Generating and solving the Chen-Fliess-Sussmann (CFS) equation for a given representation of motion is a crucial step in deriving controls to steer nilpotent nonholonomic systems using the Lafferriere-Sussmann method....
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Generating and solving the Chen-Fliess-Sussmann (CFS) equation for a given representation of motion is a crucial step in deriving controls to steer nilpotent nonholonomic systems using the Lafferriere-Sussmann method. the equation can be quite complicated, and its derivation differs substantially from one representation to another. therefore instead to derive CFS for a given hard-to-compute representation we propose to derive it for any easy-to-compute representation and then to transform it to the given representation. For this purpose the Campbell-Baker-Hausdorff-Dynkin formula is applied. this approach is illustrated on generating and solving CFS for forward, backward and canonical representations.
In the paper an algorithm of 3D data registration based on CUDA implementation is shown. the research is related to the problem of collecting 3D data with laser measurement system mounted on rotated head, to be used i...
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In the paper an algorithm of 3D data registration based on CUDA implementation is shown. the research is related to the problem of collecting 3D data with laser measurement system mounted on rotated head, to be used in mobile robot applications. Assumed performance of data registration algorithm is achieved, therefore it can used as On-line. the ICP (Iterative Closest Point) approach is chosen as registration method. Computation is based on massively parallel architecture of NVIDIA CUDA. the presented concept of 3D data matching is based on parallel computation used for fast nearest neighbor search. Nearest neighbor search procedure is using 3D space decomposition into cubic buckets, therefore the time of matching is deterministic.
Reducing exhaust emissions is one of the biggest challenges facing the automotive engineering community today. In order to comply with future more stringent emission standards, the air-fuel ratio control must be impro...
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Reducing exhaust emissions is one of the biggest challenges facing the automotive engineering community today. In order to comply with future more stringent emission standards, the air-fuel ratio control must be improved. Commonly used control concepts do not account for the oxygen storage ability of the three-way catalyst, even though the storage state mainly influences post-catalyst exhaust emissions. In this paper a new air-fuel ratio control strategy based on the oxygen storage state is presented. Since the oxygen storage state can not be measured directly, a major component of the controller is a newly developed catalyst model that accounts for the spatial distribution of oxygen within the catalyst. the proposed control concept has been successfully tested on a vehicle equipped with a turbocharged, direct injection gasoline engine.
In this paper, we consider an optimal sequence of tasks for systems that improve their performances due to autonomous learning (learning-by-doing). In particular, we focus on a problem of determining sequence of perfo...
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In this paper, we consider an optimal sequence of tasks for systems that improve their performances due to autonomous learning (learning-by-doing). In particular, we focus on a problem of determining sequence of performed tasks for the autonomous learning systems to minimize the total weighted completion times of tasks. Fundamental for the presented approach is that schedule (a sequence of tasks) allows to efficiently utilize learning abilities of the system to optimize its objective, but it does not affect the system itself. To solve the problem, we prove an eliminating property that is used to construct a branch and bound algorithm and present some fast heuristic and metaheuristic methods. An extensive analysis of the efficiency of the proposed algorithms is also provided.
Many various algorithms are developed for state estimation of dynamic switching systems. It is not a straightforward task to choose the most suitable one. this paper deals with testing of state estimation via two well...
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We consider the following problem of error estimation for the optimal control of nonlinear parabolic partial differential equations: Let an arbitrary control function be given. How far is it from the next locally opti...
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We consider the following problem of error estimation for the optimal control of nonlinear parabolic partial differential equations: Let an arbitrary control function be given. How far is it from the next locally optimal control? Under natural assumptions including a second order sufficient optimality condition for the (unknown) locally optimal control, we are able to estimate the distance between the two controls. To do this, we need some information on the lowest eigenvalue of the reduced Hessian. We apply this technique to a model reduced optimal control problem obtained by proper orthogonal decomposition (POD). the distance between a (suboptimal) local solution of the reduced problem to a local solution of the original problem is estimated.
the paper presents innovative, reconfigurable simulator of mobile robots included in anti-crisis operations system. the simulator development is a part of the PROTEUS project. Along the whole anti-crisis system is dev...
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the paper presents innovative, reconfigurable simulator of mobile robots included in anti-crisis operations system. the simulator development is a part of the PROTEUS project. Along the whole anti-crisis system is developed, the simulator of mobile robots is constructed. Majority of the simulators developed worldwide are intended for specific vehicles and used for various applications of particular robots. this approach does not satisfy developers from Poland because the anti-crisis operations system has an open architecture and allows to introduce different mobile robots into action. In the paper the requirements and architecture of the simulator are described. Its integration with training system of all parts of anti-crisis operations is discussed. the paper shows the novelty of the solution (both in hardware and software).
Control-oriented magnetic models for solenoid actuators are analyzed and evaluated subject to their capabilities to reflect important features of the magnetic properties. Moreover, it is shown that undesired tuning re...
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Control-oriented magnetic models for solenoid actuators are analyzed and evaluated subject to their capabilities to reflect important features of the magnetic properties. Moreover, it is shown that undesired tuning requirements can be omitted by use of flux channel models. the focus here is put on the modeling of eddy currents since the accurate calculation of eddy current losses is a requirement for control strategies that are designed to minimize these losses. It is shown that flux channel models provide good results while the computational burden is low in comparison to finite element models. However, flux channel models require an additional step when used in flatness-based optimization schemes. this step is called state-decomposition and is explained in the second part of this paper.
For high-dimensional data processing, like pattern recognition, it seems desirable to precede with a reduction of the number of describing features. Our aim is a comparison of various feature selection methods for pat...
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For high-dimensional data processing, like pattern recognition, it seems desirable to precede with a reduction of the number of describing features. Our aim is a comparison of various feature selection methods for pattern recognition. We consider two-class supervised classification problem for signals decomposed in wavelet bases. We test kNN classification rule with soft and hard thresholding, performed in two stages: (1) wavelet detail coefficient thresholding (noise reduction) and (2) searching for the most differentiating coefficients between classes (selection of discriminating coefficients). We present a new classification rule based on LARS/LASSO. We compare criteria for L1-norm regularization of wavelet coefficients: AIC, BIC and the thresh derived for kNN rule. there were performed simulations for noisy signals with SNR in the range from 0 to 22 [dB], approximated for all possible wavelet resolutions. the quality of pattern recognition for the presented algorithms was measured by the estimated recognition risk and the size of reduced model.
this paper is concerned withthe finite-dimensional approximation of a fractional-order system represented in state-space form. To this purpose, resort is made to the Oustaloup method for approximating a fractional-or...
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this paper is concerned withthe finite-dimensional approximation of a fractional-order system represented in state-space form. To this purpose, resort is made to the Oustaloup method for approximating a fractional-order integrator by a rational filter. By applying this method to the RHS of the state equation of the fractional-order system, a matrix differential equation is obtained. this equation is then realized in a state-space form whose state matrix exhibits a (sparse) block-companion structure. To reduce the dimension of this integer-order model, an efficient method for L 2 approximation can profitably be applied. Numerical simulations show that the suggested approach compares favourably with alternative techniques recently presented in the literature to the same purpose.
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