]Daniel Arnström, David Broman, Daniel Axehill:
Exact Worst-Case Execution-Time Analysis for Implicit Model Predictive Control. CoRR abs/2304.11576 (2023) - [i11]Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson:
On the validity of using the delta method for calculating the uncertainty of the predictions from an overparameterized model. CoRR abs/2307.01031 (2023) - [i10]Magnus Malmström, Anton Kullberg, Isaac Skog, Daniel Axehill, Fredrik Gustafsson:
Extended target tracking utilizing machine-learning software - with applications to animal classification. CoRR abs/2310.08316 (2023) - 2022
- [j19]Daniel Arnström, Alberto Bemporad, Daniel Axehill:
A Linear Programming Method Based on Proximal-Point Iterations With Applications to Multi-Parametric Programming. IEEE Control. Syst. Lett. 6: 2066-2071 (2022) - [j18]Daniel Arnström, Daniel Axehill:
A Unifying Complexity Certification Framework for Active-Set Methods for Convex Quadratic Programming. IEEE Trans. Autom. Control. 67(6): 2758-2770 (2022) - [j17]Daniel Arnström, Alberto Bemporad, Daniel Axehill:
A Dual Active-Set Solver for Embedded Quadratic Programming Using Recursive LDL$^{T}$ Updates. IEEE Trans. Autom. Control. 67(8): 4362-4369 (2022) - [j16]Per Boström-Rost, Daniel Axehill, Gustaf Hendeby:
PMBM Filter With Partially Grid-Based Birth Model With Applications in Sensor Management. IEEE Trans. Aerosp. Electron. Syst. 58(1): 530-540 (2022) - [c38]Shamisa Shoja, Daniel Arnström, Daniel Axehill:
Overall Complexity Certification of a Standard Branch and Bound Method for Mixed-Integer Quadratic Programming. ACC 2022: 4957-4964 - [c37]Anja Hellander, Daniel Axehill:
On a Traveling Salesman Problem with Dynamic Obstacles and Integrated Motion Planning. ACC 2022: 4965-4972 - [c36]Daniel Arnström, Daniel Axehill:
Lift, Partition, and Project: Parametric Complexity Certification of Active-Set QP Methods in the Presence of Numerical Errors. CDC 2022: 4381-4387 - [c35]Shamisa Shoja, Daniel Arnström, Daniel Axehill:
Exact Complexity Certification of a Standard Branch and Bound Method for Mixed-Integer Linear Programming. CDC 2022: 6298-6305 - [c34]Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson:
Detection of outliers in classification by using quantified uncertainty in neural networks. FUSION 2022: 1-7 - [c33]Jonas Nordlöf, Gustaf Hendeby, Daniel Axehill:
LiDAR-Landmark Modeling for Belief-Space Planning using Aerial Forest Data. FUSION 2022: 1-8 - [c32]Carl Hynén Ulfsjöö, Daniel Axehill:
On Integrating POMDP and Scenario MPC for Planning under Uncertainty - with Applications to Highway Driving. IV 2022: 1152-1160 - [i9]Shamisa Shoja, Daniel Arnström, Daniel Axehill:
Overall Complexity Certification of a Standard Branch and Bound Method for Mixed-Integer Quadratic Programming. CoRR abs/2204.02171 (2022) - [i8]Abhishek Dhar, Carl Hynén, Johan Löfberg, Daniel Axehill:
Robust Lattice-based Motion Planning. CoRR abs/2209.14360 (2022) - 2021
- [j15]Daniel Arnström, Alberto Bemporad, Daniel Axehill:
Complexity Certification of Proximal-Point Methods for Numerically Stable Quadratic Programming. IEEE Control. Syst. Lett. 5(4): 1381-1386 (2021) - [j14]Per Boström-Rost, Daniel Axehill, Gustaf Hendeby:
Sensor Management for Search and Track Using the Poisson Multi-Bernoulli Mixture Filter. IEEE Trans. Aerosp. Electron. Syst. 57(5): 2771-2783 (2021) - [j13]Kristoffer Bergman, Oskar Ljungqvist, Daniel Axehill:
Improved Path Planning by Tightly Combining Lattice-Based Path Planning and Optimal Control. IEEE Trans. Intell. Veh. 6(1): 57-66 (2021) - [c31]Daniel Arnström, Alberto Bemporad, Daniel Axehill:
Complexity Certification of Proximal-Point Methods for Numerically Stable Quadratic Programming. ACC 2021: 947-952 - [c30]Daniel Arnström, Daniel Axehill:
Semi-Explicit Linear MPC Using a Warm-Started Active-Set QP Algorithm with Exact Complexity Guarantees. CDC 2021: 2557-2562 - [c29]Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson:
Modeling of the tire-road friction using neural networks including quantification of the prediction uncertainty. FUSION 2021: 1-6 - [c28]Jonas Nordlöf, Gustaf Hendeby, Daniel Axehill:
Improved Virtual Landmark Approximation for Belief-Space Planning. FUSION 2021: 1-8 - [i7]Daniel Arnström, Daniel Axehill:
An Efficient Dual Active-Set Solver for Embedded Model Predictive Control. CoRR abs/2103.16236 (2021) - 2020
- [j12]Daniel Arnström, Alberto Bemporad, Daniel Axehill:
Exact Complexity Certification of a Nonnegative Least-Squares Method for Quadratic Programming. IEEE Control. Syst. Lett. 4(4): 1036-1041 (2020) - [c27]Kristoffer Bergman, Oskar Ljungqvist, Jonas Linder, Daniel Axehill:
An Optimization-Based Motion Planner for Autonomous Maneuvering of Marine Vessels in Complex Environments. CDC 2020: 5283-5290 - [c26]Jonas Nordlöf, Gustaf Hendeby, Daniel Axehill:
Belief Space Planning using Landmark Density Information. FUSION 2020: 1-8 - [c25]Oskar Ljungqvist, Daniel Axehill, Henrik Pettersson:
On sensing-aware model predictive path-following control for a reversing general 2-trailer with a car-like tractor. ICRA 2020: 8813-8819 - [i6]Oskar Ljungqvist, Daniel Axehill, Henrik Pettersson:
On sensing-aware model predictive path-following control for a reversing general 2-trailer with a car-like tractor. CoRR abs/2002.06874 (2020) - [i5]Oskar Ljungqvist, Daniel Axehill, Henrik Pettersson, Johan Löfberg:
Estimation-aware model predictive path-following control for a general 2-trailer with a car-like tractor. CoRR abs/2002.10291 (2020)
2010 – 2019
- 2019
- [j11]Oskar Ljungqvist, Niclas Evestedt, Daniel Axehill, Marcello Cirillo, Henrik Pettersson:
A path planning and path-following control framework for a general 2-trailer with a car-like tractor. J. Field Robotics 36(8): 1345-1377 (2019) - [j10]Isak Nielsen, Daniel Axehill:
Direct Parallel Computations of Second-Order Search Directions for Model Predictive Control. IEEE Trans. Autom. Control. 64(7): 2845-2860 (2019) - [c24]Daniel Arnström, Daniel Axehill:
Exact Complexity Certification of a Standard Primal Active-Set Method for Quadratic Programming. CDC 2019: 4317-4324 - [c23]Per Boström-Rost, Daniel Axehill, Gustaf Hendeby:
Informative Path Planning in the Presence of Adversarial Observers. FUSION 2019: 1-7 - [c22]Kristoffer Bergman, Oskar Ljungqvist, Daniel Axehill:
Improved Optimization of Motion Primitives for Motion Planning in State Lattices. IV 2019: 2307-2314 - [i4]Oskar Ljungqvist, Niclas Evestedt, Daniel Axehill, Marcello Cirillo, Henrik Pettersson:
A path planning and path-following control framework for a general 2-trailer with a car-like tractor. CoRR abs/1904.01651 (2019) - [i3]Oskar Ljungqvist, Daniel Axehill:
A predictive path-following controller for multi-steered articulated vehicles. CoRR abs/1912.06259 (2019) - [i2]Oskar Ljungqvist, Kristoffer Bergman, Daniel Axehill:
Optimization-based motion planning for multi-steered articulated vehicles. CoRR abs/1912.06264 (2019) - 2018
- [j9]Per Boström-Rost, Daniel Axehill, Gustaf Hendeby:
On Global Optimization for Informative Path Planning. IEEE Control. Syst. Lett. 2(4): 833-838 (2018) - [j8]Isak Nielsen, Daniel Axehill:
Low-Rank Modifications of Riccati Factorizations for Model Predictive Control. IEEE Trans. Autom. Control. 63(3): 872-879 (2018) - [c21]Oskar Ljungqvist, Daniel Axehill, Johan Löfberg:
On stability for state-lattice trajectory tracking control. ACC 2018: 5868-5875 - [c20]Olov Andersson, Oskar Ljungqvist, Mattias Tiger, Daniel Axehill, Fredrik Heintz:
Receding-Horizon Lattice-Based Motion Planning with Dynamic Obstacle Avoidance. CDC 2018: 4467-4474 - [c19]Kristoffer Bergman, Daniel Axehill:
Combining Homotopy Methods and Numerical Optimal Control to Solve Motion Planning Problems. Intelligent Vehicles Symposium 2018: 347-354 - 2017
- [j7]Erik Ward, Niclas Evestedt, Daniel Axehill, John Folkesson:
Probabilistic Model for Interaction Aware Planning in Merge Scenarios. IEEE Trans. Intell. Veh. 2(2): 133-146 (2017) - [c18]Oskar Ljungqvist, Niclas Evestedt, Marcello Cirillo, Daniel Axehill, Olov Holmer:
Lattice-based motion planning for a general 2-trailer system. Intelligent Vehicles Symposium 2017: 819-824 - 2016
- [c17]Oskar Ljungqvist, Daniel Axehill, Anders Helmersson:
Path following control for a reversing general 2-trailer system. CDC 2016: 2455-2461 - [c16]Isak Nielsen, Daniel Axehill:
Reduced memory footprint in multiparametric quadratic programming by exploiting low rank structure. CDC 2016: 3654-3661 - [c15]Isak Nielsen, Daniel Axehill:
An O (log N) parallel algorithm for newton step computations with applications to moving horizon estimation. ECC 2016: 1630-1636 - [c14]Niclas Evestedt, Oskar Ljungqvist, Daniel Axehill:
Motion planning for a reversing general 2-trailer configuration using Closed-Loop RRT. IROS 2016: 3690-3697 - [c13]Niclas Evestedt, Erik Ward, John Folkesson, Daniel Axehill:
Interaction aware trajectory planning for merge scenarios in congested traffic situations. ITSC 2016: 465-472 - [c12]Niclas Evestedt, Oskar Ljungqvist, Daniel Axehill:
Path tracking and stabilization for a reversing general 2-trailer configuration using a cascaded control approach. Intelligent Vehicles Symposium 2016: 1156-1161 - [i1]Niclas Evestedt, Oskar Ljungqvist, Daniel Axehill:
Path tracking and stabilization for a reversing general 2-trailer configuration using a cascaded control approach. CoRR abs/1602.06675 (2016) - 2015
- [j6]Daniel Axehill:
Controlling the level of sparsity in MPC. Syst. Control. Lett. 76: 1-7 (2015) - [j5]Alexander Fuchs, Daniel Axehill, Manfred Morari:
Lifted Evaluation of mp-MIQP Solutions. IEEE Trans. Autom. Control. 60(12): 3328-3331 (2015) - [c11]Isak Nielsen, Daniel Axehill:
A parallel structure exploiting factorization algorithm with applications to Model Predictive Control. CDC 2015: 3932-3938 - [c10]Martin A. Skoglund, Gustaf Hendeby, Daniel Axehill:
Extended Kalman filter modifications based on an optimization view point. FUSION 2015: 1856-1861 - [c9]Niclas Evestedt, Daniel Axehill, Marco Trincavelli, Fredrik Gustafsson:
Sampling recovery for closed loop rapidly expanding random tree using brake profile regeneration. Intelligent Vehicles Symposium 2015: 101-106 - 2014
- [j4]Daniel Axehill, Thomas Besselmann, Davide Martino Raimondo, Manfred Morari:
A parametric branch and bound approach to suboptimal explicit hybrid MPC. Autom. 50(1): 240-246 (2014) - 2013
- [c8]Isak Nielsen, Daniel Ankelhed, Daniel Axehill:
Low-rank modifications of Riccati factorizations with applications to Model Predictive Control. CDC 2013: 3684-3690 - 2012
- [j3]Daniel Axehill, Manfred Morari:
An alternative use of the Riccati recursion for efficient optimization. Syst. Control. Lett. 61(1): 37-40 (2012) - 2011
- [c7]Jan Siroky, Miroslav Simandl, Daniel Axehill, Ivo Puncochár:
An optimization approach to resolve the competing aims of active fault detection and control. CDC/ECC 2011: 3712-3717 - 2010
- [j2]Daniel Axehill, Lieven Vandenberghe, Anders Hansson:
Convex relaxations for mixed integer predictive control. Autom. 46(9): 1540-1545 (2010) - [c6]Daniel Axehill, Manfred Morari:
Improved complexity analysis of branch and bound for hybrid MPC. CDC 2010: 4216-4222 - [c5]Alexander N. Fuchs, Daniel Axehill, Manfred Morari:
On the choice of the linear decision functions for point location in polytopic data sets - Application to Explicit MPC. CDC 2010: 5283-5288
2000 – 2009
- 2008
- [b1]Daniel Axehill:
Integer Quadratic Programming for Control and Communication. Linköping University, Sweden, 2008 - [j1]Daniel Axehill, Fredrik Gunnarsson, Anders Hansson:
A Low-Complexity High-Performance Preprocessing Algorithm for Multiuser Detection Using Gold Sequences. IEEE Trans. Signal Process. 56(9): 4377-4385 (2008) - [c4]Daniel Axehill, Anders Hansson:
A dual gradient projection quadratic programming algorithm tailored for model predictive control. CDC 2008: 3057-3064 - 2007
- [c3]Daniel Axehill, Anders Hansson, Lieven Vandenberghe:
Relaxations applicable to mixed integer predictive control comparisons and efficient computations. CDC 2007: 4103-4109 - 2006
- [c2]Daniel Axehill, Anders Hansson:
A Mixed Integer Dual Quadratic Programming Algorithm Tailored for MPC. CDC 2006: 5693-5698 - 2004
- [c1]Daniel Axehill, Anders Hansson:
A preprocessing algorithm for MIQP solvers with applications to MPC. CDC 2004: 2497-2502
Coauthor Index
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last updated on 2024-10-23 20:32 CEST by the dblp team
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