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Control Systems, IEEE

Issue 2 • Date April 1994

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Displaying Results 1 - 6 of 6
  • Magnetic bearing control systems and adaptive forced balancing

    Page(s): 4 - 13
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (912 KB)  

    Active magnetic bearing (AMB) actuators support rotors without friction but require feedback control for stabilization and performance. We address the application of modern control techniques such as LQG/LTR, H/spl infin/, and QFT to AMB systems. We also introduce a novel method called adaptive forced balancing (AFB) which solves the problem of synchronous vibration caused by mass unbalance. Simulation and experimental results are included to show the performance of AFB as applied to single-end AMB suspensions (SISO systems). Finally, AFB with frequency tracking and its generalization for double-end AMB suspensions (MIMO systems) are briefly explored.<> View full abstract»

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  • Quantitative control of manipulator/task interaction

    Page(s): 14 - 25
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1222 KB)  

    An architecture-independent analysis approach is described for evaluation of control systems designed to provide compliant manipulator/task interaction. Two issues are highlighted: a new safety bound which provides an explicit, quantitative design objective; and an examination of typical task dynamics and their impact on the difficulty (complexity) of control design. Two examples of control design within this framework are presented, demonstrating the need to consider safety bounds of the type proposed.<> View full abstract»

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  • Neural network observer for induction motor control

    Page(s): 26 - 37
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1071 KB)  

    In many practical problems the task is the control of a nonlinear plant, under parameter uncertainty, by a controller of known structure that uses the values of the state variables. However, it frequently is the case that some state variables cannot be measured. In this article this type of problem arises from the control requirements of an induction motor and is tackled by neural network based observer techniques so that the state variables are estimated while the variations of the unknown parameters are compensated for. Extensive simulations have shown that this scheme works well in the case of motor control.<> View full abstract»

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  • Learning control for underwater robotic vehicles

    Page(s): 39 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (754 KB)  

    Underwater robotic vehicles have become an important tool for various underwater tasks because they have greater speed, endurance, and depth capability, as well as a higher factor of safety, than human divers. However, most vehicle control system designs have been based on a simplified vehicle model, which has often resulted in poor performance because the nonlinear and time-varying vehicle dynamics have parameter uncertainty. It was also observed by experiment that the thruster system had nonlinear behavior and its effect on vehicle motion was significant. It is desirable to have an advanced control system with the capability of learning and adapting to changes in the vehicle dynamics and parameters. This article describes a learning control system using neural networks for underwater robotic vehicles. Its effectiveness is investigated by simulation.<> View full abstract»

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  • Logarithmic root loci for continuous-time loops

    Page(s): 47 - 52
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (625 KB)  

    We propose that the logarithms of closed-loop poles be drawn instead of the plain poles in root loci for continuous-time linear loops, to help visualize significant closed-loop frequency responses simultaneously at specific values of the changing open-loop parameters. We also promote the splitting of the loci into pairs of graphs showing the imaginary and real parts of the logarithms of the poles as separate functions of the changing parameters, since this allows one to foresee the effects of parameter changes on the frequency responses and, therefore, to build computational environments in which the user may steer the design of feedback control systems.<> View full abstract»

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  • Modeling programmable logic controllers for logic verification

    Page(s): 53 - 59
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (619 KB)  

    Verification method has been developed for determining the safety and operability of programmable logic controller (PLC) based systems. The method automatically checks sequential logic embedded in PLCs and provides counterexamples if it finds errors. The method consists of a system model, assertions, and a model checker. The model is a Boolean-based representation of a PLC's behavior. Assertions are questions about the behavior of the system, expressed in temporal logic. The model checker generates a state space based on the above two inputs, searches the space efficiently, determines the consistency of the model and assertions, and supplies counterexamples. A modeling technique has been developed to verify relay ladder logic (RLL), a PLC programming language. The performance of the model checker is studied in a series of alarm designs.<> View full abstract»

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