6 papers
Fast Convergence and Robustness for Two-Layered Forgetting Recursive Least Square under Finite Excitation
Satoshi Tsuruhara, Kazuhisa Ito
Under nonpersistent excitation (non-PE) conditions, conventional methods such as exponential forgetting (EF) or directional forgetting (DF) recursive least squares (RLS) that rely…
Discrete-time Two-Layered Forgetting RLS Identification under Finite Excitation
Satoshi Tsuruhara, Kazuhisa Ito
In recent years, adaptive identification methods that can achieve the true value convergence of parameters without requiring persistent excitation (PE) have been widely studied, an…
Discrete-time Indirect Adaptive Control for Systems with Disturbances via Directional Forgetting: Concurrent Learning Approach
Satoshi Tsuruhara, Kazuhisa Ito
Recently, adaptive control systems with relaxed persistent excitation (PE) conditions have been proposed to guarantee true parameter convergence and improve the transient response.…
Hierarchical-type Model Predictive Control and Experimental Evaluation for a Water-Hydraulic Artificial Muscle with Direct Data-Driven Adaptive Model Matching
Satoshi Tsuruhara, Kazuhisa Ito
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…
Optimized Pseudo-Linearization-Based Model Predictive Controller Design: Direct Data-Driven Approach
Mikiya Sekine, Satoshi Tsuruhara, Kazuhisa Ito
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 prove…
Adaptive FRIT-based Recursive Robust Controller Design Using Forgetting Factors
Satoshi Tsuruhara, Kazuhisa Ito
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 meth…