SICE Journal of Control, Measurement, and System Integration
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ONLINE ISSN: 1884-9970
PRINT ISSN: 1882-4889

SICE Journal of Control, Measurement, and System Integration Vol. 13 (2020), No. 5

  • Load Frequency Control and Real-Time Pricing with Stochastic Model Predictive Control

    pp. 215-224

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    DOI:10.9746/jcmsi.13.215

    We developed a load frequency control system with stochastic model predictive control (SMPC) for power systems where the market penetration of wind power generation is high. The controller adjusts the electricity price for heat pump water heaters while at the same time controlling thermal power plants and batteries in order to maintain the frequency in the designated range. We propose an approach for solving SMPC problems on Hammerstein models including affine disturbance feedback parametrization. Simulation results show that SMPC with affine disturbance feedback parametrization outperforms both SMPC without parametrization and deterministic model predictive control in terms of the stage-cost and constraint violation.
  • Relative Position Estimation for Formation Control with the Fusion of Predicted Future Information and Measurement Data

    pp. 225-232

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    DOI:10.9746/jcmsi.13.225

    This paper addresses a relative position estimation problem for formation control of multiple robots. In the authors' previous paper, a relative position estimation method has been proposed, which fuses information from distance sensors and wireless communication. In this method, it is assumed that the robots communicate with others by wireless devices at every control sampling time. Therefore, depending on the performance of the wireless devices, the control sampling time should be set to a large value, which can degrade control performance. In this paper, we propose a new relative position estimation method, which is effective even if the communication sampling time is longer than the control sampling time. The idea in this method is to use predicted information on the time-series of the control input from detected robots. We develop a method to generate the time-series of the predicted control input for successful estimation. Finally, we verify the effectiveness of the proposed method by simulations and an experiment.
  • Stability Optimization of Positive Semi-Markov Jump Linear Systems via Convex Optimization

    pp. 233-239

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    DOI:10.9746/jcmsi.13.233

    In this paper, we study the problem of optimizing the stability of positive semi-Markov jump linear systems. We specifically consider the problems of tuning the coefficients of the system matrices for maximizing the exponential decay rate of the system under a budget-constraint and minimizing the parameter tuning cost under the decay rate constraint. By using a result from the matrix theory on the log-log convexity of the spectral radius of nonnegative matrices, we show that the stability optimization problems are reduced to convex optimization problems under certain regularity conditions on the system matrices and the cost function. We illustrate the validity and effectiveness of the proposed results by using an example from the population biology.
  • A Performance Evaluation of Periodic Signal Analysis by ARS Compared with Frequency Analysis by FFT

    pp. 240-248

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    DOI:10.9746/jcmsi.13.240

    Accumulation for real-time serial-to-parallel converter (ARS) has been proposed as a computationally-efficient method for signal analysis. Since it consists of additions and a few divisions only, it was shown that the computational load is drastically reduced compared with the fast Fourier transform (FFT). In this paper, we clarify that ARS achieves a higher resolution in low-frequency bands comparing with FFT. In addition, selection criteria between ARS and FFT are clarified theoretically in terms of the resolution. Through the performance analysis of ARS in low-frequency bands, it is expected that ARS is a powerful tool for signal analysis in the Internet of Things realizing the edge computing.
  • Synthesis of Memory Gain-Scheduled Controllers for Discrete-Time LPV Systems

    pp. 249-255

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    DOI:10.9746/jcmsi.13.249

    This paper considers synthesis of discrete-time gain-scheduled controllers for linear parameter varying systems based on linear matrix inequalities (LMIs). Unlike most of the previous results, gain-scheduled controllers that depend on memory of the scheduling parameters are investigated in this paper. Through the method of change-of-variables, parameter-dependent LMIs are obtained for synthesis of gain-scheduled controllers from extended LMIs for H and H2 performances. Numerical examples are provided to illustrate the proposed synthesis methods.

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