arXiv subjects

Satoshi Tsuruhara

Publications and source records attributed to Satoshi Tsuruhara.

6 recordsLinked to original sources

arXiv2026

Fast Convergence and Robustness for Two-Layered Forgetting Recursive Least Square under Finite Excitation

Under nonpersistent excitation (non-PE) conditions, conventional methods such as exponential forgetting (EF) or directional forgetting (DF) recursive least squares (RLS) that rely on direct regressor vectors exhibit inherent limitations in terms of stability guarantees for parameter errors, robustness to system changes, and convergence rates. To address these limitations, this study introduces a novel two-layer forgetting RLS (TLF-RLS) identification method based on an augmented regressor matrix constructed using DF, which ensures global exponential stability and enhances robustness under non-PE condition. However, the convergence rate of the parameter is strongly dependent on the forgetting factor because of the introduction of EF in the outer layer, which causes an estimation windup under non-PE condition. To address this issue, a novel reconfiguration-based EF (ReEF) algorithm is proposed, which is achieved through variable- and matrix-based forgetting related to the magnitude of the eigenvalues of the current covariance matrix. Theoretical analysis indicates that TLF-RLS with ReEF algorithm guarantees uniform ultimate boundedness of the condition number under mild assumptions. Consequently, the proposed method resolves the trade-off between fast parameter convergence and robustness in both transient and steady-state responses under changes in system characteristics. Numerical simulations of three aforementioned cases demonstrate the effectiveness of the proposed method.

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arXiv2025

Discrete-time Two-Layered Forgetting RLS Identification under Finite Excitation

In recent years, adaptive identification methods that can achieve the true value convergence of parameters without requiring persistent excitation (PE) have been widely studied, and concurrent learning has been intensively studied. However, the parameter convergence rate is limited for the gradient-based method owing to small parameter update gain, and even the introduction of forgetting factors does not work sufficiently. To address this problem, this study proposes a novel discrete-time recursive least squares method under finite excitation (FE) conditions using two forgetting factors (inner and outer) and an augmented regressor matrix comprising a sum of regressor vectors. The proposed method ensures the PE condition of the augmented regressor matrix under FE conditions of the regressor vector and allows the properly design of the forgetting factor without estimator windup and/or destabilization of the system. Numerical simulations demonstrate its effectiveness by comparing it with several conventional methods.

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arXiv2025

Discrete-time Indirect Adaptive Control for Systems with Disturbances via Directional Forgetting: Concurrent Learning Approach

Recently, adaptive control systems with relaxed persistent excitation (PE) conditions have been proposed to guarantee true parameter convergence and improve the transient response. However, in some cases, sufficient control performance and parameter convergence cannot be easily achieved, with stability demonstrated only under ideal conditions, such as the absence of disturbances and matching conditions required. In this study, we propose a novel adaptive control method for discrete-time systems with disturbances, which is not under an ideal case, that combines directional forgetting and concurrent learning. The proposed method does not require the PE condition, information on disturbances, unknown parameters, or matching conditions, and it guarantees uniformly ultimately bounded (UUB). It was also theoretically demonstrated that the ultimate bound can be designed based on the forgetting factor, which is a design parameter. In addition, the upper bound decreases with time step, which is independent of the system order and/or target trajectory due to forgetting factor. This also implies stronger stability than a normal UUB. Numerical simulation results illustrate the effectiveness of the proposed method.

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arXiv2024

Hierarchical-type Model Predictive Control and Experimental Evaluation for a Water-Hydraulic Artificial Muscle with Direct Data-Driven Adaptive Model Matching

High-precision displacement control for water-hydraulic artificial muscles is a challenging issue due to its strong hysteresis characteristics that is hard to be modelled precisely, and many control methods have been proposed. Recently, data-driven control methods have attracted much attention because they do not explicitly use mathematical models, making design much easier. In our previous work, we proposed fictitious reference iterative tuning (FRIT)-based model predictive control (FMPC), which combines data-driven and model-based methods for the muscle and showed its effectiveness because it can consider input constraints as well. However, the problem in which control performance strongly depends on prior input-output data remains still unsolved. Adaptive FRIT based on directional forgetting has also been proposed; however, it is difficult to achieve the desired transient performance because it cannot consider input constraints and there are no design parameters that directly determine the control performance, such as MPC. In this study, we propose a novel data-driven adaptive model matching-based controller that combines these methods. Experimental results show that the proposed method could significantly improve the control performance and achieve high robustness against inappropriate initial experimental data , while considering the input constraints in the design phase.

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arXiv2024

Optimized Pseudo-Linearization-Based Model Predictive Controller Design: Direct Data-Driven Approach

To reduce the typical time-consuming routines of plant modeling for model-based controller designs, the fictitious reference iterative tuning (FRIT) has been proposed and has proven to be effective in many applications. However, it is generally difficult to select a reference model properly without information on the plant, which significantly affects the control performance and sometimes leads to considerable performance degradation. To address this problem, we propose a pseudo-linearization (PL) method using FRIT and design a new controller for nonlinear systems that combines data-driven and model-based control. This design considers the input constraints using model predictive control. The effectiveness of the proposed method was evaluated according to several practical references using numerical simulations for nonlinear classes and experiments involving artificial muscles with hysteresis characteristics.

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arXiv2024

Adaptive FRIT-based Recursive Robust Controller Design Using Forgetting Factors

Adaptive FRIT (A-FRIT) with exponential forgetting (EF) has been proposed for time-varying systems to improve the data dependence of FRIT, which is a direct data-driven tuning method. However, the EF-based method is not a reliable controller because it can cause significant degradation of the control performance and instability unless the persistent excitation (PE) condition is satisfied. To solve this problem, we propose a new A-FRIT method based on directional forgetting (DF) and exponential resetting that can forget old data without instability regardless of the PE condition. To confirm the effectiveness of the proposed method, we applied it to artificial muscle control with strong asymmetric hysteresis characteristics and evaluated its robust performance against load changes during the experiment. The experimental results show that the proposed method based on DF achieves high control performance and is robust against changes in the characteristics and/or target trajectory. The proposed method is also practical because it does not require system identification, model structure, or prior experimentation.

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