arXiv subjects

Shuichi Yahagi

Publications and source records attributed to Shuichi Yahagi.

6 recordsLinked to original sources

arXiv2026

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

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 effective because the Koopman operator enables a high-dimensional linear representation of nonlinear dynamics. However, traditional approaches face significant challenges in industrial applications. Measuring all system states is often impractical due to constraints on sensor installation. Moreover, the predictive performance of a Koopman model strongly depends on the choice of lifting functions, and their design typically requires substantial manual effort. In addition, although a linear time-invariant (LTI) Koopman model is the most commonly used model structure in the Koopman framework, such model exhibit limited predictive accuracy. To address these limitations, we propose an input-output bilinear Koopman modeling in which the design parameters of radial basis function (RBF)-based lifting functions are optimized using a global metaheuristic algorithm to improve long-term prediction performance. Consideration of the long-term prediction performance enhances the reliability of the resulting model. The proposed methodology is validated in simulations and experimental tests, with the airpath control system of a diesel engine as the plant to be modeled. This plant represents a challenging industrial application because it exhibits strong nonlinearities and coupled multi-input multi-output (MIMO) dynamics. These results demonstrate that the proposed input-output bilinear Koopman model significantly outperforms traditional linear Koopman models in predictive accuracy.

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arXiv2026

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

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 identified as the sparse linear combination of the given basis functions. A major challenge in SINDy is the design of a library, which is a set of candidate basis functions, as the appropriate library is not trivial for many dynamical systems. To overcome this difficulty, this study proposes SINDy with library optimization mechanism (SINDy-LOM), which is a combination of the sparse regression technique and the novel learning strategy of the library. In the proposed approach, the basis functions are parametrized. The SINDy-LOM approach involves a two-layer optimization architecture: the inner-layer, in which the data-driven model is extracted as the sparse linear combination of the candidate basis functions, and the outer-layer, in which the basis functions are optimized from the viewpoint of the recursive long-term (RLT) prediction accuracy; thus, the library design is reformulated as the optimization of the parametrized basis functions. The dynamical model obtained by SINDy-LOM has good interpretability and usability, as this approach yields a parsimonious closed-form model. The library optimization mechanism significantly reduces user burden. The RLT perspective improves the reliability of the resulting model compared with the traditional SINDy approach that can only ensure the one-step-ahead prediction accuracy. The effectiveness of the proposed approach is verified through numerical experiments.

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arXiv2025

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

This paper proposes a sparse identification of nonlinear dynamics (SINDy) with control and exogenous inputs for highly accurate and reliable prediction. Although SINDy is recognized as a remarkable approach for identifying nonlinear systems, several challenges remain. Its application to industrial systems remains limited, and multi-step predictions are not guaranteed due to overfitting and noisy data. This phenomenon is often caused by the increase in basis functions resulting from the extension of coordinates, such as time-delay embedding. To address these problems, this study proposes an emphasized SINDy framework by integrating ensemble-learning, multi-step prediction evaluations, elite strategy, and classification techniques (EMEC-SINDy), while preserving convex optimization. The proposed method employs library bagging and extracts elites with an R-squared greater than 90%. Then, clustering is performed on the surviving elites because physically motivated basis functions are not always available, and the elites obtained do not always have similar basis functions. After the classification, discrete model candidates are obtained by taking the mean of each classified elite. Finally, the best model is selected. Simulation results demonstrate that EMEC-SINDy significantly outperforms original SINDy approaches in multi-step prediction accuracy under noisy conditions, validating its applicability to the diesel engine airpath system, which is known as a complex and highly coupled nonlinear multi-input multi-output system.

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arXiv2025

Simple controller design to achieve iso-damping robustness: Non-iterative data-driven approach based on fractional-order reference model

This study proposes a simple controller design approach to achieve a class of robustness, the so-called iso-damping property. The proposed approach can be executed using only one-shot input/output data. An accurate mathematical model of a controlled plant is not required. The model-reference control problem is defined to achieve the desired closed-loop specifications, including the iso-damping, and the reference model is designed on the basis of fractional-order calculus. The optimization problem for the model-reference control is formulated using the one-shot input/output data while considering the bounded-input bounded-output (BIBO) stability from a bounded reference input to a bounded output. The iso-damping robust controller is obtained by solving the optimization problem. The representative advantages of the proposed approach over the conventional methods are the simplicity, practicality, and reliability from the viewpoint of the unnecessity of the plant model and explicit consideration of the BIBO stability from a bounded reference input to a bounded output. Numerical and experimental studies demonstrate the validity of the proposed approach.

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arXiv2025

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

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. Minimizing the ITAE is a traditional approach for tuning FO controllers. This technique reduces the over/undershoot and suppresses the steady-state error. In contrast to conventional approaches of ITAE-based controller tuning, the proposed approach does not require multiple closed-loop experiments or model-based simulations to evaluate the ITAE. The one-shot input/output data is collected from the controlled plant. A fictitious reference signal is defined on the basis of the collected input and output signal, which enables us to evaluate the closed-loop response provided by the arbitrary controller parameters. To avoid repeated experiments that are necessary in the conventional approach, we reformulate the ITAE minimization problem using the fictitious reference signal. The desired FO controller parameters minimizing the ITAE are obtained by solving the optimization problem that is based on the fictitious reference signal. The validity of the proposed approach is demonstrated by a numerical study. The avoidance of repeated experiments significantly reduces the development cost of linear FO controllers, thereby facilitating their practical application.

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arXiv2024

Practical one-shot data-driven design of fractional-order PID controller: Fictitious reference signal approach

This study proposes a one-shot data-driven tuning method for a fractional-order proportional-integral-derivative (FOPID) controller. The proposed method tunes the FOPID controller in the model-reference control formulation. A loss function is defined to evaluate the match between a given reference model and the closed-loop response while explicitly considering the closed-loop stability. A loss function value is based on the fictitious reference signal computed using the input/output data. Model matching is achieved via loss function minimization. The proposed method is simple and practical: it needs only one-shot input/output data of a plant (no plant model required), considers the bounded-input bounded-output stability of the closed-loop system from a bounded reference input to a bounded output, and automatically determines the appropriate parameter value via optimization. Numerical simulations show that the proposed approach facilitates good control performance, and destabilization can be avoided even if perfect model matching is unachievable.

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