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Axehill, Daniel, Biträdande professorORCID iD iconorcid.org/0000-0001-6957-2603
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Publikasjoner (10 av 47) Visa alla publikasjoner
Boström-Rost, P., Axehill, D. & Hendeby, G. (2019). Informative Path Planning for Active Tracking of Agile Targets. In: 2019 IEEE Aerospace Conference: . Paper presented at Proceedings of 2019 IEEE Aerospace Conference, Big Sky, MT, USA, March 3-8, 2019 (pp. 1-11). Institute of Electrical and Electronics Engineers (IEEE), Article ID 06.0701.
Åpne denne publikasjonen i ny fane eller vindu >>Informative Path Planning for Active Tracking of Agile Targets
2019 (engelsk)Inngår i: 2019 IEEE Aerospace Conference, Institute of Electrical and Electronics Engineers (IEEE), 2019, s. 1-11, artikkel-id 06.0701Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper proposes a method to generate informative trajectories for a mobile sensor that tracks agile targets.The goal is to generate a sensor trajectory that maximizes the tracking performance, captured by a measure of the covariance matrix of the target state estimate. The considered problem is acombination of estimation and control, and is often referred to as informative path planning (IPP). When using nonlinear sensors, the tracking performance depends on the actual measurements, which are naturally unavailable in the planning stage.The planning problem hence becomes a stochastic optimization problem, where the expected tracking performance is used inthe objective function. The main contribution of this work is anapproximation of the problem based on deterministic sampling of the predicted target distribution. This is in contrast to prior work, where only the most likely target trajectory is considered.It is shown that the proposed method greatly improves the ability to track agile targets, compared to a baseline approach.   

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2019
Emneord
Informative Path Planning; Target Tracking; Sensor Management; Stochastic Control
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-155035 (URN)10.1109/AERO.2019.8741840 (DOI)9781538668542 (ISBN)9781538668559 (ISBN)
Konferanse
Proceedings of 2019 IEEE Aerospace Conference, Big Sky, MT, USA, March 3-8, 2019
Prosjekter
WASP
Forskningsfinansiär
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Tilgjengelig fra: 2019-03-09 Laget: 2019-03-09 Sist oppdatert: 2019-08-12bibliografisk kontrollert
Nielsen, I. & Axehill, D. (2018). Low-Rank Modifications of Riccati Factorizations for Model Predictive Control. IEEE Transactions on Automatic Control, 63(3), 872-879
Åpne denne publikasjonen i ny fane eller vindu >>Low-Rank Modifications of Riccati Factorizations for Model Predictive Control
2018 (engelsk)Inngår i: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 63, nr 3, s. 872-879Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In Model Predictive Control (MPC), the control input is computed by solving a constrained finite-time optimal control (CFTOC) problem at each sample in the control loop. The main computational effort when solving the CFTOC problem using an active-set (AS) method is often spent on computing the search directions, which in MPC corresponds to solving unconstrained finite-time optimal control (UFTOC) problems. This is commonly performed using Riccati recursions or generic sparsity exploiting algorithms. In this work the focus is efficient search direction computations for AS type methods. The system of equations to be solved at each AS iteration is changed only by a low-rank modification of the previous one, and exploiting this structured change is important for the performance of AS type solvers. In this paper, theory for how to exploit these low-rank changes by modifying the Riccati factorization between AS iterations in a structured way is presented. A numerical evaluation of the proposed algorithm shows that the computation time can be significantly reduced by modifying, instead of re-computing, the Riccati factorization. This speed-up can be important for AS type solvers used for linear, nonlinear and hybrid MPC.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2018
Emneord
Indexes; Linear matrix inequalities; Optimal control; Optimization; Predictive control; Search problems; Sparse matrices; MPC; Riccati recursion; low-rank; optimization
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-142894 (URN)10.1109/TAC.2017.2737228 (DOI)000426276500025 ()
Tilgjengelig fra: 2017-11-09 Laget: 2017-11-09 Sist oppdatert: 2018-03-20bibliografisk kontrollert
Ljungqvist, O., Axehill, D. & Löfberg, J. (2018). On stability for state-lattice trajectory tracking control. In: 2018 Annual American Control Conference (ACC): . Paper presented at 2018 Annual American Control Conference (ACC) June 27–29, 2018. Wisconsin Center, Milwaukee, USA (pp. 5868-5875). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>On stability for state-lattice trajectory tracking control
2018 (engelsk)Inngår i: 2018 Annual American Control Conference (ACC), IEEE, 2018, s. 5868-5875Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In order to guarantee that a self-driving vehicle is behaving as expected, stability of the closed-loop system needs to be rigorously analyzed. The key components for the lowest levels of control in self-driving vehicles are the controlled vehicle, the low-level controller and the local planner.The local planner that is considered in this work constructs a feasible trajectory by combining a finite number of precomputed motions. When this local planner is considered, we show that the closed-loop system can be modeled as a nonlinear hybrid system. Based on this, we propose a novel method for analyzing the behavior of the tracking error, how to design the low-level controller and how to potentially impose constraints on the local planner, in order to guarantee that the tracking error is bounded and decays towards zero. The proposed method is applied on a truck and trailer system and the results are illustrated in two simulation examples.

sted, utgiver, år, opplag, sider
IEEE, 2018
Serie
American Control Conference (ACC), E-ISSN 2378-5861
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-152455 (URN)10.23919/ACC.2018.8430822 (DOI)978-1-5386-5428-6 (ISBN)978-1-5386-5427-9 (ISBN)978-1-5386-5429-3 (ISBN)
Konferanse
2018 Annual American Control Conference (ACC) June 27–29, 2018. Wisconsin Center, Milwaukee, USA
Tilgjengelig fra: 2018-11-01 Laget: 2018-11-01 Sist oppdatert: 2019-01-17
Andersson, O., Ljungqvist, O., Tiger, M., Axehill, D. & Heintz, F. (2018). Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance. In: 2018 IEEE Conference on Decision and Control (CDC): . Paper presented at 2018 IEEE 57th Annual Conference on Decision and Control (CDC),17-19 December, Miami, Florida, USA (pp. 4467-4474). Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance
Vise andre…
2018 (engelsk)Inngår i: 2018 IEEE Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 4467-4474Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

A key requirement of autonomous vehicles is the capability to safely navigate in their environment. However, outside of controlled environments, safe navigation is a very difficult problem. In particular, the real-world often contains both complex 3D structure, and dynamic obstacles such as people or other vehicles. Dynamic obstacles are particularly challenging, as a principled solution requires planning trajectories with regard to both vehicle dynamics, and the motion of the obstacles. Additionally, the real-time requirements imposed by obstacle motion, coupled with real-world computational limitations, make classical optimality and completeness guarantees difficult to satisfy. We present a unified optimization-based motion planning and control solution, that can navigate in the presence of both static and dynamic obstacles. By combining optimal and receding-horizon control, with temporal multi-resolution lattices, we can precompute optimal motion primitives, and allow real-time planning of physically-feasible trajectories in complex environments with dynamic obstacles. We demonstrate the framework by solving difficult indoor 3D quadcopter navigation scenarios, where it is necessary to plan in time. Including waiting on, and taking detours around, the motions of other people and quadcopters.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2018
Serie
Conference on Decision and Control (CDC), ISSN 2576-2370 ; 2018
Emneord
Motion Planning, Optimal Control, Autonomous System, UAV, Dynamic Obstacle Avoidance, Robotics
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-152131 (URN)10.1109/CDC.2018.8618964 (DOI)9781538613955 (ISBN)9781538613948 (ISBN)9781538613962 (ISBN)
Konferanse
2018 IEEE 57th Annual Conference on Decision and Control (CDC),17-19 December, Miami, Florida, USA
Forskningsfinansiär
VINNOVAKnut and Alice Wallenberg FoundationSwedish Foundation for Strategic Research ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsSwedish Research CouncilLinnaeus research environment CADICSCUGS (National Graduate School in Computer Science)
Merknad

This work was partially supported by FFI/VINNOVA, the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation, the Swedish Foundation for Strategic Research (SSF) project Symbicloud, the ELLIIT Excellence Center at Linköping-Lund for Information Technology, Swedish Research Council (VR) Linnaeus Center CADICS, and the National Graduate School in Computer Science, Sweden (CUGS).

Tilgjengelig fra: 2018-10-18 Laget: 2018-10-18 Sist oppdatert: 2019-01-30bibliografisk kontrollert
Ward, E., Evestedt, N., Axehill, D. & Folkesson, J. (2017). Probabilistic Model for Interaction Aware Planning in Merge Scenarios. IEEE Transactions on Intelligent Vehicles, 2(2), 133-146
Åpne denne publikasjonen i ny fane eller vindu >>Probabilistic Model for Interaction Aware Planning in Merge Scenarios
2017 (engelsk)Inngår i: IEEE Transactions on Intelligent Vehicles, ISSN 2379-8858, Vol. 2, nr 2, s. 133-146Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Merge scenarios confront drivers with some of the most complicated driving maneuvers in every day driving, requiring anticipatory reasoning of positions of other vehicles, and the own vehicles future trajectory. In congested traffic it might be impossible to merge without cooperation of up-stream vehicles, therefore, it is essential to gauge the effect of our own trajectory when planning a merge maneuver. For an autonomous vehicle to perform a merge maneuver in congested traffic similar capabilities are required. This includes a model describing the future evolution of the scene that allows for optimizing the autonomous vehicle's planned trajectory with respect to risk, comfort, and dynamical limitations. We present a probabilistic model that explicitly models interaction between vehicles and allows for evaluating the utility of a large number of candidate trajectories of an autonomous vehicle using a receding horizon approach in order to select an appropriate merge maneuver. The model is an extension of the intelligent driver model and the modeled behavior of other vehicles are adjusted using on-line model parameter estimation in order to give better predictions. The prediction model is evaluated using naturalistic traffic data and the merge maneuver planner is evaluated in simulation.

sted, utgiver, år, opplag, sider
IEEE, 2017
Emneord
Predictive models, Planning, Vehicles, Probabilistic logic
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-147067 (URN)10.1109/TIV.2017.2730588 (DOI)
Forskningsfinansiär
VINNOVA
Tilgjengelig fra: 2018-04-10 Laget: 2018-04-10 Sist oppdatert: 2018-04-20bibliografisk kontrollert
Nielsen, I. & Axehill, D. (2016). An O(log N) Parallel Algorithm for Newton Step Computations with Applications to Moving Horizon Estimation. In: Proceedings of the 2016 European Control Conference: . Paper presented at 2016 European Control Conference, Aalborg, Denmark, June 29 - July 1, 2016. (pp. 1630-1636).
Åpne denne publikasjonen i ny fane eller vindu >>An O(log N) Parallel Algorithm for Newton Step Computations with Applications to Moving Horizon Estimation
2016 (engelsk)Inngår i: Proceedings of the 2016 European Control Conference, 2016, s. 1630-1636Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In Moving Horizon Estimation (MHE) the computed estimate is found by solving a constrained finite-time optimal estimation problem in real-time at each sample in a receding horizon fashion. The constrained estimation problem can be solved by, e.g., interior-point (IP) or active-set (AS) methods, where the main computational effort in both methods is known to be the computation of the search direction, i.e., the Newton step. This is often done using generic sparsity exploiting algorithms or serial Riccati recursions, but as parallel hardware is becoming more commonly available the need for parallel algorithms for computing the Newton step is increasing. In this paper a newly developed tailored, non-iterative parallel algorithm for computing the Newton step using the Riccati recursion for Model Predictive Control (MPC) is extended to MHE problems. The algorithm exploits the special structure of the Karush-Kuhn-Tucker system for the optimal estimation problem. As a result it is possible to obtain logarithmic complexity growth in the estimation horizon length, which can be used to reduce the computation time for IP and AS methods when applied to what is today considered as challenging estimation problems. Furthermore, promising numerical results have been obtained using an ANSI-C implementation of the proposed algorithm, which uses Message Passing Interface (MPI) together with InfiniBand and is executed on true parallel hardware. Beyond MHE, due to similarities in the problem structure, the algorithm can be applied to various forms of on-line and off-line smoothing problems.

Emneord
Parallel MHE, Moving Horizon Estimation, Riccati Recursion
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-129995 (URN)10.1109/ECC.2016.7810524 (DOI)000392695300271 ()978-1-5090-2591-6 (ISBN)978-1-5090-2590-9 (ISBN)
Konferanse
2016 European Control Conference, Aalborg, Denmark, June 29 - July 1, 2016.
Tilgjengelig fra: 2016-07-04 Laget: 2016-07-04 Sist oppdatert: 2017-08-09
Evestedt, N., Ljungqvist, O. & Axehill, D. (2016). Path tracking and stabilization for a reversing general 2-trailer configuration using a cascaded control approach. In: Intelligent Vehicles Symposium (IV), 2016 IEEE: . Paper presented at 2016 IEEE Intelligent Vehicles Symposium, Gothenburg, Sweden, June 19-22, 2016 (pp. 1156-1161). Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>Path tracking and stabilization for a reversing general 2-trailer configuration using a cascaded control approach
2016 (engelsk)Inngår i: Intelligent Vehicles Symposium (IV), 2016 IEEE, Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 1156-1161Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In this paper a cascaded approach for stabilizationand path tracking of a general 2-trailer vehicle configurationwith an off-axle hitching is presented. A low level LinearQuadratic controller is used for stabilization of the internalangles while a pure pursuit path tracking controller is used ona higher level to handle the path tracking. Piecewise linearityis the only requirement on the control reference which makesthe design of reference paths very general. A Graphical UserInterface is designed to make it easy for a user to design controlreferences for complex manoeuvres given some representationof the surroundings. The approach is demonstrated with challengingpath following scenarios both in simulation and on asmall scale test platform.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2016
Emneord
cascade control, control system synthesis, graphical user interfaces, linear quadratic control, mobile robot, path planning, piecewise linear techniques
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-130950 (URN)10.1109/IVS.2016.7535535 (DOI)000390845600183 ()978-1-5090-1821-5 (ISBN)978-1-5090-1822-2 (ISBN)
Konferanse
2016 IEEE Intelligent Vehicles Symposium, Gothenburg, Sweden, June 19-22, 2016
Prosjekter
iQMatic
Forskningsfinansiär
VINNOVA
Tilgjengelig fra: 2016-09-01 Laget: 2016-09-01 Sist oppdatert: 2019-01-17bibliografisk kontrollert
Axehill, D. (2015). Controlling the level of sparsity in MPC. Systems & control letters (Print), 76, 1-7
Åpne denne publikasjonen i ny fane eller vindu >>Controlling the level of sparsity in MPC
2015 (engelsk)Inngår i: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, Vol. 76, s. 1-7Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In optimization algorithms used for on-line Model Predictive Control (MPC), linear systems of equations are often solved in each iteration. This is true both for Active Set methods as well as for Interior Point methods, and for linear MPC as well as for nonlinear MPC and hybrid MPC. The main computational effort is spent while solving these linear systems of equations, and hence, it is of greatest interest to solve them efficiently. Classically, the optimization problem has been formulated in either of two ways. One leading to a sparse linear system of equations involving relatively many variables to compute in each iteration and another one leading to a dense linear system of equations involving relatively few variables. In this work, it is shown that it is possible not only to consider these two distinct choices of formulations. Instead it is shown that it is possible to create an entire family of formulations with different levels of sparsity and number of variables, and that this extra degree of freedom can be exploited to obtain even better performance with the software and hardware at hand. This result also provides a better answer to a recurring question in MPC; should the sparse or dense formulation be used.

Emneord
Predictive control, Optimization, Riccati recursion, Sparsity
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-113276 (URN)10.1016/j.sysconle.2014.12.002 (DOI)000349733500001 ()
Tilgjengelig fra: 2015-01-14 Laget: 2015-01-14 Sist oppdatert: 2017-12-05bibliografisk kontrollert
Skoglund, M., Hendeby, G. & Axehill, D. (2015). Extended Kalman Filter Modifications Based on an Optimization View Point. In: 18th International Conference of Information Fusion: . Paper presented at 18th International Conference of Information Fusion, Washington, D.C., USA, July 6-9, 2015. Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>Extended Kalman Filter Modifications Based on an Optimization View Point
2015 (engelsk)Inngår i: 18th International Conference of Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2015Konferansepaper (Fagfellevurdert)
Abstract [en]

The extended Kalman filter (EKF) has been animportant tool for state estimation of nonlinear systems sinceits introduction. However, the EKF does not possess the same optimality properties as the Kalman filter, and may perform poorly. By viewing the EKF as an optimization problem it is possible to, in many cases, improve its performance and robustness. The paper derives three variations of the EKF by applying different optimisation algorithms to the EKF costfunction and relate these to the iterated EKF. The derived filters are evaluated in two simulation studies which exemplify the presented filters.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2015
Emneord
extended Kalman filter, optimization, iterated extended Kalman filter
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-120383 (URN)978-098244386-6 (ISBN)
Konferanse
18th International Conference of Information Fusion, Washington, D.C., USA, July 6-9, 2015
Prosjekter
Scalable Kalman Filters
Forskningsfinansiär
VINNOVA, LINK-SICSwedish Research CouncilSecurity Link
Tilgjengelig fra: 2015-08-03 Laget: 2015-08-03 Sist oppdatert: 2016-08-31
Fuchs, A., Axehill, D. & Morari, M. (2015). Lifted Evaluation of mp-MIQP Solutions. IEEE Transactions on Automatic Control, 60(12), 3328-3331
Åpne denne publikasjonen i ny fane eller vindu >>Lifted Evaluation of mp-MIQP Solutions
2015 (engelsk)Inngår i: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 60, nr 12, s. 3328-3331Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This note presents an efficient approach for the evaluation of multi-parametric mixed integer quadratic programming (mp-MIQP) solutions, occurring for instance in control problems involving discrete time hybrid systems with quadratic cost. Traditionally, the online evaluation requires a sequential comparison of piecewise quadratic value functions. We introduce a lifted parameter space in which the piecewise quadratic value functions become piecewise affine and can be merged to a single value function defined over a single polyhedral partition without any overlaps. This enables efficient point location approaches using a single binary search tree. Numerical experiments with a power electronics application demonstrate an online speedup up to an order of magnitude. We also show how the achievable online evaluation time can be traded off against the offline computational time.

sted, utgiver, år, opplag, sider
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2015
Emneord
Control of constrained systems; control of discrete time hybrid systems; explicit MPC
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-124516 (URN)10.1109/TAC.2015.2417853 (DOI)000367284600022 ()
Tilgjengelig fra: 2016-02-02 Laget: 2016-02-01 Sist oppdatert: 2017-11-30
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0001-6957-2603