研究内容 / Research:

　中央大学理工学部 電気電子情報通信工学科 空間知能化研究室(橋本秀紀研究室)にて, 安心/安全なロボットを実現する制約条件を考慮した制御系設計および空間知能化を中心に研究しています.

　I am conducting research on robust and intelligent constrained control systems to achieve safe and reliable flexible human-assist robotics and the development of intelligent spaces.

研究テーマ / Research theme:

モデルベース制御手法では，モデル化誤差やパラメータ誤差による制御性能の劣化が一般的な問題として挙げられます．この問題に対し，これらの誤差要因を外乱として扱い，その影響を推定・補償・除去することで，ロバスト性を向上させる研究を行っています． 特に，外乱オブザーバ（Disturbance Observer: DOB）およびモデル予測制御（Model Predictive Control: MPC）の2つの手法に焦点を当て、，モデルベース制御の効率的かつ統合的なロバスト設計手法の開発を進めています．

A common problem with model-based control methods is the degradation of control performance due to modeling and parameter errors. To enhance robustness, I treat these performance-degrading factors as disturbances, and develop estimation- and compensation-based approaches to suppress their effects. In particular, my research focuses on two core control techniques: the Disturbance Observer (DOB) and Model Predictive Control (MPC), aiming to establish an efficient and unified framework for robust model-based control that ensures both stability and performance.

[ref] Takashi Ohhira, Keinosuke Yokota, Shuichi Tatsumi, Toshiyuki Murakami, "A Robust Hybrid Position/Force Control Considering Motor Torque Saturation," IEEE Access, Vol. 9, pp. 34515-34528, doi: 10.1109/ACCESS.2021.3059889, 2021.

[ref] Takashi Ohhira, Akira Shimada, "Movement Control Based on Model Predictive Control with Disturbance Suppression using Kalman Filter including Disturbance Estimation," IEEJ Journal of Industry Applications, Vol.7, No.5, pp.387-395, 2018. 

外乱推定技術（Disturbance Estimation Techniques）においては，外乱推定速度とノイズ感度がトレードオフの関係にあることが知られています． 本研究では，このトレードオフを解消するために，カルマンフィルタ（Kalman Filter）をベースとし，適応ゲイン則（Adaptive Observer Gain Law）を導入した新しい外乱推定手法の開発を行っています．さらに，外乱と状態を同時に推定することで，高精度な状態推定を実現するロバスト状態推定手法（Robust State Observer）としての応用にも取り組んでいます．加えて，非線形カルマンフィルタ（Nonlinear Kalman Filter, e.g., EKF, UKF）を用いることで，非線形システムへの拡張的研究も進めています．

A general problem with disturbance observer techniques (e.g., Kalman Filter, Disturbance Observer, Model Error Compensator, Extended State Observer) is the trade-off between disturbance estimation speed and noise sensitivity, which depends on the designed observer bandwidth. To address this issue, we are developing a novel disturbance observer based on a Kalman Filter with an adaptive observer gain law to improve robustness against noise. Moreover, by simultaneously estimating both the system state and disturbance, we aim to construct a robust state observer that achieves highly accurate state estimation. This study is also extended to nonlinear systems using nonlinear Kalman filters (e.g.,　Extended Kalman filter, EKF; Unscented Kalman Filter, UKF) for broader applicability.

[ref] Takashi Ohhira, Akira Shimada, Toshiyuki Murakami, "Variable Forgetting Factor-based Adaptive Kalman Filter with Disturbance Estimation Considering Observation Noise Reduction," IEEE Access, Vol. 9, pp. 100747 - 100756, doi: 10.1109/ACCESS.2021.3097342, 2021.

[ref] Takashi Ohhira, Akihiro Kawamura, Akira Shimada, Toshiyuki Murakami, "An underwater quadrotor control with wave-disturbance compensation by a UKF," The 21th World Congress of the International Federation of Automatic Control, IFAC-PapersOnLine, Vol. 53, No.2, pp.9017-9022, July 2020.

近年，在宅や日常生活の中での非侵襲・非接触型の生体信号計測が注目されています．本研究では，Wi-Fi通信信号やカメラ映像を活用し，心拍数・脈波・血圧などの生体信号を推定することで，人が日常的に健康状態をモニタリングできるシステムの構築を目指しています．特に，Wi-Fi電波のチャネル状態情報（CSI）を用いた心拍変動や体動の抽出，およびカメラ映像（RGB・近赤外）によるリモートフォトプレチスモグラフィ（iPPG）信号解析を行い，非接触かつ高精度な生体センシングの実現を追求しています．

To enable daily and non-invasive health monitoring, our research focuses on contactless biological signal detection systems using Wi-Fi signals and camera sensors. We aim to estimate vital signs such as heart rate, pulse wave, and blood pressure through remote sensing techniques. Specifically, we utilize Wi-Fi Channel State Information (CSI) to extract heart rate variability and body motion, and camera-based near-infrared iPPG (imaging photoplethysmography) to perform robust and motion-compensated vital signal estimation during daily activities and sleep.　

[ref] Ryoto Fujita, Takashi Ohhira, Hideki Hashimoto, "Stable Electrocardiogram Measurement Using Capacitive-Coupled Electrodes," IEEE ICM2023, March 2023.

[ref] Kazuya Tsubota, Takashi Ohhira, Hideki Hashimoto, "Heart Rate Variability and Body-Movement Extractions Using Wi-Fi Channel State Information," Proceedings of 2023 IEEE/SICE International Symposium on System Integration (SII2023), ThP1M1.4, Janu. 2023.

[ref] Keisuke Terai, Takashi Ohhira, Hideki Hashimoto, "Near-Infrared iPPG Body Motion Compensation Using Head Euler Angles During Sleep," Proceedings of 2023 IEEE/SICE International Symposium on System Integration (SII2023), WeP2M2.4, Janu. 2023.

磁気式エンコーダは，光学式エンコーダに比べて角度検出精度の面で劣る一方，高い耐環境性・小型化・低コスト化を実現できるという利点があります．本研究では，この精度面での課題を克服するための角度算出アルゴリズムの高精度化に取り組んでいます．特に，磁力信号に特徴付けを行った磁気式アブソリュートエンコーダを対象として，磁石の偏心誤差や組立誤差によって生じる角度推定誤差をモデル化し，Levenberg–Marquardt法などの数値最適化手法を用いた高精度角度推定アルゴリズムの開発を行っています・これにより、磁気式エンコーダの高精度化と実用性の両立を目指しています．

Magnetic encoders, while less accurate than optical encoders in angle detection, offer significant advantages in environmental robustness, compactness, and cost efficiency. To overcome the accuracy limitations, this study focuses on developing highly precise angle estimation algorithms. In particular, we target magnetic absolute encoders characterized by magnetic field signals, and model eccentricity and assembly errors that cause angular estimation inaccuracies. Using numerical optimization techniques such as the Levenberg–Marquardt algorithm, we aim to establish a high-precision and practically applicable magnetic encoder system that maintains accuracy under real-world conditions. 

[ref8] Akishi Takeyama, Shota Komatsuzaki, Takashi Ohhira, Hideki Hashimoto, "Levenberg-Marquardt method based Precise Angle Estimation for Eccentric Magnetic Absolute Encoders," IEEE International Conference on Mechatronics 2023, March 2023.

モータは，電流を適切に制御することで定格トルクを超える出力を瞬時的に発生させることが可能です．しかし，過大な電流によってコイル温度の上昇や熱損傷が発生するため，高トルク動作を安全に維持するには熱特性を考慮した制御設計が必要です．本研究では，設計する熱モデルに基づくモータ温度推定と熱制御を組み合わせることで，熱損傷を回避しながら瞬時的な高トルク化を実現する制御手法の開発を行っています．

By properly controlling the motor current, it is possible to generate torque exceeding the rated value instantaneously. However, excessive current causes thermal stress and potential coil damage, making thermal-aware control design essential for safe high-torque operation. This research focuses on motor control methods that achieve high torque while preventing thermal damage, through coil temperature estimation and thermal modeling techniques.

[ref] Momodayu Hattori, Subaru Murakami, Takashi Ohhira, Hideki Hashimoto, "A Coil Temperature Estimation for Disk Rotor Type Brushless DC Motors," IEEE ICM2023, March 2023.

[ref] Subaru Murakami, Momodayu Hattori, Takashi Ohhira, Hideki Hashimoto, "High-Torque Electric Motors with Coil Cooling Via Thermal Model and Peltier Element," Proceedings of 2023 IEEE/SICE International Symposium on System Integration (SII2023), WeP2M1.6, Janu. 2023.