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Publications (10 of 194) Show all publications
Ebadat, A., Varagnolo, D., Bottegal, G., Wahlberg, B. & Johansson, K. H. (2017). Application-oriented input design for room occupancy estimation algorithms. In: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC): . Paper presented at IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, AUSTRALIA. IEEE
Open this publication in new window or tab >>Application-oriented input design for room occupancy estimation algorithms
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2017 (English)In: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2017Conference paper, Published paper (Refereed)
Abstract [en]

We consider the problem of occupancy estimation in buildings using available environmental information. In particular, we study the problem of how to collect data that is informative enough for occupancy estimation purposes. We thus propose an application-oriented input design approach for designing the ventilation signal to be used while collecting the system identification datasets. The main goal of the method is to guarantee a certain accuracy in the estimated occupancy levels while minimizing the experimental time and effort. To take into account potential limitations on the actuation signals we moreover formulate the problem as a recursive nonlinear and nonconvex optimization problem, solved then using exhaustive search methods. We finally corroborate the theoretical findings with some numerical examples, which results show that computing ventilation signals using experiment design concepts leads to occupancy estimator performing 4 times better in terms of Mean Square Error (MSE).

Place, publisher, year, edition, pages
IEEE, 2017
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
Keywords
Occupancy estimation, CO2 dynamics, application-oriented input design, minimum-time input design
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-223848 (URN)000424696903050 ()978-1-5090-2873-3 (ISBN)
Conference
IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, AUSTRALIA
Funder
Swedish Research Council
Note

QC 20180306

Available from: 2018-03-06 Created: 2018-03-06 Last updated: 2018-03-06Bibliographically approved
Annergren, M., Larsson, C. A., Hjalmarsson, H., Bombois, X. & Wahlberg, B. (2017). Application-Oriented Input Design in System Identification Optimal input design for control. IEEE CONTROL SYSTEMS MAGAZINE, 37(2), 31-56
Open this publication in new window or tab >>Application-Oriented Input Design in System Identification Optimal input design for control
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2017 (English)In: IEEE CONTROL SYSTEMS MAGAZINE, ISSN 1066-033X, Vol. 37, no 2, p. 31-56Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE, 2017
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-208262 (URN)10.1109/MCS.2016.2643243 (DOI)000398902900003 ()2-s2.0-85016139089 (Scopus ID)
Funder
Swedish Research Council, 621-2009-4017EU, FP7, Seventh Framework Programme, 257059EU, European Research Council, 267381
Note

QC 20170614

Available from: 2017-06-14 Created: 2017-06-14 Last updated: 2017-06-30Bibliographically approved
Mattila, R., Rojas, C. R., Krishnamurthy, V. & Wahlberg, B. (2017). Asymptotically Efficient Identification of Known-Sensor Hidden Markov Models. IEEE Signal Processing Letters, 24(12), 1813-1817
Open this publication in new window or tab >>Asymptotically Efficient Identification of Known-Sensor Hidden Markov Models
2017 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 24, no 12, p. 1813-1817Article in journal (Refereed) Published
Abstract [en]

We consider estimating the transition probability matrix of a finite-state finite-observation alphabet hidden Markov model with known observation probabilities. We propose a two-step algorithm: a method of moments estimator (formulated as a convex optimization problem) followed by a single iteration of a Newton-Raphson maximum-likelihood estimator. The two-fold contribution of this letter is, first, to theoretically show that the proposed estimator is consistent and asymptotically efficient, and second, to numerically show that the method is computationally less demanding than conventional methods-in particular for large datasets.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2017
Keywords
Hidden Markov models (HMM), maximum-likelihood (ML), method of moments, system identification
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-217930 (URN)10.1109/LSP.2017.2759902 (DOI)000413962800006 ()2-s2.0-85031786373 (Scopus ID)
Note

QC 20171121

Available from: 2017-11-21 Created: 2017-11-21 Last updated: 2017-11-21Bibliographically approved
Persson, L., Muskardin, T. & Wahlberg, B. (2017). Cooperative Rendezvous of Ground Vehicle and Aerial Vehicle using Model Predictive Control. In: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC): . Paper presented at IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, AUSTRALIA. IEEE
Open this publication in new window or tab >>Cooperative Rendezvous of Ground Vehicle and Aerial Vehicle using Model Predictive Control
2017 (English)In: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2017Conference paper, Published paper (Refereed)
Abstract [en]

This paper considers the problem of controlling a fixed-wing unmanned aerial vehicle and a cooperating unmanned ground vehicle to rendezvous by making the aerial vehicle land on top the ground vehicle. Both vehicles are actively taking part in the control effort, where they coordinate positions and velocities to complete the landing. The rendezvous time and the terminal state are kept free to increase the flexibility of the solution. There are two main challenges with this maneuver. First, the controller must force the system to stay within a safe set such that the aerial vehicle approaches the ground vehicle directly from above. Second, the rendezvous must occur within some finite distance. A model predictive control algorithm is proposed to achieve these objectives. The choice is motivated by recent experimental results showing how the landing safety and efficiency could benefit from including safety margins already in the computation of the control inputs. A controller, which steers the agents towards rendezvous and which indirectly provides safety guarantees through non-convex optimization constraints, is derived. Simulations are provided showing the ability of the controller to plan a safe trajectory online, even under the disturbance of wind gusts.

Place, publisher, year, edition, pages
IEEE, 2017
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-223866 (URN)000424696902117 ()978-1-5090-2873-3 (ISBN)
Conference
IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, AUSTRALIA
Funder
Knut and Alice Wallenberg Foundation
Note

QC 20180306

Available from: 2018-03-06 Created: 2018-03-06 Last updated: 2018-03-14Bibliographically approved
Mattila, R., Rojas, C. R., Krishnamurthy, V. & Wahlberg, B. (2017). Inverse filtering for hidden Markov models. In: Advances in Neural Information Processing Systems: . Paper presented at 31st Annual Conference on Neural Information Processing Systems, NIPS 2017, 4 December 2017 through 9 December 2017 (pp. 4205-4214). Neural information processing systems foundation, 2017
Open this publication in new window or tab >>Inverse filtering for hidden Markov models
2017 (English)In: Advances in Neural Information Processing Systems, Neural information processing systems foundation , 2017, Vol. 2017, p. 4205-4214Conference paper, Published paper (Refereed)
Abstract [en]

This paper considers a number of related inverse filtering problems for hidden Markov models (HMMs). In particular, given a sequence of state posteriors and the system dynamics; i) estimate the corresponding sequence of observations, ii) estimate the observation likelihoods, and iii) jointly estimate the observation likelihoods and the observation sequence. We show how to avoid a computationally expensive mixed integer linear program (MILP) by exploiting the algebraic structure of the HMM filter using simple linear algebra operations, and provide conditions for when the quantities can be uniquely reconstructed. We also propose a solution to the more general case where the posteriors are noisily observed. Finally, the proposed inverse filtering algorithms are evaluated on real-world polysomnographic data used for automatic sleep segmentation.

Place, publisher, year, edition, pages
Neural information processing systems foundation, 2017
Series
Advances in Neural Information Processing Systems, ISSN 1049-5258 ; 2017
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-228586 (URN)2-s2.0-85047014120 (Scopus ID)
Conference
31st Annual Conference on Neural Information Processing Systems, NIPS 2017, 4 December 2017 through 9 December 2017
Note

QC 20180528

Available from: 2018-05-28 Created: 2018-05-28 Last updated: 2018-05-28Bibliographically approved
Ebadat, A., Valenzuela, P. E., Rojas, C. R. & Wahlberg, B. (2017). Model Predictive Control oriented experiment design for system identification: A graph theoretical approach. Journal of Process Control, 52, 75-84
Open this publication in new window or tab >>Model Predictive Control oriented experiment design for system identification: A graph theoretical approach
2017 (English)In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 52, p. 75-84Article in journal (Refereed) Published
Abstract [en]

We present a new approach to Model Predictive Control (MPC) oriented experiment design for the identification of systems operating in closed-loop. The method considers the design of an experiment by minimizing the experimental cost, subject to probabilistic bounds on the input and output signals due to physical limitations of actuators, and quality constraints on the identified model. The excitation is done by intentionally adding a disturbance to the loop. We then design the external excitation to achieve the minimum experimental effort while we are also taking care of the tracking performance of MPC. The stability of the closed-loop system is guaranteed by employing robust MPC during the experiment. The problem is then defined as an optimization problem. However, the aforementioned constraints result in a non-convex optimization which is relaxed by using results from graph theory. The proposed technique is evaluated through a numerical example showing that it is an attractive alternative for closed-loop experiment design.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2017
Keywords
Closed-loop identification, Optimal input design, System identification, Model Predictive Control, Constrained systems
National Category
Chemical Engineering
Identifiers
urn:nbn:se:kth:diva-207702 (URN)10.1016/j.jprocont.2017.02.001 (DOI)000399849400008 ()2-s2.0-85013678020 (Scopus ID)
Note

QC 20170530

Available from: 2017-05-30 Created: 2017-05-30 Last updated: 2017-06-30Bibliographically approved
Lima, P. F., Nilsson, M., Trincavelli, M., Mårtensson, J. & Wahlberg, B. (2017). Spatial Model Predictive Control for Smooth and Accurate Steering of an Autonomous Truck. IEEE Transactions on Intelligent Vehicles, 2(4), 238-250
Open this publication in new window or tab >>Spatial Model Predictive Control for Smooth and Accurate Steering of an Autonomous Truck
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2017 (English)In: IEEE Transactions on Intelligent Vehicles, ISSN 2379-8858, Vol. 2, no 4, p. 238-250Article in journal (Refereed) Published
Abstract [en]

In this paper, we present an algorithm for lateral control of a vehicle – a smooth and accurate model predictive controller. The fundamental difference compared to a standard MPC is that the driving smoothness is directly addressed in the cost function. The controller objective is based on the minimization of the first- and second-order spatial derivatives of the curvature. By doing so, jerky commands to the steering wheel, which could lead to permanent damage on the steering components and vehicle structure, are avoided. A good path tracking accuracy is ensured by adding constraints to avoid deviations from the reference path. Finally, the controller is experimentally tested and evaluated on a Scania construction truck. The evaluation is performed at Scania’s facilities near So ̈derta ̈lje, Sweden via two different paths: a precision track that resembles a mining scenario and a high-speed test track that resembles a highway situation. Even using a linearized kinematic vehicle to predict the vehicle motion, the performance of the proposed controller is encouraging, since the deviation from the path never exceeds 30 cm. It clearly outperforms an industrial pure-pursuit controller in terms of path accuracy and a standard MPC in terms of driving smoothness. 

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Autonomous vehicles, predictive control
National Category
Control Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-220573 (URN)10.1109/TIV.2017.2767279 (DOI)
Projects
iQMatic
Funder
VINNOVA, 2012-04626
Note

QC 20180117

Available from: 2017-12-27 Created: 2017-12-27 Last updated: 2018-01-17Bibliographically approved
Lima, P. F., Mårtensson, J. & Wahlberg, B. (2017). Stability Conditions for Linear Time-Varying Model Predictive Control in Autonomous Driving. In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017: . Paper presented at 56th IEEE Annual Conference on Decision and Control, CDC 2017, Melbourne Convention and Exhibition Centre (MCEC)Melbourne, Australia, 12 December 2017 through 15 December 2017 (pp. 2775-2782). IEEE
Open this publication in new window or tab >>Stability Conditions for Linear Time-Varying Model Predictive Control in Autonomous Driving
2017 (English)In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, IEEE, 2017, p. 2775-2782Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents stability conditions when designing a linear time-varying model predictive controller for lateral control of an autonomous vehicle. Stability is proved via Lyapunov techniques by adding a terminal state constraint and a terminal cost. We detail how to compute the terminal state and the terminal cost for the linear time-varying case, and interpret the obtained results in the light of an autonomous driving application. To determine the stability conditions, the concept of multi-model description is used, where the linear time-varying model is separated into a finite number of time- invariant models that depend on a single parameter. The terminal set is the maximum positive invariant set of the multi- model description and the terminal cost is the result of a min-max optimization that determines the worst time-invariant model if used as a prediction model. In fact, in the autonomous driving case, we show that the min-max approach is a convex optimization problem. The stability conditions are computed offline, maintain the convexity of the optimization, and do not affect the execution time of the controller. In simulation, we demonstrate the stabilizing effectiveness of the proposed conditions through an illustrative example of path following with a heavy-duty vehicle. 

Place, publisher, year, edition, pages
IEEE, 2017
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
Keywords
Autonomous Driving, Model Predictive Control, Stability
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-220576 (URN)10.1109/CDC.2017.8264062 (DOI)000424696902110 ()2-s2.0-85046277035 (Scopus ID)978-1-5090-2873-3 (ISBN)
Conference
56th IEEE Annual Conference on Decision and Control, CDC 2017, Melbourne Convention and Exhibition Centre (MCEC)Melbourne, Australia, 12 December 2017 through 15 December 2017
Funder
VINNOVA
Note

QC 20180119

Available from: 2017-12-27 Created: 2017-12-27 Last updated: 2018-06-01Bibliographically approved
Plessen, M. G., Lima, P. F., Mårtensson, J., Bemporad, A. & Wahlberg, B. (2017). Trajectory Planning Under Vehicle Dimension Constraints Using Sequential Linear Programming. In: 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC): . Paper presented at 20th IEEE International Conference on Intelligent Transportation Systems (ITSC), OCT 16-19, 2017, Yokohama, JAPAN. IEEE
Open this publication in new window or tab >>Trajectory Planning Under Vehicle Dimension Constraints Using Sequential Linear Programming
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2017 (English)In: 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE , 2017Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a spatial-based trajectory planning method for automated vehicles under actuator, obstacle avoidance, and vehicle dimension constraints. Starting from a nonlinear kinematic bicycle model, vehicle dynamics are transformed to a road-aligned coordinate frame with path along the road centerline replacing time as the dependent variable. Space-varying vehicle dimension constraints are linearized around a reference path to pose convex optimization problems. Such constraints do not require to inflate obstacles by safety-margins and therefore maximize performance in very constrained environments. A sequential linear programming (SLP) algorithm is motivated. A linear program (LP) is solved at each SLP-iteration. The relation between LP formulation and maximum admissible traveling speeds within vehicle tire friction limits is discussed. The proposed method is evaluated in a roomy and in a tight maneuvering driving scenario, whereby a comparison to a semi-analytical clothoid-based path planner is given. Effectiveness is demonstrated particularly for very constrained environments, requiring to account for constraints and planning over the entire obstacle constellation space.

Place, publisher, year, edition, pages
IEEE, 2017
Series
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:kth:diva-230879 (URN)10.1109/ITSC.2017.8317665 (DOI)000432373000077 ()2-s2.0-85046257090 (Scopus ID)978-1-5386-1526-3 (ISBN)
Conference
20th IEEE International Conference on Intelligent Transportation Systems (ITSC), OCT 16-19, 2017, Yokohama, JAPAN
Note

QC 20180618

Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2018-07-30Bibliographically approved
Mattila, R., Siika, A., Roy, J. & Wahlberg, B. (2016). A Markov Decision Process Model to Guide Treatment of Abdominal Aortic Aneurysms. In: 2016 IEEE CONFERENCE ON CONTROL APPLICATIONS (CCA): . Paper presented at IEEE Conference on Control Applications (CCA), SEP 19-22, 2016, Buenos Aires, ARGENTINA. IEEE
Open this publication in new window or tab >>A Markov Decision Process Model to Guide Treatment of Abdominal Aortic Aneurysms
2016 (English)In: 2016 IEEE CONFERENCE ON CONTROL APPLICATIONS (CCA), IEEE, 2016Conference paper, Published paper (Refereed)
Abstract [en]

An abdominal aortic aneurysm (AAA) is an enlargement of the abdominal aorta which, if left untreated, can progressively widen and may rupture with fatal consequences. In this paper, we determine an optimal treatment policy using Markov decision process modeling. The policy is optimal with respect to the number of quality adjusted life-years (QALYs) that are expected to be accumulated during the remaining life of a patient. The new policy takes into account factors that are ignored by the current clinical policy (e.g. the life-expectancy and the age-dependent surgical mortality). The resulting optimal policy is structurally different from the current policy. In particular, the policy suggests that young patients with small aneurysms should undergo surgery. The robustness of the policy structure is demonstrated using simulations. A gain in the number of expected QALYs is shown, which indicates a possibility of improved care for patients with AAAs.

Place, publisher, year, edition, pages
IEEE, 2016
Series
IEEE International Conference on Control Applications, ISSN 1085-1992
Keywords
Abdominal aortic aneurysm (AAA), biosystems, decision making, Markov decision process (MDP), public health-care, treatment policy
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-197033 (URN)000386696600057 ()978-1-5090-0755-4 (ISBN)
Conference
IEEE Conference on Control Applications (CCA), SEP 19-22, 2016, Buenos Aires, ARGENTINA
Note

QC 20161208

Available from: 2016-12-08 Created: 2016-11-28 Last updated: 2016-12-08Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1927-1690

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