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Sadeghi, M., Rojas, C. R. & Wahlberg, B. (2018). A Branch and Bound Approach to System Identification based on Fixed-rank Hankel Matrix Optimization. IFAC-PapersOnLine, 51(15), 96-101
Öppna denna publikation i ny flik eller fönster >>A Branch and Bound Approach to System Identification based on Fixed-rank Hankel Matrix Optimization
2018 (Engelska)Ingår i: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, nr 15, s. 96-101Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

We consider identification of linear systems with a certain order from a set of noisy input-output observations. We utilize the fact that the system order corresponds to the rank of the Hankel matrix associated with the system impulse response. Then, the system identification problem is formulated as the minimization of the output error subject to a rank constraint on a Hankel matrix. As this problem is non-convex, we propose a branch and bound (BB) solver, which is a powerful tool for solving non-convex problems to optimality. The main ingredients of the proposed BB method are a convex relaxation problem and a local minimizer of the original non-convex problem. We illustrate the promising performance of the proposed scheme in a system identification problem. The results demonstrate the higher accuracy and stability of our method in estimating the true system compared to the standard output error (OE) algorithm.

Ort, förlag, år, upplaga, sidor
Elsevier B.V., 2018
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:kth:diva-247401 (URN)10.1016/j.ifacol.2018.09.097 (DOI)2-s2.0-85054351520 (Scopus ID)
Anmärkning

QC 20190322

Tillgänglig från: 2019-03-22 Skapad: 2019-03-22 Senast uppdaterad: 2019-03-22Bibliografiskt granskad
Wahlberg, B. & Ljung, L. (2018). Algorithms and Performance Analysis for Stochastic Wiener System Identification. IEEE Control Systems Letters, 2(3), 471-476
Öppna denna publikation i ny flik eller fönster >>Algorithms and Performance Analysis for Stochastic Wiener System Identification
2018 (Engelska)Ingår i: IEEE Control Systems Letters, ISSN 2475-1456, Vol. 2, nr 3, s. 471-476Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

We analyze the statistical performance of identification of stochastic dynamical systems with non-linear measurement sensors. This includes stochastic Wiener systems, with linear dynamics, process noise and measured by a non-linear sensor with additive measurement noise. There are many possible system identification methods for such systems, including the maximum likelihood (ML) method and the prediction error method. The focus has mostly been on algorithms and implementation, and less is known about the statistical performance and the corresponding Cramér-Rao lower bound (CRLB) for identification of such non-linear systems. We derive expressions for the CRLB and the asymptotic normalized covariance matrix for certain Gaussian approximations of Wiener systems to show how a non-linear sensor affects the accuracy compared to a corresponding linear sensor. The key idea is to take second order statistics into account by using a common parametrization of the mean and the variance of the output process. This analysis also leads to an ML motivated identification method based on the conditional mean predictor and a Gaussian distribution approximation. The analysis is supported by numerical simulations.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers Inc., 2018
Nyckelord
Nonlinear systems identification, stochastic systems, Covariance matrix, Dynamical systems, Identification (control systems), Linear systems, Maximum likelihood, Nonlinear systems, Religious buildings, Algorithms and performance, Gaussian approximations, Maximum likelihood methods, Prediction error method, Statistical performance, Stochastic dynamical system, System identification methods
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
urn:nbn:se:kth:diva-247211 (URN)10.1109/LCSYS.2018.2840878 (DOI)2-s2.0-85057638172 (Scopus ID)
Anmärkning

QC 20190415

Tillgänglig från: 2019-04-15 Skapad: 2019-04-15 Senast uppdaterad: 2019-04-15Bibliografiskt granskad
Oliveira, R., Cirillo, M., Mårtensson, J. & Wahlberg, B. (2018). Combining Lattice-Based Planning and Path Optimization in Autonomous Heavy Duty Vehicle Applications. In: IEEE Intelligent Vehicles Symposium, Proceedings: . Paper presented at 2018 IEEE Intelligent Vehicles Symposium, IV 2018, 26 September 2018 through 30 September 2018 (pp. 2090-2097). Institute of Electrical and Electronics Engineers Inc.
Öppna denna publikation i ny flik eller fönster >>Combining Lattice-Based Planning and Path Optimization in Autonomous Heavy Duty Vehicle Applications
2018 (Engelska)Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018, s. 2090-2097Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Lattice-based motion planners are an established method to generate feasible motions for car-like vehicles. However, the solution paths can only reach a discretized approximation of the intended goal pose. Moreover, they can be optimal only with respect to the actions available to the planner, which can result in paths with excessive steering. These drawbacks have a negative impact when used in real systems. In this paper we address both drawbacks by integrating a steering method into a state-of-the-art lattice-based motion planner. Un- like previous approaches, in which path optimization happens in an a posteriori step after the planner has found a solution, we propose an interleaved execution of path planning and path optimization. The proposed approach can run in real-time and is implemented in a full-size autonomous truck, and we show experimentally that it is able to greatly improve the quality of the solutions provided by a lattice planner.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers Inc., 2018
Nyckelord
Intelligent vehicle highway systems, Motion planning, Steering, Car-like vehicles, Heavy duty vehicles, Lattice planners, Lattice-based, Motion planners, Path optimizations, State of the art, Steering method, Automobile steering equipment
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
urn:nbn:se:kth:diva-247124 (URN)10.1109/IVS.2018.8500616 (DOI)2-s2.0-85056776487 (Scopus ID)9781538644522 (ISBN)
Konferens
2018 IEEE Intelligent Vehicles Symposium, IV 2018, 26 September 2018 through 30 September 2018
Anmärkning

QC 20190403

Tillgänglig från: 2019-04-03 Skapad: 2019-04-03 Senast uppdaterad: 2019-04-03Bibliografiskt granskad
Lima, P. F., Pereira, G. C., Mårtensson, J. & Wahlberg, B. (2018). Experimental validation of model predictive control stability for autonomous driving. Control Engineering Practice, 81, 244-255
Öppna denna publikation i ny flik eller fönster >>Experimental validation of model predictive control stability for autonomous driving
2018 (Engelska)Ingår i: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 81, s. 244-255Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

This paper addresses the design of time-varying model predictive control of an autonomous vehicle in the presence of input rate constraints such that closed-loop stability is guaranteed. Stability is proved via Lyapunov techniques by adding a terminal state constraint and a terminal cost to the controller formulation. The terminal set is the maximum positive invariant set of a multi-plant description of the vehicle linear time-varying model. The terminal cost is an upper-bound on the infinite cost-to-go incurred by applying a linear-quadratic regulator control law. The proposed control design is experimentally tested and successfully stabilizes an autonomous Scania construction truck in an obstacle avoidance scenario.

Ort, förlag, år, upplaga, sidor
PERGAMON-ELSEVIER SCIENCE LTD, 2018
Nyckelord
Model predictive control, Stability, Set invariance, Autonomous driving, Automatic control
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:kth:diva-239756 (URN)10.1016/j.conengprac.2018.09.021 (DOI)000449899500022 ()2-s2.0-85054297364 (Scopus ID)
Anmärkning

QC 20190110

Tillgänglig från: 2019-01-10 Skapad: 2019-01-10 Senast uppdaterad: 2019-01-10Bibliografiskt granskad
Mattila, R., Rojas, C. R., Krishnamurthy, V. & Wahlberg, B. (2018). Inverse Filtering for Linear Gaussian State-Space Models. In: 2018 IEEE Conference on Decision and Control  (CDC): . Paper presented at 57th IEEE Conference on Decision and Control, CDC 2018; Centre of the Fontainebleau in Miami Beac hMiami; United States; 17 December 2018 through 19 December 2018 (pp. 5556-5561). Institute of Electrical and Electronics Engineers (IEEE), Article ID 8619013.
Öppna denna publikation i ny flik eller fönster >>Inverse Filtering for Linear Gaussian State-Space Models
2018 (Engelska)Ingår i: 2018 IEEE Conference on Decision and Control  (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 5556-5561, artikel-id 8619013Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper considers inverse filtering problems for linear Gaussian state-space systems. We consider three problems of increasing generality in which the aim is to reconstruct the measurements and/or certain unknown sensor parameters, such as the observation likelihood, given posteriors (i. e., the sample path of mean and covariance). The paper is motivated by applications where one wishes to calibrate a Bayesian estimator based on remote observations of the posterior estimates, e. g., determine how accurate an adversary's sensors are. We propose inverse filtering algorithms and evaluate their robustness with respect to noise (e. g., measurement or quantization errors) in numerical simulations.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2018
Serie
IEEE Conference on Decision and Control, ISSN 0743-1546
Nationell ämneskategori
Signalbehandling
Identifikatorer
urn:nbn:se:kth:diva-245114 (URN)10.1109/CDC.2018.8619013 (DOI)000458114805022 ()2-s2.0-85062188998 (Scopus ID)978-1-5386-1395-5 (ISBN)
Konferens
57th IEEE Conference on Decision and Control, CDC 2018; Centre of the Fontainebleau in Miami Beac hMiami; United States; 17 December 2018 through 19 December 2018
Anmärkning

QC 20190306

Tillgänglig från: 2019-03-06 Skapad: 2019-03-06 Senast uppdaterad: 2019-03-06Bibliografiskt granskad
Pereira, G. C., Lima, P. F., Wahlberg, B., Pettersson, H. & Mårtensson, J. (2018). Linear Time-Varying Robust Model Predictive Control for Discrete-Time Nonlinear Systems. In: 2018 IEEE Conference on Decision and Control  (CDC): . Paper presented at 57th IEEE Conference on Decision and Control, CDC 2018; Centre of the Fontainebleau in Miami Beach Miami; United States; 17 December 2018 through 19 December 2018 (pp. 2659-2666). Institute of Electrical and Electronics Engineers (IEEE)
Öppna denna publikation i ny flik eller fönster >>Linear Time-Varying Robust Model Predictive Control for Discrete-Time Nonlinear Systems
Visa övriga...
2018 (Engelska)Ingår i: 2018 IEEE Conference on Decision and Control  (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 2659-2666Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper presents a robust model predictive controller for discrete-time nonlinear systems, subject to state and input constraints and unknown but bounded input disturbances. The prediction model uses a linearized time-varying version of the original discrete-time system. The proposed optimization problem includes the initial state of the current nominal model of the system as an optimization variable, which allows to guarantee robust exponential stability of a disturbance invariant set for the discrete-time nonlinear system. From simulations, it is possible to verify the proposed algorithm is real-time capable, since the problem is convex and posed as a quadratic program.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2018
Serie
IEEE Conference on Decision and Control, ISSN 0743-1546
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:kth:diva-245109 (URN)10.1109/CDC.2018.8618866 (DOI)000458114802081 ()2-s2.0-85062174591 (Scopus ID)978-1-5386-1395-5 (ISBN)
Konferens
57th IEEE Conference on Decision and Control, CDC 2018; Centre of the Fontainebleau in Miami Beach Miami; United States; 17 December 2018 through 19 December 2018
Anmärkning

QC 20190306

Tillgänglig från: 2019-03-06 Skapad: 2019-03-06 Senast uppdaterad: 2019-03-06Bibliografiskt granskad
Muller, E. R., Wahlberg, B. & Carlson, R. C. (2018). Optimal motion planning for automated vehicles with scheduled arrivals at intersections. In: 2018 European Control Conference, ECC 2018: . Paper presented at 16th European Control Conference, ECC 2018, Limassol, Cyprus, 12 June 2018 through 15 June 2018 (pp. 1672-1678). Institute of Electrical and Electronics Engineers (IEEE), Article ID 8550329.
Öppna denna publikation i ny flik eller fönster >>Optimal motion planning for automated vehicles with scheduled arrivals at intersections
2018 (Engelska)Ingår i: 2018 European Control Conference, ECC 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 1672-1678, artikel-id 8550329Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

We design and compare three different optimal control strategies for the motion planning of automated vehicles approaching an intersection with scheduled arrivals. The objective is to minimize a combination of energy consumption and deviation from the schedule. The strategies differ in allowed deviations. When taking only vehicles inside the control region into account, the strategy that achieves the lowest energy consumption is the less strict one, albeit at the expense of higher travel times. When traffic conditions beyond the control region are considered, no strategy is able to achieve lower energy consumption or vehicle delay than the strategy that is the most strict in keeping with the schedule. Results suggests that in high traffic situations, from a global energy consumption standpoint, it is best to have vehicles crossing the intersection as soon as possible.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2018
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:kth:diva-241400 (URN)10.23919/ECC.2018.8550329 (DOI)2-s2.0-85059806506 (Scopus ID)9783952426982 (ISBN)
Konferens
16th European Control Conference, ECC 2018, Limassol, Cyprus, 12 June 2018 through 15 June 2018
Anmärkning

QC 20190121

Tillgänglig från: 2019-01-21 Skapad: 2019-01-21 Senast uppdaterad: 2019-01-21Bibliografiskt granskad
Lima, P. F., Pereira, G. C., Mårtensson, J. & Wahlberg, B. (2018). Progress Maximization Model Predictive Controller. In: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC): . Paper presented at 21st IEEE International Conference on Intelligent Transportation Systems (ITSC), NOV 04-07, 2018, Maui, HI (pp. 1075-1082). IEEE
Öppna denna publikation i ny flik eller fönster >>Progress Maximization Model Predictive Controller
2018 (Engelska)Ingår i: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE , 2018, s. 1075-1082Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper addresses the problem of progress maximization (i.e., traveling time minimization) along a given path for autonomous vehicles. Progress maximization plays an important role not only in racing, but also in efficient and safe autonomous driving applications. The progress maximization problem is formulated as a model predictive controller, where the vehicle model is successively linearized at each time step, yielding a convex optimization problem. To ensure real-time feasibility, a kinematic vehicle model is used together with several linear approximations of the vehicle dynamics constraints. We propose a novel polytopic approximation of the 'g-g' diagram, which models the vehicle handling limits by constraining the lateral and longitudinal acceleration. Moreover, the tire slip angles are restricted to ensure that the tires of the vehicle always operate in their linear force region by limiting the lateral acceleration. We illustrate the effectiveness of the proposed controller in simulation, where a nonlinear dynamic vehicle model is controlled to maximize the progress along a track, taking into consideration possible obstacles.

Ort, förlag, år, upplaga, sidor
IEEE, 2018
Serie
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
Nationell ämneskategori
Transportteknik och logistik
Identifikatorer
urn:nbn:se:kth:diva-244588 (URN)000457881301013 ()2-s2.0-85060480601 (Scopus ID)978-1-7281-0323-5 (ISBN)
Konferens
21st IEEE International Conference on Intelligent Transportation Systems (ITSC), NOV 04-07, 2018, Maui, HI
Anmärkning

QC 20190304

Tillgänglig från: 2019-03-04 Skapad: 2019-03-04 Senast uppdaterad: 2019-03-04Bibliografiskt granskad
Hou, J., Liu, T., Wahlberg, B. & Jansson, M. (2018). Subspace Hammerstein Model Identification under Periodic Disturbance. In: : . Paper presented at 18th IFAC Symposium on System Identification SYSID 2018 (pp. 335-340). Elsevier B.V., 51(15)
Öppna denna publikation i ny flik eller fönster >>Subspace Hammerstein Model Identification under Periodic Disturbance
2018 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

In this paper, a subspace identification method is proposed for Hammerstein systems under periodic disturbance. By using the linear superposition principle to decompose the periodic disturbance response from the deterministic system response, an orthogonal projection is established to eliminate the disturbance effect. The unknown disturbance period can be estimated by defining an objective function of output prediction error for minimization. Correspondingly, a singular value decomposition (SVD) based algorithm is given to estimate the observability matrix and the lower triangular block-Toeplitz matrix. The state matrices A and C are subsequently retrieved from the estimated observability matrix via a shift-invariant algorithm, while the input matrix B and the nonlinear input function parameters are retrieved from the estimated lower triangular block-Toeplitz matrix by an SVD approach. Consistent estimation of the observability matrix and the lower triangular block-Toeplitz matrix is analyzed. An illustrative example is shown to demonstrate the effectiveness of the proposed identification method. 

Ort, förlag, år, upplaga, sidor
Elsevier B.V., 2018
Nyckelord
Consistent estimation, Hammerstein system, Periodic disturbance, Subspace identification, Identification (control systems), Nonlinear systems, Observability, Block Toeplitz matrices, Linear superposition principles, Output prediction errors, Periodic disturbances, Subspace identification methods, Singular value decomposition
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:kth:diva-247499 (URN)10.1016/j.ifacol.2018.09.157 (DOI)000446599200058 ()2-s2.0-85054358180 (Scopus ID)
Konferens
18th IFAC Symposium on System Identification SYSID 2018
Anmärkning

QC 20190403

Tillgänglig från: 2019-04-03 Skapad: 2019-04-03 Senast uppdaterad: 2019-05-20Bibliografiskt granskad
Oliveira, R. F., Lima, P. F., Cirillo, M., Mårtensson, J. & Wahlberg, B. (2018). Trajectory Generation using Sharpness Continuous Dubins-like Paths with Applications in Control of Heavy-Duty Vehicles. In: 2018 European Control Conference, ECC 2018: . Paper presented at 16th European Control Conference, ECC 2018, 12 June 2018 through 15 June 2018 (pp. 935-940). Institute of Electrical and Electronics Engineers Inc.
Öppna denna publikation i ny flik eller fönster >>Trajectory Generation using Sharpness Continuous Dubins-like Paths with Applications in Control of Heavy-Duty Vehicles
Visa övriga...
2018 (Engelska)Ingår i: 2018 European Control Conference, ECC 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, s. 935-940Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

We present a trajectory generation framework for control of wheeled vehicles under steering actuator constraints. The motivation is smooth driving of autonomous heavy-duty vehicles, which are characterized by slow actuator dynamics. In order to deal with the slow dynamics, we take into account rate and, additionally, torque limitations of the steering actuator directly. Previous methods only take into account limitations in the path curvature, which deals indirectly with steering rate limitations. We propose the new concept of Sharpness Continuous curves, which uses cubic curvature paths together with circular arcs to steer the vehicle. The obtained paths are characterized by a smooth and continuously differentiable steering angle profile. The final trajectories computed with our method provide low-level controllers with reference signals which are easier to track, resulting in improved performance. The smoothness of the obtained steering profiles also results in increased passenger comfort. The method is characterized by fast computation times. We detail possible path planning applications of the method, and conduct simulations that show its advantages and real-time capabilities.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers Inc., 2018
Nyckelord
Actuators, Automobile steering equipment, Motion planning, Trajectories, Actuator dynamics, Continuously differentiable, Heavy duty vehicles, Low-level controllers, Planning applications, Real time capability, Steering actuators, Trajectory generation, Steering
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
urn:nbn:se:kth:diva-247039 (URN)10.23919/ECC.2018.8550279 (DOI)2-s2.0-85056785367 (Scopus ID)9783952426982 (ISBN)
Konferens
16th European Control Conference, ECC 2018, 12 June 2018 through 15 June 2018
Anmärkning

QC 20190625

Tillgänglig från: 2019-06-25 Skapad: 2019-06-25 Senast uppdaterad: 2019-06-25Bibliografiskt granskad
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-1927-1690

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