241 research outputs found
State Estimation for a Tractor Semi-trailer System using a Minimum-Energy Filter
Full text linkIn this article we apply a second-order minimum-energy filter based on Lie groups to the problem of parking a truck with a semi-trailer in a docking station. The use of the filter, that exploits the geometry of Lie groups to estimate the truck and trailer pose, is useful to improve the precision of the state and thus perform better controls. We consider two different types of measurements: the first consists of GPS-like devices that detect the positions of the front wheels of the truck and the rear wheels of the trailer, and the second improves the measurement of the rear wheels with the measurement of the pose of the trailer with a LIDAR sensor. The accuracy of the LIDAR is useful for having a better estimate when parking in reverse. We show two simulations with two different datasets
Data for OR spectrum paper simultaneous Berth allocation and yard planning at tactical level
No full textA gzipped-tar file containing the files we used for generating the result in our 2013 paper: M.P.M. Hendriks, E. Lefeber, J.T. Udding, Simultaneous berth allocation and yard planning at tactical level, OR Spectrum 35(2), 441-456, 2013
Qualitative resonance of feedback-controlled chaotic oscillators
Full text linkThe qualitative resonance of feedback-controlled chaotic oscillators is the ability of the control system to qualitatively synchronize with a reference signal similar to one of the unstable periodic orbits embedded in the open-loop attractor. This property, discovered by O. De Feo (2004a; 2004b) while studying Shilnikov-type attractors, was explained in terms of the random-like rephasing mechanism characterizing the oscillator's dynamics, so to guarantee the eventual in-phase looking with the reference forcing. We experimentally show that the phenomenon works more in general, even in the absence of a rephasing mechanism. Intuitively, the forcing by the target cycle, or by a qualitative approximation of it, is sufficient to bring in the in-phase condition. Our results can make chaos control more practicable than so far imagined, as a qualitative control can be achieved with no a-priori knowledge about the target solution
Counterexamples to the Kalman Conjectures
No full textIn the paper counterexamples to the Kalman conjecture with smooth nonlinearity basing on the Fitts system, that are periodic solution or hidden chaotic attractor are presented. It is shown, that despite the fact that Kalman’s conjecture (as well as Aizerman’s) turned out to be incorrect in the case of n > 3, it had a huge impact on the theory of absolute stability, namely, the selection of the class of nonlinear systems whose stability can be studied with linear methods.peerReviewe
Modeling and Control of Manufacturing Systems
No full textIn this chapter we provide a framework within which concepts from the field of systems and control can be used for controlling manufacturing systems. After introducing some basic notions from manufacturing analysis, we start with the concept of effective process times (EPTs) which can be used for modeling a manufacturing system as a large queuing network. Next, we restrict ourselves to mass production, which enables us to model manufacturing systems by means of a linear system subject to nonlinear constraints (clearing functions). These models serve as a starting point for designing controllers for these manufacturing systems using Model-based Predictive Control (MPC). Finally, the resulting controllers can be implemented on the queuing network model, and ultimately at the real manufacturing system.</p
Learning Linear Surrogate Models of Nonlinear Systems
No full textIn general, most dynamical systems exhibit some sort ofnonlinear behavior. However, most control and identificationapplications rely on LTI models, which are only validlocally. In recent years, the Koopman framework hasgained popularity within the control and identification communities,proposing a global linear representation of nonlinearsystems. This is achieved through the embedding of thenonlinear state-space into a possibly infinite-dimensionallifted space where the dynamics are linear and governedby the Koopman operator. In practice, only a finite numberof lifting functions is used and, while the choice of thedictionary heavily impacts the representation quality of theresulted linear model, there are little to no systematic methodsfor the selection. We address this by combining a LeastSquares Support Vector Machine (LS-SVM) regression,to estimate the nonlinear state transition map, with the linearitycondition of the Koopman form
Learning Linear Surrogate Models of Nonlinear Systems
No full textIn general, most dynamical systems exhibit some sort ofnonlinear behavior. However, most control and identificationapplications rely on LTI models, which are only validlocally. In recent years, the Koopman framework hasgained popularity within the control and identification communities,proposing a global linear representation of nonlinearsystems. This is achieved through the embedding of thenonlinear state-space into a possibly infinite-dimensionallifted space where the dynamics are linear and governedby the Koopman operator. In practice, only a finite numberof lifting functions is used and, while the choice of thedictionary heavily impacts the representation quality of theresulted linear model, there are little to no systematic methodsfor the selection. We address this by combining a LeastSquares Support Vector Machine (LS-SVM) regression,to estimate the nonlinear state transition map, with the linearitycondition of the Koopman form
Modelling Geometrically Nonlinear Flexure Mechanisms With Piezoelectric Vibration Damping
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