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An adaptive controller design for cases with disturbances is critical in practical applications for preventing unexpected control performance degradation and instability. Recently, adaptive control systems with relaxed persistent excitation (PE) conditions have been proposed to solve this problem; however, most discussions have focused on continuous-time systems. In this study, we propose a novel adaptive control method for discrete-time systems with disturbances 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 exponential uniform ultimate unbounded (UUB). It was also theoretically demonstrated that the upper bound of the UUB can be designed based on the forgetting factor, which is a design parameter. Numerical simulation results illustrate the effectiveness of the proposed method., Comment: 6 pages, 1 figure

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., Comment: 14 pages, 17 figures

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., Comment: This work has been accepted to The 32nd Mediterranean Conference on Control and Automation (MED2024)

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., Comment: 11 pages, 16 figures, 4 tables, To be submitted to IET Control Theory & Applications

Since the 1985 discovery of the phase transition at $T_{\rm HO}=17.5$ K in the heavy-fermion metal URu$_2$Si$_2$, neither symmetry change in the crystal structure nor magnetic ordering have been observed, which makes this "hidden order" enigmatic. Some high-field experiments have suggested electronic nematicity which breaks fourfold rotational symmetry, but direct evidence has been lacking for its ground state at zero magnetic field. Here we report on the observation of lattice symmetry breaking from the fourfold tetragonal to twofold orthorhombic structure by high-resolution synchrotron X-ray diffraction measurements at zero field, which pins down the space symmetry of the order. Small orthorhombic symmetry-breaking distortion sets in at $T_{\rm HO}$ with a jump, uncovering the weakly first-order nature of the hidden-order transition. This distortion is observed only in ultrapure sample, implying a highly unusual coupling nature between the electronic nematicity and underlying lattice., Comment: 17 pages, 3 figures. Submitted version. Revisions have been made through the review process. See the published version in Nature Communications

The observation of cyclotron resonance in ultra-clean crystals of URu2Si2 [S. Tonegawa et al., PRL 109, 036401 (2012)] provides another route besides quantum oscillations to the determination of the bulk electronic structure in the hidden order phase. We report detailed analyses of the resonance lines, which fully resolve the cyclotron mass structure of the main Fermi surface sheets. A particular focus is given to the anomalous splitting of the sharpest resonance line near the [110] direction under in-plane magnetic-field rotation, which implies peculiar electronic structure in the hidden order phase. The results under the field rotation from [110] toward [001] direction reveal that the splitting is a robust feature against field tilting from the basal plane. This is in sharp contrast to the reported frequency branch alpha in the quantum oscillation experiments showing a three-fold splitting that disappears by a small field tilt, which can be explained by the magnetic breakdown between the large hole sphere and small electron pockets. Our analysis of the cyclotron resonance profiles reveals that the heavier branch of the split line has a larger scattering rate, providing evidence for the existence of hot-spot regions along the [110] direction. These results are consistent with the broken fourfold rotational symmetry in the hidden-order phase, which can modify the interband scattering in an asymmetric manner. We also extend our measurements down to 0.7 K, which results in the observation of cyclotron resonance in the superconducting state, where novel effects of vortex dynamics may enter. We find that the cyclotron mass undergoes no change in the superconducting state. In contrast, the quasiparticle scattering rate shows a rapid decrease below the vortex-lattice melting transition temperature, which supports the formation of quasiparticle Bloch state in the vortex lattice phase., Comment: 15 pages, 12 figures

In a superconductor, the ratio of the carrier density, $n$, to their effective mass, $m^*$, is a fundamental property directly reflecting the length scale of the superfluid flow, the London penetration depth, $\lambda_L$. In two dimensional systems, this ratio $n/m^*$ ($\sim 1/\lambda_L^2$) determines the effective Fermi temperature, $T_F$. We report a sharp peak in the $x$-dependence of $\lambda_L$ at zero temperature in clean samples of BaFe$_2$(As$_{1-x}$P$_x$)$_2$ at the optimum composition $x = 0.30$, where the superconducting transition temperature $T_c$ reaches a maximum of 30\,K. This structure may arise from quantum fluctuations associated with a quantum critical point (QCP). The ratio of $T_c/T_F$ at $x = 0.30$ is enhanced, implying a possible crossover towards the Bose-Einstein condensate limit driven by quantum criticality., Comment: Main text (5 pages, 4 figures) + Supplementary Materials (5 pages, 5 figures). Published on June 22, 2012