This paper addresses the problem of implementing predictive controllers for supervisory level controlsystems. In this configuration the manipulated variables calculated by the Predictive controller are used as comman...
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This paper addresses the problem of implementing predictive controllers for supervisory level controlsystems. In this configuration the manipulated variables calculated by the Predictive controller are used as command signals for the Distributed controlsystems, which provide references to the operator-tuned local PID controllers that act on the physical system. This structure introduces the problem of loosing of performance if the inner-loop controllers are re-tuned. The paper discusses the solution to this problem based on the use of a two-degrees-of-freedom structure in the inner loop, that separates open and closed-loop properties. Both design guidelines and robustness issues are discussed.
In this paper, a method for automatic construction of a fuzzy rule-based system from numerical data using the Incremental Learning Fuzzy Neural (ILFN) network and the Genetic Algorithm is presented. The ILFN network w...
In this paper, a method for automatic construction of a fuzzy rule-based system from numerical data using the Incremental Learning Fuzzy Neural (ILFN) network and the Genetic Algorithm is presented. The ILFN network was developed for pattern classification applications. The ILFN network, which employed fuzzy sets and neural network theory, equips with a fast, one-pass, on-line, and incremental learning algorithm. After trained, the ILFN network stored numerical knowledge in hidden units, which can then be directly interpreted into if then rule bases. However, the rules extracted from the ILFN network are not in an optimized fuzzy linguistic form. In this paper, a knowledge base for fuzzy expert system is extracted from the hidden units of the ILFN classifier. A genetic algorithm is then invoked, in an iterative manner, to reduce number of rules and select only discriminate features from input patterns needed to provide a fuzzy rule-based system. Three computer simulations using a simulated 2-D 3-class data, the well-known Fisher's Iris data set, and the Wisconsin breast cancer data set were performed. The fuzzy rule-based system derived from the proposed method achieved 100% and 97.33% correct classification on the 75 patterns for training set and 75 patterns for test set, respectively. For the Wisconsin breast cancer data set, using 400 patterns for training and 299 patterns for testing, the derived fuzzy rule-based system achieved 99.5% and 98.33% correct classification on the training set and the test set, respectively.
In this work are presented results of transient stability studies, which are accomplished when assessing the effects of the connection of wind farms in a power system. For the execution of those studies a software env...
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The formation of a convoy of cars (platoon) by linking these electronically (soft platooning) or mechanically (hard platooning) are two of the most researched methods in highway transportation systems, particularly th...
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The formation of a convoy of cars (platoon) by linking these electronically (soft platooning) or mechanically (hard platooning) are two of the most researched methods in highway transportation systems, particularly the first one that is the base of the Automated Highway systems. This paper focuses on the advantages and disadvantages of both systems and introduces the main technical problems of them. The need for new traffic rules is examined in the case of truck train travels on a multi-lane highway. The kingpin sliding technique is also proposed to face the off-tracking phenomenon of multi-articulated vehicles. Some conclusions from the comparison of the two systems are also given
The motion of a multi-body autonomous robot as well as of a train-like multi-articulated transportation vehicle is characterized by the deviation of the path of each intermediate vehicle from that of the leading one (...
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The motion of a multi-body autonomous robot as well as of a train-like multi-articulated transportation vehicle is characterized by the deviation of the path of each intermediate vehicle from that of the leading one (off-tracking). This paper proposes an intelligent controller for the elimination of the off-tracking phenomenon, in both robotic and transportation multi- articulated vehicles. By using a sliding kingpin technique that allows articulated semi-trailers to follow closely the tractor's path, and taking into consideration the heuristic knowledge that stems from the driving practice and the tests' experience, a fuzzy rule-based controller has been developed to determine the sliding distance and the sliding rate of the kingpin mechanism. Simulation results for various cases, without and with the sliding kingpin system showed that off-tracking elimination is achieved to a significant degree through the use of intelligent controller.
In this paper we propose the use of inexpensive offthe- shelf hardware and software in place of high-cost commercial solutions to easily set up graphical VR environments and simulations with a human in the control loo...
In this paper we propose the use of inexpensive offthe- shelf hardware and software in place of high-cost commercial solutions to easily set up graphical VR environments and simulations with a human in the control loop. For this purpose we have developed a generic simulation environment which permits to rapidly implement control simulation and evaluation experiments. As an example setup a car simulator with a human in the control loop is presented. The user receives both 3D visual and haptic feedback while driving the simulated vehicle.
A causal iterative learning control algorithm based on optimal feedback and feedforward control is derived to provide perfect tracking of selected output values at specified times. Exponential convergence of the algor...
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Automatic recognition of frog vocalization is considered a valuable tool for a variety of biological research and environmental monitoring applications. In this research an automatic monitoring system, which can recog...
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Automatic recognition of frog vocalization is considered a valuable tool for a variety of biological research and environmental monitoring applications. In this research an automatic monitoring system, which can recognize the vocalizations of four species of frogs and can identify different individuals within the species of interest, is proposed. For the desired monitoring system, species identification is performed first with the proposed filtering and grouping algorithm. Individual identification, which can estimate frog population within the specific species, is performed in the second stage. Digital signal pre-processing, feature extraction, dimensionality reduction, and neural network pattern classification are performed step by step in this stage. Wavelet Packet feature extraction together with two different dimension reduction algorithms are synergistically integrated to produce final feature vectors, which are to be fed into a neural network classifier. The simulation results show the promising future of deploying an array of continuous, on-line environmental monitoring systems based upon nonintrusive analysis of animal calls.
Repetitive processes are a distinct class of 2D systems of both practical and theoretical interest. Their essential characteristic is repeated sweeps, termed passes, through a set of dynamics defined over a finite dur...
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This article introduces a technique for estimating samples of a random signal based on observations made by several observers and at different sampling rates. We consider a discrete-time mathematical model where an ob...
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ISBN:
(纸本)0780367154
This article introduces a technique for estimating samples of a random signal based on observations made by several observers and at different sampling rates. We consider a discrete-time mathematical model where an observer sees the original random signal x(n) through a bank of sensors which we model by linear filters and downsamplers. Each sensor, therefore, outputs a measurement signal v/sub i/(n) whose sampling rate is only a fraction of the sampling rate assumed for the original signal under observation. It is straightforward to show that the optimal least-mean-squares estimator for our problem is a linear operator F operating on v/sub i/(n)s. We observe, however, that to find F we need to know the power spectral density P/sub x/(e/sup jw/) of x(n) which is itself not observable. This motivates us to consider the possibility of estimating P/sub x/(e/sup jw/) using the observable low-rate data. We show that the statistical inference problem which addresses estimation of P/sub x/(e/sup jw/) given certain statistics of v/sub i/(n) is mathematically ill-posed. We resolve this ill-posed inference problem using the principle of maximum entropy. We show, moreover, that the proposed maximum entropy inference technique is a continuous mapping. Therefore, one might safely use it to estimate P/sub x/(e/sup jw/) based on approximate statistics of v/sub i/(n) obtained from the samples.
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