It is well known that conventional control theories are widely suited for applications where the processes can be reasonably described in advance. However, when the plant's dynamics are hard to characterize precis...
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It is well known that conventional control theories are widely suited for applications where the processes can be reasonably described in advance. However, when the plant's dynamics are hard to characterize precisely or are subject to environmental uncertainties, one may encounter difficulties in applying the conventional controller design methodologies. Despite the difficulty in achieving high control performance, the fine tuning of controller parameters is a tedious task that always requires experts with knowledge in both control theory and process information. Nowadays, more and more studies have focused on the development of adaptive control algorithms that can be directly applied to complex processes whose dynamics are poorly modeled and/or have severe nonlinearities. In this context, the design of a Model-Free Learning Adaptive Control (MFLAC) based oil pseudogradient concepts and optimization procedure by a Particle Swarm Optimization (PSO) approach using constriction coefficient and Henon chaotic sequences (CPSOH) is presented in this paper. PSO is a stochastic global optimization technique inspired by social behavior of bird flocking. The PSO models the exploration of a problem space by a population of particles. Each particle in PSO has a randomized velocity associated to it, which moves through the space of the problem. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed CPSOH introduces chaos mapping which introduces some flexibility in particle movements in each iteration. The chaotic sequences allow also explorations at early stages and exploitations at later stages during the search procedure of CPSOH. Motivation for application of CPSOH approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the plant has different gains for the operational range when designed by trial-and-error by User. Numerical results of the MFLAC with CPSOH tuni
In this article we analyze the M-X/G(Y)/1/K + B bulk queue. For this model, we consider three rejection policies: partial acceptance, complete rejection, and complete acceptance. For each of these policies, we are int...
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In this article we analyze the M-X/G(Y)/1/K + B bulk queue. For this model, we consider three rejection policies: partial acceptance, complete rejection, and complete acceptance. For each of these policies, we are interested in the loss probability for an arriving group of customers and for individual customers within a group. To obtain these loss probabilities, we derive a numerically stable method to compute the limiting probabilities of the queue length process under all three rejection policies. At the end of the article we demonstrate our method by means of a numerical example.
In this paper a design procedure and experimental implementation of a PID controller is presented. The PID controller is tuned according to damping optimum in order to achieve precise position control of a pneumatic s...
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In this paper a design procedure and experimental implementation of a PID controller is presented. The PID controller is tuned according to damping optimum in order to achieve precise position control of a pneumatic servo drive. It is extended by a friction compensation and stabilization algorithm in order to deal with friction effects. In a case of supply pressure variations, more robust control system is needed. It is implemented by extending the proposed PID controller with friction compensator with the gain scheduling algorithm, which is provided by means of fuzzy logic. The effectiveness of proposed control algorithms is experimentally verified on an industrial cylindrical rodless actuator controlled by a proportional valve.
Nonlinear Muskingum models are important tools in hydrological forecasting. In this paper, we have come up with a class of new discretization schemes including a parameter theta to approximate the nonlinear Muskingum ...
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Nonlinear Muskingum models are important tools in hydrological forecasting. In this paper, we have come up with a class of new discretization schemes including a parameter theta to approximate the nonlinear Muskingum model based on general trapezoid formulas. The accuracy of these schemes is second order, if theta not equal 1/3, but interestingly when theta = 1/3, the accuracy of the presented scheme gets improved to third order. Then, the present schemes are transformed into an unconstrained optimization problem which can be solved by a hybrid invasive weed optimization (HIWO) algorithm. Finally, a numerical example is provided to illustrate the effectiveness of the present methods. The numerical results substantiate the fact that the presented methods have better precision in estimating the parameters of nonlinear Muskingum models.
In this paper, we develop a new adaptive image denoising algorithm in the presence of Gaussian noise. Because the proposed method operates in the gradient domain and is close to Wiener filter, it is named as gradient-...
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In this paper, we develop a new adaptive image denoising algorithm in the presence of Gaussian noise. Because the proposed method operates in the gradient domain and is close to Wiener filter, it is named as gradient-based Wiener filter (GWF). Inspired by the Perona-Malik anisotropic diffusion (PMAD), the proposed algorithm is implemented by iterations. The parameters for the GWF are studied in full detail. At the same time, the tuning method of the gradient thresholding based on noise variance for PMAD is presented. Experimental results indicate the proposed algorithm achieves higher peak signal-to-noise ratio (PSNR) and better visual effect compared to related algorithms. On the other hand, the simulation results also show the tremendous power of the given parameter tuning method for PMAD. (C) 2012 Elsevier Ltd. All rights reserved.
This paper presents a direct-data (DD) counterpart to the covariance-based (CB) algorithm for direction-of-arrival (DOA) estimation. The proposed DD-DOA scheme provides reduced computational complexity as compared wit...
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This paper presents a direct-data (DD) counterpart to the covariance-based (CB) algorithm for direction-of-arrival (DOA) estimation. The proposed DD-DOA scheme provides reduced computational complexity as compared with other ESPRIT variations, filling in a theoretical gap not covered by previously presented schemes. A mean-squared error (MSE) analysis as well as computer simulations are provided allowing performance comparisons between DD-DOA and ESPRIT algorithms. The MSE performance is compared with the Cramer-Rao lower bound (CRLB) for one source. Results indicate that the proposed algorithm represents an efficient trade-off between computational complexity and final MSE as compared with standard ESPRIT schemes.
As far as the increasing number of mixture components in the Gaussian mixture PHD filter is concerned, an iterative mixture component pruning algorithm is proposed. The pruning algorithm is based on maximizing the pos...
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As far as the increasing number of mixture components in the Gaussian mixture PHD filter is concerned, an iterative mixture component pruning algorithm is proposed. The pruning algorithm is based on maximizing the posterior probability density of the mixture weights. The entropy distribution of the mixture weights is adopted as the prior distribution of mixture component parameters. The iterative update formulations of the mixture weights are derived by Lagrange multiplier and Lambert W function. Mixture components, whose weights become negative during iterative procedure, are pruned by setting corresponding mixture weights to zeros. In addition, multiple mixture components with similar parameters describing the same PHD peak can be merged into one mixture component in the algorithm. Simulation results show that the proposed iterative mixture component pruning algorithm is superior to the typical pruning algorithm based on thresholds.
STEPPING MOTORS ARE USED IN numerous applications because of their low manufacturing cost and simple open-loop position control capabilities. It is well known that their energy efficiency is low, although the actual e...
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STEPPING MOTORS ARE USED IN numerous applications because of their low manufacturing cost and simple open-loop position control capabilities. It is well known that their energy efficiency is low, although the actual efficiency values are generally not available. Moreover, the bulk of the stepping motors are driven in a non-optimal way, e.g., in an open loop with a maximum current to avoid step loss and, thus, with low efficiency. In this article, the impact of the control algorithm on the efficiency of the motor is analyzed, measured, and discussed. The basic open-loop full-, half-, and microstepping algorithms are considered together with a more advanced vector control algorithm. For each algorithm, the torque/current optimization is discussed.
A new and highly efficient algorithm developed under MATLAB for calculating the optical spectra generated by non-resonant optical parametric fluorescence is presented. This algorithm, which allows quick simulation of ...
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A new and highly efficient algorithm developed under MATLAB for calculating the optical spectra generated by non-resonant optical parametric fluorescence is presented. This algorithm, which allows quick simulation of the spectra, is shown to be much more rapid than standard ones. The ways to modify the algorithm for other environments are discussed.
In this study, an improved observer-based stabilising controller has been designed for networked systems involving both random measurement and actuation delays and subject to non-stationary packet dropouts. The develo...
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In this study, an improved observer-based stabilising controller has been designed for networked systems involving both random measurement and actuation delays and subject to non-stationary packet dropouts. The developed control algorithm is suitable for networked systems with any type of delays. By the simultaneous presence of binary random delays and making full use of the delay information in the measurement model and controller design, new and less conservative stabilisation conditions for networked control systems are derived. The criterion is formulated in terms of linear matrix inequalities. Detailed simulation studies on representative systems are provided to show the applicability of the developed design technique.
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