A modular controller structure for automotive powertrains has certain benefits. These include improved productivity through module reuse, seamless integration of new features, transparent removal of obsolete features,...
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A modular controller structure for automotive powertrains has certain benefits. These include improved productivity through module reuse, seamless integration of new features, transparent removal of obsolete features, and module sharing across powertrain platforms. Modular architecture also potentially reduces the complexity in the design and calibration process, in that controller modules for different subsystems are developed independently. Due to the fact that the automotive powertrain system contains many highly interactive sub-systems, it is not clear that a modular controller development process can yield acceptable feedback controller performance with respect to emissions, fuel economy, and drivability. In this paper, we describe the engineering design issues associated with a decentralized development process, and the impact that the resulting decentralized controller has upon the dynamic response of the feedback system. We describe the possible detrimental consequences of subsystem interaction, and the potential of coordinated, multivariable feedback for alleviating these limitations. control of a spark ignition engine incorporating variable camshaft timing is used as a case study.
An exhaust gas recirculation (EGR) control system for automotive application is briefly presented. Robustness is one of the main issues in this particular system due to nonlinearities, time delays, and the effects of ...
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An exhaust gas recirculation (EGR) control system for automotive application is briefly presented. Robustness is one of the main issues in this particular system due to nonlinearities, time delays, and the effects of complex exhaust gas dynamics. It was found that a good approximation of the system dynamics consists of a first order transfer function with a time delay where both transfer function coefficients and the time delay depend on the reference signal (desired EGR(%)). Moreover, it is sufficient to identify only four models corresponding to four different values of desired EGR (%) and assume interpolation for transfer function coefficients to characterize the system dynamics. Since the value of the reference signal is available in real time the controller design can be reduced to four controllers corresponding to four identified models and interpolation for coefficients of the controller transfer function in cases when the value of desired EGR (%) is between the assumed values. Simulation results for such designs are presented.
Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulat...
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Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas.< >
Iterative least-squares estimation requires accurate reflectance models to retrieve geometrical parameters of 3-D objects from an image projection. We investigate the use of separating the diffuse (body) reflection fr...
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Iterative least-squares estimation requires accurate reflectance models to retrieve geometrical parameters of 3-D objects from an image projection. We investigate the use of separating the diffuse (body) reflection from the specular (surface) reflection, where the latter is responsible for image highlights. The performance of several models has been analysed by comparing local higher-order derivatives of the least-squares error function. Experiments show that the (smooth) diffuse component yields the best convergence properties, while the (sharp) specular component cast be utilized to improve noise insensitivity.
This paper reports the application of evolutionary computation in the automatic generation of a neural network architecture. It is a usual practice to use trial and error to find a suitable neural network architecture...
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This paper reports the application of evolutionary computation in the automatic generation of a neural network architecture. It is a usual practice to use trial and error to find a suitable neural network architecture. This is not only time consuming but may not generate an optimal solution for a given problem. The use of evolutionary computation is a step towards automation in architecture generation. In this paper a brief introduction to the field is given as well as an implementation of automatic neural network generation using genetic programming.< >
作者:
Chiodo, E.Menniti, D.Testa, A.Picardi, C.Elio Chiodo (1959) received the degree in Electronics Engineering in 1985
and the Ph.D. degree in Computational Statistics both from the University of Naplefltaly. He is a Researcher at the Department of Electrical Engineering of the University of Naples and a member of the Italian Statistical Society. His areas of interest include probabilistic methods applied to electric power systems analysis. (University of Naples Fedrrico 11. Electrical Engineering Dept.via Claudio 21 1-80125 Naplefltaly T +3981/7683226 Fax+3981/2396897) Daniele Menniti (1958) received the degree in Electrical Engineering from the University of Calabria. Cosenzataly and the Ph.D. degree in Electrical Engineering from the University of NapleslItaly
in 1984 and 1989 respectively. He is a researcher at the Electronic. Computer and Systems Science Department of the University of Calabria. Italy. Hiscurrent research interests concern electric power system analysis real-time control and automation. (University of Calabria Electronic Computer and Systems Science Dep. Arcavacataji Rende (CS). 1-87036 CosenzdItaly T +39984/494707. Fax +39984/4947 13) Alfredo Testa (1950) received the degree in Electrical Engineering from the University of Naples/Italy
in 1975. He is an Associate Professor in Electrical Power Systems at the Department of Electrical Engineering of the University of Naples. He is engaged in researches on electrical power systems reliability and harmonic analysis. (University of Naples Federico 11. Electrical Engineering Dep. via Claudio '2 1 1-80 I25 NapleslItaly T + 39 8 I/7 68 3'2 11. Fax+3981/2396897) Ciro Picardi (1949) received the degree in Electronics Engineering from the University of Naples/Italy
in 1975. He is currently Associate Professor in Process Control at the Department of Electronic Computer and System Science of the University of Calabria. Italy. His current research interests are in the area of electrical drives robotics neural networks and fuzzy control. (University of Calabria Electronic. Compu
An artificial‐neural‐network (ANN) application for steady‐state security evaluation of electrical power systems is presented. Such application is based upon a combined use of a multilayer back‐propagation neural n...
The problem of design and evaluation of binary hypothesis tests based on a set of available observations is considered. A so-called structured adaptive network (SAN) configuration for the modeling and implementation o...
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The problem of design and evaluation of binary hypothesis tests based on a set of available observations is considered. A so-called structured adaptive network (SAN) configuration for the modeling and implementation of a wide class of such tests is introduced. A general framework for the analysis and performance evaluation of a SAN is developed.
We consider the problem of multisensor detection in the presence of misalignment. We assume that the region that is covered by the sensors contains subregions that constitute blind spots in the sensors' fields of ...
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We consider the problem of multisensor detection in the presence of misalignment. We assume that the region that is covered by the sensors contains subregions that constitute blind spots in the sensors' fields of view. For analytical simplicity and numerical convenience, we consider the two-sensor case only, and describe the misalignment mathematically using a model that we have developed earlier. Preliminary assumptions involve a known geometry of the regions covered by each sensor and symmetric coverage. We formulate and analyze the distributed decision problem in the presence of misalignment when the sensors transmit only local decisions to the fusion. Different combining roles are considered at the fusion and compared with a centralized fusion scheme. Numerical results in the Gaussian channel indicate that for two sensors and under the imposed assumptions, only the OR combining rule at the fusion results in performance that degrades gracefully as the coverage factor decreases. The performance of the fusion under the OR rule is comparable-although inferior-to the performance of the centralized scheme. However, the AND combining rule yields very poor performance that degrades rapidly as the coverage factor varies.
The synthesis of a fuzzy logic controller that provides a small mobile robot with the capability to exhibit a wall-following behavior is described. A general account of the procedure is given with emphasis placed on t...
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The synthesis of a fuzzy logic controller that provides a small mobile robot with the capability to exhibit a wall-following behavior is described. A general account of the procedure is given with emphasis placed on the embedded realization of the fuzzy controller and its feasibility using commercially available tools.< >
This paper describes the implementation of hierarchical control on a robotic manipulator using fuzzy logic. A decentralized control approach is implemented, i.e., individual controllers control the two links of the ro...
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This paper describes the implementation of hierarchical control on a robotic manipulator using fuzzy logic. A decentralized control approach is implemented, i.e., individual controllers control the two links of the robot. The kinematic aspect of the control is treated as the supervisory mode at a higher level and the joint control is treated as the lower level. Fuzzy logic based rules determine the inverse kinematic mapping which maps the Cartesian coordinates to the individual joint angles. This scheme is implemented using Togai Infra Logic software and the entire simulation software is implemented using C language. The results of the simulation are discussed. This experiment is a proof of principle to show that the fuzzy controller can be used to map the nonlinear mapping which can be implemented to the more complex problem of inverse kinematics of higher degree of freedom robots. A fuzzy PD controller is implemented on a Rhino robot and the performance is compared with a traditional PD controller.< >
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