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. 6

  • Bandwidth Maximization of Disturbance Observer Based on Experimental Frequency Response Data

    pp. 257-264

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    DOI:10.9746/jcmsi.13.257

    A disturbance observer (DOB) has been widely employed in industrial field due to its simplicity and effectiveness in disturbance rejection. This paper focuses on systematic bandwidth-maximized DOB design by frequency response data-based convex optimization. The transformation process from original non-convex optimization to convex optimization has been formulated. Simulation results have verified the feasibility and generality of the proposal and shown that the designed DOB is able to achieve good disturbance rejection performance.
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  • Non-Cooperative Optimization Algorithm of Charging Scheduling for Electric Vehicle

    pp. 265-273

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    DOI:10.9746/jcmsi.13.265

    In this paper, we aim to propose a charging scheduling algorithm for electric vehicles on highways. While the number of electric vehicles has been increasing recently, charging stations are not becoming widespread compared to gas stations. The distance that an electric vehicle can run on one charge is only around 120km to 400km. Therefore, it is necessary to plan to recharge in advance when driving long distances. Problems related to planning algorithms are called charging scheduling problems of electric vehicles. In this paper, we assume that there is no difference in the power of the electric vehicle and the charging station, and consider the situation where each acts to maximize its profit. First, since the electric vehicle can select the charging station freely, it motivates us to solve the optimal allocation problem of the electric vehicle to the charging station using matching theory. Then, non-cooperative game theory is utilized to obtain the energy demand and energy price for the electric vehicles and charging stations, respectively. In addition, the convergence condition of the non-cooperative game is theoretically derived. Finally, the effectiveness of the proposed non-cooperative charging scheduling algorithm is confirmed by numerical simulation.
  • Stochastic Consensus Algorithms over General Noisy Networks

    pp. 274-281

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    DOI:10.9746/jcmsi.13.274

    Stochastic consensus algorithms are considered for multi-agent systems over noisy unbalanced directed networks. The graph which represents a communication network of the system is assumed to contain a directed spanning tree, that is, a given digraph is weakly connected. Then two types of stochastic consensus are investigated, where one is for the agent states themselves and the other is for the time averages of the agent states. The convergence of the algorithms is investigated, which gives a stopping rule, i.e., an explicit relation between the number of iterations and the closeness of the agreement.
  • Recursive Elimination Method in Moving Horizon Estimation for a Class of Nonlinear Systems and Non-Gaussian Noise

    pp. 282-290

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    DOI:10.9746/jcmsi.13.282

    This paper proposes a recursive elimination method for optimal filtering problems of a class of discrete-time nonlinear systems with non-Gaussian noise. By this method, most of the computations to solve an optimal filtering problem can be carried out off-line by using symbolic computation based on the results from algebraic geometry. This property is suitable for moving horizon estimation, where a certain optimal filtering problem must be solved for different measurement sequences in each sampling interval. A numerical example is provided to compare the proposed method with other state estimation methods including the particle filter, and the efficiency of the proposed method is shown.
  • A Consideration on Approximation Methods of Model Matching Error for Data-Driven Controller Tuning

    pp. 291-298

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    DOI:10.9746/jcmsi.13.291

    This paper proposes two kinds of data-driven controller tuning. The proposed methods are derived from the approximated model matching errors expressed by the filtered ideal model matching error. The main contribution of the paper is to find out specific filters that characterize the proposed data-driven methods. Similar filters are also presented in the existing virtual reference feedback tuning and fictitious reference iterative tuning as well. The comparison among the filters for the approximations clarifies the relation among them as well as the novelty of the proposed approach. The paper shows two numerical examples: one is a flexible transmission system and the other is a plant with an unstable zero. The numerical examples show the superiority of the proposed method to existing methods.
  • Robustification of Continuous-Time ADMM against Communication Delays under Non-Strict Convexity: A Passivity-Based Approach

    pp. 299-305

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    DOI:10.9746/jcmsi.13.299

    In this paper, we address a class of distributed optimization problems with non-strictly convex cost functions in the presence of communication delays between an agent and a coordinator. To this end, we focus on a continuous-time optimization algorithm that mirrors the alternating direction method of multipliers. We first redesign the algorithm so that the dynamics ensures smoothness and a sub-block for primal optimization includes stable zeros. It is then revealed that the algorithm is composed of feedback interconnection of passive systems. We next robustify the algorithm against communication delays by applying the so-called scattering transformation. The smoothness of the dynamics allows one to use the invariance principle for delay systems, and accordingly, the state trajectories are shown to converge to an optimal solution even without the strict convexity assumption. Finally, the presented method is demonstrated via simulation of an environmental-monitoring problem.

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