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Control Systems, IEEE

Issue 2 • Date April 1992

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Displaying Results 1 - 12 of 12
  • Reinforcement learning is direct adaptive optimal control

    Publication Year: 1992 , Page(s): 19 - 22
    Cited by:  Papers (46)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (414 KB)  

    Neural network reinforcement learning methods are described and considered as a direct approach to adaptive optimal control of nonlinear systems. These methods have their roots in studies of animal learning and in early learning control work. An emerging deeper understanding of these methods is summarized that is obtained by viewing them as a synthesis of dynamic programming and stochastic approxi... View full abstract»

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  • Intelligent control using neural networks

    Publication Year: 1992 , Page(s): 11 - 18
    Cited by:  Papers (53)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (788 KB)  

    Intelligent control, using neural networks, of complex dynamical systems in the presence of structural failures is addressed. After a failure occurs, the dynamical system is assumed to be in one of a finite number of configurations, with a stabilizing controller corresponding to each. A two-level controller structure is used, in which the higher level detects a failure using pattern recognition te... View full abstract»

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  • Theory and development of higher-order CMAC neural networks

    Publication Year: 1992 , Page(s): 23 - 30
    Cited by:  Papers (130)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (635 KB)  

    The cerebellar model articulation controller (CMAC) neural network is capable of learning nonlinear functions extremely quickly due to the local nature of its weight updating. The rectangular shape of CMAC receptive field functions, however, produces discontinuous (staircase) function approximations without inherent analytical derivatives. The ability to learn both functions and function derivativ... View full abstract»

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  • Self-organizing visual servo system based on neural networks

    Publication Year: 1992 , Page(s): 31 - 36
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (580 KB)  

    A neural-network-based self-organizing control system for a robotic manipulator is presented. The end-effector position and orientation control loop is closed using visual data to generate the necessary control inputs for the manipulator joints by two neural networks. The task considered is to move the manipulator end-effector to a position where an object can easily be gripped. The relations betw... View full abstract»

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  • Hierarchical neurocontroller architecture for robotic manipulation

    Publication Year: 1992 , Page(s): 37 - 41
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (497 KB)  

    A hierarchical neurocontroller architecture consisting of two artificial neural network systems for the manipulation of a robotic arm is presented. The higher-level network system participates in the delineation of the robot arm workspace and coordinates transformation and the motion decision-making process. The lower-level network provides the correct sequence of control actions. A straightforwar... View full abstract»

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  • Disturbance pattern classification and neuro-adaptive control

    Publication Year: 1992 , Page(s): 42 - 48
    Cited by:  Papers (2)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (682 KB)  

    An adaptation strategy is given that is based on an analysis of patterns exhibited in the recent history of the controller error and manipulated process input variable. The focus of the strategy is on adaptation requirements due to load disturbances. The manipulated input pattern associated with a disturbance is analyzed to determine whether the disturbance has the potential for changing process c... View full abstract»

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  • Implementations of learning control systems using neural networks

    Publication Year: 1992 , Page(s): 49 - 57
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (775 KB)  

    The systematic storage in neural networks of prior information to be used in the design of various control subsystems is investigated. Assuming that the prior information is available in a certain form (namely, input/output data points and specifications between the data points), a particular neural network and a corresponding parameter design method are introduced. The proposed neural network add... View full abstract»

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  • Robust control for high-performance materials testing

    Publication Year: 1992 , Page(s): 63 - 70
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (647 KB)  

    The use of root-locus-based robust design techniques in the design of a digital controller for a high-performance variable-amplitude fatigue test is described. Evaluation of the resulting controller on a prototype laboratory materials testing system, designed to simulate the mechanical stress loads experienced by certain aircraft components, shows that it meets design performance goals of 0.25% sp... View full abstract»

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  • Self-tuning control of a solar power plant with a distributed collector field

    Publication Year: 1992 , Page(s): 72 - 78
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (778 KB)  

    A self-tuning proportional-integral controller for a solar distributed collector field that is based on a pole-assignment approach employing serial compensation to cope with measurable external disturbances is presented. The scheme is compared with the more commonly adopted parallel feedforward approach by both simulation and implementation on the plant itself.<> View full abstract»

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  • Progressive reasoning for real-time intelligent computing

    Publication Year: 1992 , Page(s): 79 - 83
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (428 KB)  

    It is argued that the success of real-time intelligent computing hinges on reconciling the unpredictability and vigor of the inference process with the real-time requirement that control system responses must be both appropriate and timely. The use of progressive reasoning, which provides a means of relaxing the demands made on the inference process without violating real-time constraints, is cons... View full abstract»

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  • Experiments illustrating the importance of automated reasoning

    Publication Year: 1992 , Page(s): 84 - 92
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (800 KB)  

    Experimental results that demonstrate the need to incorporate automated reasoning in the design of a controller for a spacecraft nuclear reactor are presented. It is argued that control systems intended for safety-constrained nonlinear processes should not only provide such traditional functions as tracking a demanded trajectory but also incorporate methods for planning, prediction, and assessment... View full abstract»

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  • Design challenges for control of molecular dynamics

    Publication Year: 1992 , Page(s): 93 - 94
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (197 KB)  

    Material modification at the molecular scale is considered. Optimal control is explored as a theoretical framework for exploiting molecular dynamics. The possibility of using optical fields to break bonds selectively is discussed.<> View full abstract»

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Aims & Scope

As the official means of communication for the IEEE Control Systems Society, the IEEE Control Systems Magazine publishes interesting, useful, and informative material on all aspects of control system technology for the benefit of control educators, practitioners, and researchers.

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Editor-in-Chief
Jonathan P. How
jhow@mit.edu