advanced powerful tools are sought for in order to conduct power system studies, especially for large scale systems. In this paper, an efficient PC-based environment for power system studies is presented. It is based ...
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advanced powerful tools are sought for in order to conduct power system studies, especially for large scale systems. In this paper, an efficient PC-based environment for power system studies is presented. It is based on both analytical and artificial intelligence techniques. To demonstrate its effectiveness, this environment has been used for restoration studies following a recent blackout in the Hellenic power system, with very promising results.
The increasing demand for robotic applications in unstructured environments is motivating the need for dextrous end-effecters which can cope with the wide variety of tasks and objects encountered in these environments...
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The increasing demand for robotic applications in unstructured environments is motivating the need for dextrous end-effecters which can cope with the wide variety of tasks and objects encountered in these environments, A new anthropomorphic robot hand (Anthrobot) which was designed for these environments is described. In this paper, the problem of developing the kinematics and unit consistent hybrid position/force control of the Anthrobot fingers is considered as the first step toward its utilization for either teleoperation or autonomous operation, A dynamic model of the fingers is identified and used to develop the control laws, The experimental results of applying the control to the Anthrobot are discussed.
An assembly is defined by a configuration of parts of known geometries subject to tolerances in the pose, dimensions, and mating relations among part features. Using a tolerance model based on matrix transforms and Ga...
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An assembly is defined by a configuration of parts of known geometries subject to tolerances in the pose, dimensions, and mating relations among part features. Using a tolerance model based on matrix transforms and Gaussian models of geometric variations, the pose and dimensional tolerance models are considered as a priori models of the assembly with nominal and variational components for both position and orientation. The mating relations are regarded as linear relational constraints, also with nominal and variational components. With this formulation, estimation of the configuration of parts may be posed as a maximum likelihood problem and solved by a Kalman filter algorithm. The resulting maximum likelihood configuration of the assembly may be used to evaluate the required deviation from nominal and the assemblability as defined by the maximum likelihood clearance from constraints. In addition, application of the technique to intermediate subassemblies may be used to evaluate assemblability of specific steps and discriminate among alternative assembly sequence plans.
A modular dynamic neural network which has been used in preceding research to solve uncertain algebraic problems and to represent and compress signals, is applied to the representation of an adjustment control in the ...
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A modular dynamic neural network which has been used in preceding research to solve uncertain algebraic problems and to represent and compress signals, is applied to the representation of an adjustment control in the optimal control of simple nonlinear systems. The optimal control method implemented is based on the loop transfer recovery (LTR) technique, and the basis for signal representation is composed of Daubechies wavelets. Averaging the optimal controllers obtained at three representative equilibrium states followed by using the adjustment control signal leads to a single structure capable of controlling the system over a wider range of operation. A simple inverted pendulum example is presented, and an averaging optimal controller is designed. The neural network is used to represent an adjustment control signal by a linear combination of six wavelets. The resulting signal is incorporated with the averaging LTR controller in order to enhance its performance. Simulation results are presented and discussed.
We describe laboratory experiments, in which tactile data obtained from the finger-tips of a robot hand, while it is holding an object in front of a calibrated camera, is fused with the vision data from the camera, to...
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We describe laboratory experiments, in which tactile data obtained from the finger-tips of a robot hand, while it is holding an object in front of a calibrated camera, is fused with the vision data from the camera, to determine the object identity, pose, and the touch and vision data correspondences. The touch data is incomplete due to required hand configurations, while nearly half of the vision data are spurious due to the presence of the hand in the image. Using either sensor alone results in ambiguous or incorrect interpretations. A minimal representation size framework is used to formulate the multisensor fusion problem, and can automatically select the object class, correspondence (data subsamples), and pose parameters. The experiments demonstrate that it consistently finds the correct interpretation, and is a practical method for multisensor fusion and model selection.
For pt.I see ibid., p.3726-30. Our work has been focused on developing a methodology for process control of bulk deformation-specifically, extrusion. In Part I, we developed a dynamical system model which could be use...
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For pt.I see ibid., p.3726-30. Our work has been focused on developing a methodology for process control of bulk deformation-specifically, extrusion. In Part I, we developed a dynamical system model which could be used for real-time applications. In this paper, we use this model development plus output relationships from our previous model development work (1995, 1996) to develop parameter identification and open-loop control methodologies. An example with plant trial data is provided.
Our work has been focused on developing a methodology for process control of bulk deformation-specifically, extrusion. A process model is required for control. The difficulties with currently available models are stat...
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Our work has been focused on developing a methodology for process control of bulk deformation-specifically, extrusion. A process model is required for control. The difficulties with currently available models are stated. An appropriate decomposition and associated assumptions are introduced, permitting construction a real-time process model via spectral approximation to solve the axi-symmetric, 2D transient heat conduction equation with heat generation and loss. This solution, simulation results, and model execution times are provided.
The passivity property of dissipative mechanical structures has long been exploited in designing passive controllers that provide robust stability even in the presence of unmodeled dynamics of the system. For an input...
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The passivity property of dissipative mechanical structures has long been exploited in designing passive controllers that provide robust stability even in the presence of unmodeled dynamics of the system. For an input/output map of a flexible structure to be passive, collocation of the actuators and sensors is required and also the sensors should measure the velocity of the system. The so-called smart structures frequently have more sensors than actuators. Therefore, the passive controller can at best utilize only a subset of the sensors. In this paper we consider the design of a squaring down matrix which would render a system passive subject to some additional performance considerations. The problem of obtaining the synthesized passive output is cast as a set of linear matrix inequalities (LMIs) which can be efficiently solved by the LMI Toolbox in Matlab. We apply this procedure with the assumption that the sensors provide both displacement and velocity information which is generally not true. We show that the passive controllers can be implemented without the use of velocity information. By using synthesized passive outputs in addition to naturally occurring passive outputs, we obtain better system performance. We present experimental results involving a single flexible beam with torque input and hub angular position and strain gage outputs.
The paper is concerned with the application of quadratic optimization for motion control to feedback control of robotic systems using neural networks. Explicit solutions to the Hamilton-Jacobi-Bellman (HJB) equation f...
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The paper is concerned with the application of quadratic optimization for motion control to feedback control of robotic systems using neural networks. Explicit solutions to the Hamilton-Jacobi-Bellman (HJB) equation for optimal control of robotic systems are found by solving an algebraic Riccati equation. It is shown how neural networks can cope with nonlinearities through optimization with no preliminary off-line learning phase required. The adaptive learning algorithm is derived from Lyapunov stability analysis, so that both system tracking stability and error convergence can be guaranteed in the closed-loop system. The filtered tracking error or critic gain and the Lyapunov function for the nonlinear analysis are derived from the user input in terms of a specified quadratic performance index. Simulation results on a two-link robot manipulator show the satisfactory performance of the proposed control schemes even in the presence of large modeling uncertainties and external disturbances.
We consider the feedback stabilization of nonlinear systems. The design methodology is based on a class of iterative methods, which has previously been proposed for the path planning and feedback stabilization of nonh...
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We consider the feedback stabilization of nonlinear systems. The design methodology is based on a class of iterative methods, which has previously been proposed for the path planning and feedback stabilization of nonholonomic systems. We show that this scheme guarantees the closed loop asymptotic stability for a class of nonlinear systems.
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