930 resultados para computer control


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IEEE; IEEE Computer Society; IEEE Technical Committee on Scalable Computing (TCSC)

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提出了一种用于工业机器人时间最优轨迹规划及轨迹控制的新方法,它可以确保在关节位移、速度、加速度以及二阶加速度边界值的约束下,机器人手部沿笛卡尔空间中规定路径运动的时间阳短。在这种方法中,所规划的关节轨迹都采用二次多项式加余弦函数的形式,不仅可以保证各关节运动的位移、速度 、加速度连续而且还可以保证各关节运动的二阶加速度连续。采用这种方法,既可以提高机器人的工作效率又可以延长机器人的工作寿命以PUMA560机器人为对象进行了计算机仿真和机器人实验,结果表明这种方法是正确的有效的。它为工业机器人在非线性运动学约束条件下的时间最优轨迹规划及控制问题提供了一种较好的解决方案。

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The buckling of compressively-loaded members is one of the most important factors limiting the overall strength and stability of a structure. I have developed novel techniques for using active control to wiggle a structural element in such a way that buckling is prevented. I present the results of analysis, simulation, and experimentation to show that buckling can be prevented through computer-controlled adjustment of dynamical behavior.sI have constructed a small-scale railroad-style truss bridge that contains compressive members that actively resist buckling through the use of piezo-electric actuators. I have also constructed a prototype actively controlled column in which the control forces are applied by tendons, as well as a composite steel column that incorporates piezo-ceramic actuators that are used to counteract buckling. Active control of buckling allows this composite column to support 5.6 times more load than would otherwise be possible.sThese techniques promise to lead to intelligent physical structures that are both stronger and lighter than would otherwise be possible.

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This report presents issues relating to the kinematics and control of dexterous robotic hands using the Utah-MIT hand as an illustrative example. The emphasis throughout is on the actual implementation and testing of the theoretical concepts presented. The kinematics of such hands is interesting and complicated owing to the large number of degrees of freedom involved. The implementation of position and force control algorithms on such tendon driven hands has previously suffered from inefficient formulations and a lack of sophisticated computer hardware. Both these problems are addressed in this report. A multiprocessor architecture has been built with high performance microcomputers on which real-time algorithms can be efficiently implemented. A large software library has also been built to facilitate flexible software development on this architecture. The position and force control algorithms described herein have been implemented and tested on this hardware.

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Control of machines that exhibit flexibility becomes important when designers attempt to push the state of the art with faster, lighter machines. Three steps are necessary for the control of a flexible planet. First, a good model of the plant must exist. Second, a good controller must be designed. Third, inputs to the controller must be constructed using knowledge of the system dynamic response. There is a great deal of literature pertaining to modeling and control but little dealing with the shaping of system inputs. Chapter 2 examines two input shaping techniques based on frequency domain analysis. The first involves the use of the first deriviate of a gaussian exponential as a driving function template. The second, acasual filtering, involves removal of energy from the driving functions at the resonant frequencies of the system. Chapter 3 presents a linear programming technique for generating vibration-reducing driving functions for systems. Chapter 4 extends the results of the previous chapter by developing a direct solution to the new class of driving functions. A detailed analysis of the new technique is presented from five different perspectives and several extensions are presented. Chapter 5 verifies the theories of the previous two chapters with hardware experiments. Because the new technique resembles common signal filtering, chapter 6 compares the new approach to eleven standard filters. The new technique will be shown to result in less residual vibrations, have better robustness to system parameter uncertainty, and require less computation than other currently used shaping techniques.

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A fundamental problem in artificial intelligence is obtaining coherent behavior in rule-based problem solving systems. A good quantitative measure of coherence is time behavior; a system that never, in retrospect, applied a rule needlessly is certainly coherent; a system suffering from combinatorial blowup is certainly behaving incoherently. This report describes a rule-based problem solving system for automatically writing and improving numerical computer programs from specifications. The specifications are in terms of "constraints" among inputs and outputs. The system has solved program synthesis problems involving systems of equations, determining that methods of successive approximation converge, transforming recursion to iteration, and manipulating power series (using differing organizations, control structures, and argument-passing techniques).

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A system for visual recognition is described, with implications for the general problem of representation of knowledge to assist control. The immediate objective is a computer system that will recognize objects in a visual scene, specifically hammers. The computer receives an array of light intensities from a device like a television camera. It is to locate and identify the hammer if one is present. The computer must produce from the numerical "sensory data" a symbolic description that constitutes its perception of the scene. Of primary concern is the control of the recognition process. Control decisions should be guided by the partial results obtained on the scene. If a hammer handle is observed this should suggest that the handle is part of a hammer and advise where to look for the hammer head. The particular knowledge that a handle has been found combines with general knowledge about hammers to influence the recognition process. This use of knowledge to direct control is denoted here by the term "active knowledge". A descriptive formalism is presented for visual knowledge which identifies the relationships relevant to the active use of the knowledge. A control structure is provided which can apply knowledge organized in this fashion actively to the processing of a given scene.

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This report is concerned with the problem of achieving flexibility (additivity, modularity) and efficiency (performance, expertise) simultaneously in one AI program. It deals with the domain of elementary electronic circuit design. The proposed solution is to provide a deduction-driven problem solver with built-in-control-structure concepts. This problem solver and its knowledge base in the applicaitn areas of design and electronics are descrbed. The prgram embodying it is being used to explore the solutionof some modest problems in circuit design. It is concluded that shallow reasoning about problem-solver plans is necessary for flexibility, and can be implemented with reasonable efficiency.

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"The Structure and Interpretation of Computer Programs" is the entry-level subject in Computer Science at the Massachusetts Institute of Technology. It is required of all students at MIT who major in Electrical Engineering or in Computer Science, as one fourth of the "common core curriculum," which also includes two subjects on circuits and linear systems and a subject on the design of digital systems. We have been involved in the development of this subject since 1978, and we have taught this material in its present form since the fall of 1980 to approximately 600 students each year. Most of these students have had little or no prior formal training in computation, although most have played with computers a bit and a few have had extensive programming or hardware design experience. Our design of this introductory Computer Science subject reflects two major concerns. First we want to establish the idea that a computer language is not just a way of getting a computer to perform operations, but rather that it is a novel formal medium for expressing ideas about methodology. Thus, programs must be written for people to read, and only incidentally for machines to execute. Secondly, we believe that the essential material to be addressed by a subject at this level, is not the syntax of particular programming language constructs, nor clever algorithms for computing particular functions of efficiently, not even the mathematical analysis of algorithms and the foundations of computing, but rather the techniques used to control the intellectual complexity of large software systems.

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M J Neal and J Timmis. Timidity: A useful mechanism for robot control? Informatica - special issue on perception and emotion based control, 4(27):197-204, 2003.

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N. W. Hardy, M. H. Lee, and D. P. Barnes. Knowledge engineering in robot control. In Proceedings of Expert Systems '83, pages 70-77, Cambridge, 1983.

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Barnes, D. P., Lee, M. H., Hardy, N. W. (1983). A control and monitoring system for multiple-sensor industrial robots. In Proc. 3rd. Int. Conf. Robot Vision and Sensory Controls, Cambridge, MA. USA., 471-479.

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Garrett S.M. and Lee M.H., A Case-Based Approach to Black-Box Control Learning, Proc. Int. Conf. on Comutational Intelligence for Modelling, Control and Automation (CIMCA`99), 17-19 Feb. 1999. Vienna.

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Q. Meng and M. H. Lee, Learning and Control in Assistive Robotics for the Elderly, IEEE Conference on Robotics, Automation and Mechatronics (RAM), Singapore, 2004.

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C.R. Bull, R. Zwiggelaar and J.V. Stafford, 'Imaging as a technique for assessment and control in the field', Aspects of Applied Biology 43, 197-204 (1995)