| 1. |
- Falkman, Petter, 1972, et al.
(författare)
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Combined Process Algebra and Petri Nets for Specification of Resource Booking Problems
- 2001
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Ingår i: American Control Conference. - 0743-1619. - 0780364953 ; 6, s. 4949-4955
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Konferensbidrag (refereegranskat)abstract
- A modeling framework for general routing and resource booking problems is presented. The task is to specify desired routes for individual objects (products, data packets, vehicles), which are to be served by a number of shared resources (machines, computers, communication links). A high level language is presented in order to simplify the specification of desired routes. This modeling language combines Petri nets and process algebra. Process operators are introduced for specification of sequences, alternative choices, arbitrary ordered sequences and process synchronization. Multiple as well as alternative resources are easily specified utilizing sets of resources. These sets and operators imply a very compact and efficient representation of complex routing specifications. In fact, the representation is suitable for verification and synthesis based on symbolic tools such as BDDs.
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| 2. |
- Gustafsson, Fredrik, et al.
(författare)
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Range Estimation using Angle-Only Target Tracking with Particle Filters
- 2001
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Ingår i: Proceedings of the Third Conference on Computer Science and Systems Engineering. ; , s. 55-62, s. 3743-3748 vol.5
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- We consider the recursive state estimation of a maneuverable aircraft using an airborne passive IR-sensor. The main issue addressed in the paper is the range- and velocity estimation using angle-only measurements. In contrast to standard target tracking literature we do not rely on linearized motion models and measurement relations, or on any Gaussian assumptions. Instead, we apply optimal recursive Bayesian filters directly to the nonlinear target model. We present novel sequential simulation based algorithms developed explicitly for the angle-only target tracking problem. These Monte Carlo filters approximate optimal inference by simulating a large number of tracks, or particles. In a simulation study our particle filter approach is compared to a range parameterized extended Kalman filter (RPEKF). Tracking is performed in both Cartesian and modified spherical coordinates (MSC).
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| 3. |
- Gustafsson, Fredrik, et al.
(författare)
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Sequential Monte Carlo Filtering Techniques Applied to Integrated Navigation Systems
- 2001
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Ingår i: Proceedings of the 2001 American Control Conference. - 0780364953 ; , s. 4375-4380 vol.6
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of integrated aircraft navigation, more specifically how to integrate inertial navigation with terrain aided positioning. This is a highly nonlinear and non-Gaussian recursive state estimation problem which requires state of the art methods. We propose an algorithm based on the particle filter with particular attention to the complexity of the problem. The proposed algorithm takes advantage of linear and Gaussian structure within the system and solves these parts using the Kalman filter. The remaining parts suffering from severe nonlinear and/or non-Gaussian structure are solved using the particle filter. The proposed filter is applied to a simplified integrated navigation system. The result shows that very good performance is achieved for a tractable computational load.
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| 4. |
- Hansson, Anders, et al.
(författare)
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A Primal-Dual Potential Reduction Method for Integral Quadratic Constraints
- 2001
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Ingår i: Proceedings of the 2001 American Control Conference. - : IEEE. - 0780364953 ; , s. 3013-3018
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Konferensbidrag (refereegranskat)abstract
- We discuss how to implement an efficient interior-point algorithm for semi-definite programs that result from integral quadratic constraints. The algorithm is a primal-dual potential reduction method, and the computational effort is dominated by a least-squares system that has to be solved in each iteration. The key to an efficient implementation is to utilize iterative methods and the specific structure of integral quadratic constraints. The algorithm has been implemented in Matlab. To give a rough idea of the efficiencies obtained, it is possible to solve problems resulting in a linear matrix inequality of dimension 130 × 130 with approximately 5000 variables in about 5 minutes on a lap-top. Problems with approximately 20000 variable and a linear matrix inequality of dimension 230 × 230 are solved in about 45 minutes. It is not assumed that the system matrix has no eigenvalues on the imaginary axis, nor is it assumed that it is Hurwitz.
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| 5. |
- Henriksson, Björn, et al.
(författare)
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Control relevant identification of nonlinear systems using linear models
- 2001
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Ingår i: PROCEEDINGS OF THE 2001 AMERICAN CONTROL CONFERENCE. - 0780364953 ; , s. 1178-1183
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Konferensbidrag (refereegranskat)abstract
- In this contribution we consider control of nonlinear systems by means of linear time-invariant model based control design. It is shown that a desired closed loop response of the nonlinear system for a given reference signal can be achieved using a controller based on a linear model. This is possible if the linear model used in the control design is able to capture the input/output behaviour of the nonlinear system for the desired response. An identification method, designed for this purpose, is presented and illustrated on an numerical example.
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| 6. |
- Markusson, Ola, et al.
(författare)
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Higher order cumulant based parameter estimation in nonlinear time series models
- 2001
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Ingår i: PROCEEDINGS OF THE 2001 AMERICAN CONTROL CONFERENCE. - 0780364953 ; , s. 4888-4889
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Konferensbidrag (refereegranskat)abstract
- Parameter estimation in nonlinear time-series models based on higher order cumulant matching is proposed in this paper. The cumulant estimates are computed from measured and simulated data and a cost function computed from second and fourth order cumulants is used. To simplify the calculations a reduced cost function is suggested using low-dimensional slices of the cumulant functions. The estimation method is illustrated on a numerical example.
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