activity
20232026
most citedSimple inverse kinematics computation considering joint motion efficiency

13 citations · 25 across the 8 of their papers we have counts for

collaborators

9 papers

eess.SY2026

Generalized bilinear Koopman realization from input-output data for multi-step prediction with metaheuristic optimization of lifting function and its application to real-world industrial system

Shuichi Yahagi, Ansei Yonezawa, Heisei Yonezawa +2

This paper introduces an input-output bilinear Koopman realization with an optimization algorithm of lifting functions. For nonlinear systems with inputs, Koopman-based modeling is…

cs.LG2026

Continual Uncertainty Learning for Robust Control of Nonlinear Systems with Multiple Heterogeneous Uncertainties

Heisei Yonezawa, Ansei Yonezawa, Itsuro Kajiwara

Robust control of mechanical systems with multiple uncertainties remains a fundamental challenge, particularly when nonlinear dynamics and operating-condition variations are intric…

eess.SY20256 cited

Fractional-order controller tuning via minimization of integral of time-weighted absolute error without multiple closed-loop tests

Ansei Yonezawa, Heisei Yonezawa, Shuichi Yahagi +2

This study presents a non-iterative tuning technique for a linear fractional-order (FO) controller, based on the integral of the time-weighted absolute error (ITAE) criterion. Mini…

cs.LG20252 cited

Sparse identification of nonlinear dynamics with library optimization mechanism: Recursive long-term prediction perspective

Ansei Yonezawa, Heisei Yonezawa, Shuichi Yahagi +4

The sparse identification of nonlinear dynamics (SINDy) approach can discover the governing equations of dynamical systems based on measurement data, where the dynamical model is i…

eess.SY20252 cited

Model-based controller assisted domain randomization for transient vibration suppression of nonlinear powertrain system with parametric uncertainty

Heisei Yonezawa, Ansei Yonezawa, Itsuro Kajiwara

Complex mechanical systems such as vehicle powertrains are inherently subject to multiple nonlinearities and uncertainties arising from parametric variations. Modeling errors are t…

eess.SY2025

Sparse Identification of Nonlinear Dynamics Enhanced by Ensemble Learning, Multi-Step Prediction Evaluation, Elite Strategy, and Classification Techniques for Applications to Industrial Systems

Shuichi Yahagi, Ansei Yonezawa, Hiroki Seto +2

This paper proposes a sparse identification of nonlinear dynamics (SINDy) with control and exogenous inputs for highly accurate and reliable prediction. Although SINDy is recognize…