| 1. |
- Altafini, Claudio, et al.
(author)
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A feedback control scheme for reversing a truck and trailer vehicle
- 2001
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In: IEEE transactions on robotics and automation. - : Institute of Electrical and Electronics Engineers (IEEE). - 1042-296X. ; 17:6, s. 915-922
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Journal article (peer-reviewed)abstract
- A control scheme is proposed for stabilization of backward driving along simple paths for a miniaturized vehicle composed of a truck and a two-axle trailer. The paths chosen are straight lines and arcs of circles. When reversing, the truck and trailer under examination can be modeled as an unstable nonlinear system with state and input saturations. The simplified goal of stabilizing along a trajectory (instead of a point) allows us to consider a system with controllable linearization. Still, the combination of instability and saturations makes the task impossible with a single controller. In fact, the system cannot be driven backward from all initial states because of the jack-knife effects between the parts of the multibody vehicle; it is sometimes necessary to drive forward to enter into a specific region of attraction. This leads to the use of hybrid controllers. The scheme has been implemented and successfully used to reverse the radio-controlled vehicle.
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| 2. |
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| 3. |
- Bodin, Per, et al.
(author)
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Selection of best orthonormal rational basis
- 2000
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In: SIAM Journal on Control and Optimization. - 0363-0129 .- 1095-7138. ; 38:4, s. 995-1032
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Journal article (peer-reviewed)abstract
- This contribution deals with the problem of structure determination for generalized orthonormal basis models used in system identification. The model structure is parameterized by a prespecified set of poles representing a finite-dimensional subspace of H2. Given this structure and experimental data, a model can be estimated using linear regression techniques. Since the variance of the estimated model increases with the number of estimated parameters, one objective is to find coordinates, or a basis, for the finite-dimensional subspace giving as compact or parsimonious a system representation as possible. In this paper, a best basis algorithm and a coefficient decomposition scheme are derived for the generalized orthonormal rational bases. Combined with linear regression and thresholding this leads to compact transfer function representations. The methods are demonstrated with several examples.
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| 4. |
- Hansson, Anders, et al.
(author)
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Continuous-time blind channel deconvolution using Laguerre shifts
- 2000
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In: Mathematics of Control, Signals, and Systems. - 0932-4194 .- 1435-568X. ; 13:4, s. 333-346
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Journal article (peer-reviewed)abstract
- The objective of this paper is to study the problem of continuous-time blind deconvolution of a pulse amplitude modulated signal propagated over an unknown channel and perturbed by additive noise. The main idea is to use so-called Laguerre filters to estimate a continuous-time model of the channel. Laguerre-filter-based models can be viewed as an extension of finite-impulse-response (FIR) models to the continuous-time case, and lead to compact and parsimonious linear-in-the-parameters models. Given an estimate of the channel, different symbol estimation techniques are possible. Here, the shift property of Laguerre filters is used to derive a minimum mean square error estimator to recover the transmitted symbols. This is done in a way that closely resembles recent FIR-based schemes for the corresponding discrete-time case. The advantage of this concept is that physical a priori information can be incorporated in the model structure, like the transmitter pulse shape.
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| 5. |
- Heuberger, Peter S. C., et al.
(author)
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Orthonormal basis functions in time and frequency domain : Hambo transform theory
- 2004
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In: SIAM Journal of Control and Optimization. - 0363-0129 .- 1095-7138. ; 42:4, s. 1347-1373
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Journal article (peer-reviewed)abstract
- The class of finite impulse response (FIR), Laguerre, and Kautz functions can be generalized to a family of rational orthonormal basis functions for the Hardy space H2 of stable linear dynamical systems. These basis functions are useful for constructing efficient parameterizations and coding of linear systems and signals, as required in, e.g., system identification, system approximation, and adaptive filtering. In this paper, the basis functions are derived from a transfer function perspective as well as in a state space setting. It is shown how this approach leads to alternative series expansions of systems and signals in time and frequency domain. The generalized basis functions induce signal and system transforms (Hambo transforms), which have proved to be useful analysis tools in various modelling problems. These transforms are analyzed in detail in this paper, and a large number of their properties are derived. Principally, it is shown how minimal state space realizations of the system transform can be obtained from minimal state space realizations of the original system and vice versa.
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| 6. |
- Krishnamurthy, Vikram, et al.
(author)
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A Value Iteration Algorithm for Partially Observed Markov Decision Process Multi-armed Bandits
- 2004
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In:
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Conference paper (peer-reviewed)abstract
- A value iteration based algorithm is given for computing the Gittins index of a Partially Observed Markov Decision Process (POMDP) Multi-armed Bandit problem. This problem concerns dynamical allocation of efforts between a number of competing projects of which only one can be worked on at any time period. The active project evolves according to a finite state Markov chain and generates then a reward, while the states of the idle projects remain fixed. In this contribution, it is assumed that the state of the active project only can be indirectly observed from noisy observations. The objective is to find the optimal policy based on partial information to determine which project to work on at a certain time in order to maximize the total expected reward. The solution is obtained by transforming the problem into a standard POMDP problem, for which there exist efficient near-optimal algorithms. A numerical example from the field of task planning for an autonomous robot is presented to illustrate the algorithms.
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| 7. |
- Krishnamurthy, Vikram, et al.
(author)
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Algorithms for scheduling of hidden Markov model sensors
- 2001
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In: Proceedings of the IEEE Conference on Decision and Control. - Orlando, FL. ; , s. 4818-4819
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Conference paper (peer-reviewed)abstract
- Consider the Hidden Markov model estimation problem where the realization of a single Markov chain is observed by a number of noisy sensors. The sensor scheduling problem for the resulting Hidden Markov model is as follows: Design an optimal algorithm for selecting at each time instant, one of the many sensors to provide the next measurement. Each measurement has an associated measurement cost. The problem is to select an optimal measurement scheduling policy, so as to minimize a cost function of estimation errors and measurement costs.
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| 8. |
- Krishnamurthy, Vikram, et al.
(author)
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Finite dimensional algorithms for optimal scheduling of hidden Markov model sensors
- 2001
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In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - Salt Lake, UT. ; , s. 3973-3976
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Conference paper (peer-reviewed)abstract
- Consider the Hidden Markov model estimation problem where the realization of a single Markov chain is observed by a number of noisy sensors. The sensor scheduling problem for the resulting Hidden Markov model is as follows: Design an optimal algorithm for selecting at each time instant, one of the many sensors to provide the next measurement. Each measurement has an associated measurement cost. The problem is to select an optimal measurement scheduling policy, so as to minimize a cost function of estimation errors and measurement costs. The problem of determining the optimal measurement policy is solved via stochastic dynamic programming. An optimal finite dimensional algorithm is presented along with numerical results.
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| 9. |
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| 10. |
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| 11. |
- Wahlberg, Bo, 1959-
(author)
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Orthogonal rational functions : A transformation analysis
- 2003
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In: SIAM Review. - 0036-1445 .- 1095-7200. ; 45:4, s. 689-705
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Journal article (peer-reviewed)abstract
- Finite impulse response (FIR) models are among the most basic tools in control theory and signal processing and are routinely used in almost all fields of application. The connections to orthogonal polynomials are well known. However, infinite impulse response (IIR) models often provide much more compact descriptions and in many cases give improved performance. The objective of this paper is to present a simple framework for the derivation and analysis of orthogonal IIR transfer functions, which are directly related to orthogonal rational functions. Orthogonality simplifies approximation analysis and leads to improved numerical properties. The basic idea is to use a fractional transformation to map the problem to a new domain, where an FIR description is most appropriate. This FIR representation is then mapped back to the original domain to give an orthogonal IIR representation. It is then straightforward to extend many results for FIR. models to IIR model structures with arbitrary stable poles; i.e., properties of orthogonal polynomials are easily generalized to orthogonal rational functions. Much of the theory to be presented is classical, e.g., Laguerre and Kautz functions, and we will make use of well-known results in orthogonal filter theory. However, our main contribution is to present a uniform and transparent theory which also covers more novel results that have mainly been presented in the signals, systems, and control literature in the last decade.
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| 12. |
- Wigren, Torbjörn, et al.
(author)
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Analysis of a low-complexity change detection scheme
- 2000
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In: International journal of adaptive control and signal processing (Print). - 0890-6327 .- 1099-1115. ; 14:5, s. 481-503
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Journal article (peer-reviewed)abstract
- In many applications, for example in fault detection, it is important to discriminate between changes in system dynamics and abrupt changes in the disturbance level. A new low-complexity change detection method based on the average behaviour of the estimated impulse response parameters of the normalized least mean-square (NLMS) algorithm is presented. The solution includes second-order Kalman filters based on exponential transient models for parameter convergence. Explicit formulas for time-varying state covariances and Kalman gains are given. The receiver operating characteristics (ROC) is also computed and used for performance evaluation. The effects of the approximations in the averaging analysis that occur for high adaptation gains are handled with an experimental ROC analysis.
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| 13. |
- Åkerblad, Magnus, et al.
(author)
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Automatic tuning for classical step-response specifications using iterative feedback tuning
- 2000
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In: 39th IEEE Confernce on Decision and Control. ; , s. 3347-3348
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Conference paper (peer-reviewed)abstract
- The objective of this contribution is to study how to tune PID controllers with respect to classical step-response specifications using iterative feedback tuning. Typically the closed-loop response is improved considerably using only six to nine closed-loop experiments.
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