制御工学ブログ / Control Engineering Blog

本記事では、宇都宮大学の平田光男先生が公開されている制御工学の教育用ツール ControlCanvas を紹介します。

本記事は、熊本大学の学部講義「制御工学I」（伝達関数に基づく制御、いわゆる古典制御）の各回に対応する、ブラウザで動く体験型のインタラクティブ教材と、当ブログの解説記事、そして制御工学チャンネルの解説動画をまとめたリンク集です。

2026年9月1日付で、熊本大学大学院先端科学研究部の教授に昇任しました。

This article explains how to design an observer-based feedback controller for multi-rate systems where sensors and actuators operate at different sampling periods.

制御工学教育に利用可能なインタラクティブページを作成しました。

SCI 2026 が名古屋で開催され参加しています。

This article explains a new system identification method that turns measurement noise — usually treated as a nuisance to be averaged out — into a structured uncertainty description suitable for robust control design.

A multirate Kalman filter estimates the state of a system whose sensors sample at different rates — GPS at 1 Hz fused with wheel speed sensors or an IMU at 10–100 Hz, for example.

This blog covers control engineering — from fundamental tutorials to research-level topics — with MATLAB code examples and video lectures.

This article provides a comprehensive explanation of kernel-based regularized system identification — a modern nonparametric approach that estimates the impulse response of linear systems by combining regularization theory with machine learning concepts.

This article provides a comprehensive overview of model-based compensation methods in control engineering — control structures that place a plant model inside the feedback loop and use the difference between the model output and the actual plant output as…

This article is a comprehensive guide to state feedback control and state-space design in control engineering.

This article explains how the conditional feedback structure in two-degree-of-freedom (2-DOF) control is the structural origin of the Model Error Compensator (MEC).

This article provides a detailed comparison between Internal Model Control (IMC) and the Model Error Compensator (MEC), two control methods that share the same core principle — using the difference between a plant output and a model output as a feedback s…

This article surveys research on the Model Error Compensator (MEC) conducted by research groups other than the original proposers.

This article explains how to apply the Model Error Compensator (MEC) to nonlinear systems through a robust feedback linearization approach.

This article explains how to apply the Model Error Compensator (MEC) to non-minimum phase systems using a parallel feedforward compensator approach.

This article explains how to add robustness to PID control systems using the Model Error Compensator (MEC).

This article provides a comprehensive explanation of classical parametric system identification — the approach of fitting polynomial input-output models to measured data using the prediction error method (PEM).

Subspace identification is a family of algorithms that estimate a state-space model directly from measured input-output data using linear algebra — block Hankel matrices, projections, and a singular value decomposition — rather than iterative optimization.

This article is a comprehensive guide to system identification — the process of building mathematical models of dynamical systems from measured input-output data.

This article provides a detailed comparison between the Model Error Compensator (MEC) and the Disturbance Observer (DOB), two approaches for enhancing the robustness of control systems against model uncertainty and external disturbances.

This article provides a comprehensive explanation of the filter (H-infinity filter) for robust state estimation in control systems.

This article provides a comprehensive explanation of the Kalman filter for state estimation in control systems.

This article is a comprehensive guide to state observers and state estimation in control systems.

This article provides a detailed explanation of robust invariant set estimation for discrete-time switched linear systems with peak-bounded disturbances.

This article provides a detailed explanation of observer-based feedback controller design for systems where both sensors and actuators operate at different sampling rates.

This article provides a detailed explanation of system identification for control systems with multiple sensors operating at different sampling rates, using cyclic reformulation and subspace identification methods.

This article provides a detailed explanation of system identification for linear periodically time-varying (LPTV) systems using cyclic reformulation and subspace identification.

This article provides a detailed explanation of tracking performance limitations for one-degree-of-freedom (1DOF) control systems, extending the achievable-output-set approach to systems where unstable poles also affect the fundamental limit.