A parallel-jaw type multiple-d.o.f. robot hand is designed and implemented to manipulate workpieces within the hand by rotation and translation. An efficient manipulator control algorithm and part mating application i...
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A parallel-jaw type multiple-d.o.f. robot hand is designed and implemented to manipulate workpieces within the hand by rotation and translation. An efficient manipulator control algorithm and part mating application is developed. A simulation result is also presented to show the changes of force during part insertion to verify the models developed.< >
In this paper a new method for the computation of the optimal step in gradient algorithms is presented. This method improves the convergence of the gradient algorithms and outperforms any other suboptimal scheme on li...
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In this paper a new method for the computation of the optimal step in gradient algorithms is presented. This method improves the convergence of the gradient algorithms and outperforms any other suboptimal scheme on linear problems while it does not require any additional storage. The method may be also applied to problems with state or control constraints, linear time varying systems and, via linearization, to nonlinear systems as well.
The dual relation between the Model Reference Adaptive control (MRAC) and Identification (MRAI) problems is discussed. A common framework for both problems is established.
The dual relation between the Model Reference Adaptive control (MRAC) and Identification (MRAI) problems is discussed. A common framework for both problems is established.
Dynamic segmentation is a method for improving the performance of cooperating tasks executed on loosely coupled multiprocessors. Four different data transfer techniques that can be used for intersegment data transfers...
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Dynamic segmentation is a method for improving the performance of cooperating tasks executed on loosely coupled multiprocessors. Four different data transfer techniques that can be used for intersegment data transfers in dynamically segmented buses are analyzed. Preemptive data transfers are considered for uniform message latencies and results are provided for message latency comparisons using various locality factors. These results indicate dynamic segmentation improves performance considerably, at a fraction of the cost of more complex interconnection networks such as multiple buses.< >
A common framework for model reference adaptive identification and control (MRAI and MRAC) is established. The key idea for this common framework is to maintain the same set of equations for describing the parameteriz...
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A common framework for model reference adaptive identification and control (MRAI and MRAC) is established. The key idea for this common framework is to maintain the same set of equations for describing the parameterizations of the plant and the model and to solve the control equation properly for each case (identification or control) for the corresponding tuned system, i.e. the model (identification) or the plant (control). Within this framework, two methods, the MOEM (modified output error method) and the IEM (input error method), for the MRAI and MRAC problems are studied, and global asymptotic stability properties are established.< >
An effective method for neural network based visual pattern recognition is presented. It is shown that it can be successfully used for visual recognition of deformed letters. The main advantages of the presented metho...
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An effective method for neural network based visual pattern recognition is presented. It is shown that it can be successfully used for visual recognition of deformed letters. The main advantages of the presented method are its intuitive appeal, simple implementation and analytical justification.< >
A modified output error method (MOEM) for model reference adaptive control (MRAC) and identification (MRAI) is introduced. The regressors are properly chosen so that the open-loop system can be compactly expressed as ...
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A modified output error method (MOEM) for model reference adaptive control (MRAC) and identification (MRAI) is introduced. The regressors are properly chosen so that the open-loop system can be compactly expressed as a stable system with an input linear with respect to the unknown plant parameters. The output error satisfies a constructible stable filtered equation. The method does not require any strictly positive real or arbitrary stable filterings, and the uncertainty on the magnitude of the high-frequency gain of the plant does not result in an overparameterization of the identifier. Sufficient conditions on the reference input under which the parameter and output errors converge exponentially to zero are also given.< >
A new approach to solving the collision detection problem for mobile robots in the presence of moving obstacles is proposed. Both the robot and obstacles in the two-dimensional space are assumed to be polygonal and ar...
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A new approach to solving the collision detection problem for mobile robots in the presence of moving obstacles is proposed. Both the robot and obstacles in the two-dimensional space are assumed to be polygonal and are composed of a set of convex polygons. The half-plane representation uses an algebraic method to describe the object motion including translation and rotation through time. Compared with previous methods, the proposed representation is more general in that obstacles are now allowed to move at nonconstant velocities. To detect collision between polygonal objects, a traversability vector method was developed. Traversability vectors are a simple mechanism used to analyze geometric constraints imposed by polygons. Using these vectors, a simple algorithm for predicting collision is derived. computer simulations were conducted to show the feasibility of the proposed method.< >
The authors present a self-organizing artificial neural network (ANN) that exhibits deterministically reliable behavior to noise interference when the noise does not exceed a specified level of tolerance. The complexi...
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The authors present a self-organizing artificial neural network (ANN) that exhibits deterministically reliable behavior to noise interference when the noise does not exceed a specified level of tolerance. The complexity of the proposed ANN, called DIGNET, in terms of neuron requirements versus stored patterns, increases linearly with the number of stored patterns and their dimensionality. The self-organization of DIGNET is based on the idea of competitive generation and elimination of attraction wells in the pattern space. The same neural network can be used for both pattern recognition and classification.< >
This paper examines the system identification problem from the standpoint of control system design. Noting first that nearly all robust control design methods require explicit worst-case/deterministic bounds on the ex...
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This paper examines the system identification problem from the standpoint of control system design. Noting first that nearly all robust control design methods require explicit worst-case/deterministic bounds on the existing plant uncertainty, it is argued that the class of system identification methods which are inherently compatible with robust control design methods-or control-oriented is a subset of the class of system identification methods which yield an explicit worst-case/deterministic bound on the resulting identification error. An abstract theoretical framework for control-oriented system identification is then developed. This framework is inherently worst-case/deterministic in nature, and makes precise such notions as identification error, algorithm convergence, and algorithm optimality from a worst-case/deterministic standpoint. Finally, the abstract theoretical framework is utilized to formulate and solve two related control-oriented system identification problems for stable, linear, shift-invariant, distributed parameter plants. In each of these problems the assumed apriori information is minimal, consisting only of a lower bound on the relative stability of the plant, an upper bound on a certain gain associated with the plant, and an upper bound on the noise level. In neither case are any assumptions made concerning the structure of either the plant (i.e., dynamic order, relative order, etc.) or the noise (i.e., zero-mean, etc.). The first of these problems involves identification of a point sample of the plant frequency response from a noisy, finite, output time series obtained in response to an applied sinusoidal input with frequency corresponding to the frequency point of interest.
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