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MEC Research by Other Groups

Eng: Control Engineering Eng: Control Theory

This article surveys research on the Model Error Compensator (MEC) conducted by research groups other than the original proposers. Since the MEC was first proposed by Okajima et al. in 2013, it has been adopted and extended by multiple research groups in Japan and internationally. These independent studies demonstrate the versatility and practical value of the MEC framework across diverse application domains including quadcopters, teleoperation, torsion torque control, data-driven tuning, and educational platforms. Related articles, related papers, and MATLAB links are placed at the bottom.

Author: Hiroshi Okajima, Associate Professor, Kumamoto University, Japan — 20 years of control engineering research

For the comprehensive guide on MEC, see: Model Error Compensator (MEC): Enhance the Robustness of Existing Control Systems

Why Other Groups' Research Matters

The adoption of MEC by independent research groups provides important evidence of its generality and practical utility. When researchers outside the original development team apply a method to their own problems, it validates the method's applicability beyond the original context. The following survey covers research groups that have explicitly used the MEC framework (with "Model Error Compensator" in their titles or as a core component) in their publications.

The research can be broadly categorized into three directions:

  1. Application to new domains — applying MEC to systems not originally considered (quadcopters, teleoperation, underwater robots, etc.)
  2. Data-driven and online tuning — combining MEC with data-driven control methods to automate the design of the error compensator
  3. Theoretical analysis and comparisons — analyzing the relationship between MEC and other compensation methods such as the Disturbance Observer

Application to New Domains

Quadcopter Control (Endo, Sekiguchi, Nonaka — 2017, 2019)

Endo, Aramaki, Sekiguchi, and Nonaka at Meiji University applied MEC to quadcopter altitude and attitude control in combination with the Fictitious Reference Iterative Tuning (FRIT) method.

They further developed an online tuning method for the MEC parameters:

This work is significant because it demonstrates that MEC can be combined with data-driven tuning approaches, enabling automatic adjustment of the error compensator without explicit plant identification.

Teleoperation with Time Delay (Hatori, Nagakura, Uchimura — 2021)

Hatori, Nagakura, and Uchimura applied MEC to teleoperation systems with variable and large time delays, combining it with Model Predictive Control (MPC):

This application is notable because time-delay systems are inherently challenging for both MEC and conventional control methods. The combination of MPC (for predictive compensation) and MEC (for model error suppression) provides a complementary approach.

Torsion Torque Control (Kawai, Nagao, Yokokura, Ohishi, Miyazaki — 2021)

Kawai et al. combined MEC with a Disturbance Observer for quick torsion torque control using a torsion torque sensor:

This work is interesting because it uses both MEC and DOB simultaneously, suggesting that the two approaches can be complementary rather than competing methods. For a detailed comparison of MEC and DOB as standalone methods, see MEC vs Disturbance Observer: A Structural Comparison.

Underwater Robot Control (Nishio, Hanazawa, Sagara, Ambar — 2025)

Nishio et al. applied MEC to resolved acceleration control of a 3-link dual-arm underwater robot:

    1. Nishio, Y. Hanazawa, S. Sagara and R. Ambar, Experiments on resolved acceleration control of a 3-link dual-arm underwater robot with model error compensator, Artificial Life and Robotics 2025, DOI: 10.1007/s10015-025-01032-2
    1. Osugi, R. Nishio, Y. Hanazawa, S. Sagara and R. Ambar, Force control experiment of a 3-link dual-arm underwater robot with model error compensator, The Thirtieth International Symposium on Artificial Life and Robotics 2025

Underwater robots operate in environments with significant hydrodynamic uncertainty, making MEC's ability to suppress model errors without requiring an inverse model particularly valuable.

Boost Converter Control (Satake, Yang, Hagiwara — 2025)

Satake, Yang, and Hagiwara applied MEC to nonlinear output voltage control of a boost converter:

  • 佐竹泰智, 楊熙, 萩原朋道, モデル誤差抑制補償器に基づくブーストコンバータの非線形出力電圧制御, 第69回システム制御情報学会研究発表講演会 (2025)

This extends MEC's application to power electronics, a domain where nonlinear dynamics and parameter variations (due to component aging and temperature) are common.

Data-Driven and Online Tuning

Data-Driven Tuning (Sano, Yamamoto — 2018)

Sano and Yamamoto proposed a data-driven tuning method for MEC:

    1. Sano and S. Yamamoto, A Data-Driven Tuning Method for Model Error Compensator, Proc. of SICE 2018, pp. 1199–2002 (2018)

This approach eliminates the need for an explicit plant model in the MEC design process, using measured input-output data to directly tune the error compensator parameters.

Database-Driven MEC in Smart MBD (Wakitani, Yamamoto — 2021, 2023)

Wakitani and Yamamoto developed a database-driven MEC within their Smart Model-Based Development (Smart MBD) framework:

The Smart MBD approach integrates model-based and data-driven design paradigms, where MEC serves as a bridge between the two.

Data-Driven Vehicle Control (Suzuki — 2022)

Suzuki applied data-driven design to MEC for autonomous vehicle velocity control:

Data-Driven Nonlinear Compensation (Yoshida, Ishikawa, Minami — 2023)

Yoshida, Ishikawa, and Minami developed a data-driven feedback modulator for nonlinear compensator design:

GMV-MEC for Machine Systems (Sugawara, Wakitani, Yamamoto et al. — 2024, 2025)

Sugawara, Wakitani, Yamamoto, and colleagues applied a Generalized Minimum Variance (GMV) compensator combined with MEC for hierarchical control of resin processing machinery:

Theoretical Analysis and Comparisons

Relationship between DOB and MEC (Kawada — 2024; Shikada, Sebe — 2023)

Two independent studies have analyzed the structural relationship between MEC and Disturbance Observer:

These studies provide theoretical confirmation that MEC and DOB can achieve equivalent performance under certain conditions, while differing in their structural requirements and design perspectives.

FRIT-Based PID Tuning with MEC (Kawada — 2024)

Kawada combined MEC with FRIT (Fictitious Reference Iterative Tuning) for PID parameter adjustment:

Kawada's work is particularly valuable for its educational perspective, demonstrating MEC on LEGO-based experimental platforms that are accessible to students.

Positioning Control with MEC (Matsui, Kawada — 2024)

Matsui and Kawada designed a positioning control system combined with MEC:

Fractional Order MEC (Haddi, Azzouzi, Laabissi — 2024)

Haddi, Azzouzi, and Laabissi extended MEC to fractional-order dynamical systems:

This is a significant theoretical extension because fractional-order systems require different mathematical treatment than integer-order systems, and the paper demonstrates that the MEC framework can be generalized to this broader class of dynamical systems.

Adaptive Control with MEC (Itamiya — 2024)

Itamiya investigated the role of MEC as a fixed compensation element in robust model-reference adaptive control:

  • 板宮敬悦, モデル誤差抑制補償要素を併用した適応制御系に関する研究, MSCS2024
  • 板宮敬悦, ロバストモデル規範形適応制御系における固定補償要素のモデル誤差抑制制御器としての役割, SCI 2024

State Predictive Control with MEC (Shimohigashi, Sawada — 2025)

Shimohigashi and Sawada applied MEC to robustify state predictive control:

  • 下東知隼, 澤田賢治, モデル誤差抑制補償器を用いた状態予測制御のロバスト化, 第69回システム制御情報学会研究発表講演会 (2025)

Summary of Research Directions

The following table summarizes the main directions of MEC research by other groups:

Direction Groups Key Feature
Quadcopter / UAV Endo, Sekiguchi, Nonaka (Meiji Univ.) FRIT + MEC, online tuning
Teleoperation Hatori, Uchimura MPC + MEC for time delay
Servo / Torque control Kawai, Yokokura, Ohishi DOB + MEC combined
Underwater robots Nishio, Sagara, Ambar Resolved acceleration control
Power electronics Satake, Yang, Hagiwara Boost converter nonlinear control
Data-driven tuning Sano, Yamamoto; Wakitani, Yamamoto Database-driven, Smart MBD
Vehicle control Suzuki Autonomous driving
DOB-MEC theory Kawada; Shikada, Sebe Equivalence analysis
Educational Kawada LEGO + Arduino experiments
Fractional order Haddi, Azzouzi, Laabissi Extension to fractional systems
Adaptive control Itamiya MEC in MRAC framework
Machine systems Sugawara, Wakitani, Yamamoto GMV + MEC hierarchical control

Model Error Compensator: Comprehensive Guide — For the full overview of MEC including basic structure and all design methods, see the MEC hub article.

Original MEC Paper — H. Okajima, H. Umei, N. Matsunaga and T. Asai, A Design Method of Compensator to Minimize Model Error, SICE JCMSI, Vol. 6, No. 4, pp. 267–275 (2013). See also the blog article.

MEC + PID Control — Many of the above studies combine MEC with PID control. For a dedicated overview, see MEC + PID Control: Adding Robustness to the Most Widely Used Controller.

MEC vs Disturbance Observer — The theoretical comparisons by Kawada and Shikada/Sebe are closely related to MEC vs DOB: A Structural Comparison.

MEC for Non-Minimum Phase Systems — Several groups have applied MEC to systems where the DOB is difficult to apply. See MEC for Non-Minimum Phase Systems.

MEC for Nonlinear Systems — The quadcopter, vehicle, and underwater robot applications all involve nonlinear dynamics. See MEC for Nonlinear Systems: Robust Feedback Linearization.

MEC Design with LMI — The foundational LMI-based design used by several groups is described in A Design Method of MEC for Systems with Polytopic-Type Uncertainty.

Blog Articles (blog.control-theory.com)

Research Web Pages (www.control-theory.com)

Video

Self-Introduction

Hiroshi Okajima — Associate Professor, Graduate School of Science and Technology, Kumamoto University. Member of SICE, ISCIE, and IEEE.

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