Search

[Display omitted] • A model-free vibration control strategy is examined. • New parameter tuning technique is constructed for the model-free controller. • The proposed tuning method is more efficient than a previous one. • The effectiveness of the present method is verified by experiments. In active vibration control, controller tuning is necessary to obtain a sufficient damping performance. This study presents an efficient tuning technique for model-free vibration controller based on a virtual controlled object (VCO). A model-free vibration control system is constructed by using an actuator model and the VCO. A reference controlled object (RCO), which is a designer-defined single-degree-of-freedom (SDOF) structure, is used to tune the VCO-controller offline. A novel parameter tuning technique based on the RCO and the simultaneous perturbation stochastic approximation (SPSA) is proposed considering the difference in scale of the tuning parameters. The proposed tuning scheme automatically determines the tuning parameter of the controller without manual trial-and-errors such as experiments with actual plants and is much faster than previous methods. Therefore, the proposed method significantly enhances the practicability of the VCO-based model-free vibration suppression, bridging the gap between the basic study of the VCO-method and its implementation. The effectiveness of the proposed method is verified by applying it to the VCO-based H 2 controller. [ABSTRACT FROM AUTHOR]

• Active vibration control of a drivetrain is investigated. • The actuator has various updating timings of the control input. • A sampled-data controller is used as a basic damping controller. • The intuitive fuzzy-reasoning-based compensation is proposed in this study. • The robustness of the proposed approach is validated by various simulation cases. For active vibration control of powertrain oscillations, the purpose of this research is to present a simple fuzzy-reasoning-based compensation strategy for a time-varying control cycle limitation of an actuator. A simplified drivetrain dynamics model is shown, and the simulation configuration with the control cycle limitation is constructed. A model predictive compensation using a sampled-data controller is employed to tackle the maximal phase delay of the control input due to the time-varying control cycle. The control input update timing, which is perturbed from that of the fixed periodical controller, is expressed as fuzzy sets such as "Approximately past step" and "Approximately future step". Based on these fuzzy sets, the Tsukamoto-type fuzzy reasoning with only four intuitive rules can flexibly infer unknown control inputs updated at various timings. Simulation verifications demonstrate that the 2-norm of the vibration performance by the proposed method is reduced by more than 27.8% compared to other previous controllers. Moreover, it is revealed that the better transient performance can be maintained even with 10% plant parameter variations, indicating the high robustness of the proposed damping controller. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • A model-free vibration control strategy is considered. • An online self-tuning scheme is combined with the model-free controller. • The proposed controller is free from controlled object models. • The performance of the proposed method is demonstrated by simulations. This study presents a simple active vibration controller with a self-tuning mechanism free from a mathematical model of an actual controlled object. First, a virtual controlled object (VCO), which is defined as a single-degree-of-freedom (SDOF) system, is inserted between an actuator model and an actual controlled object. Traditional model-based control theories are applied to a two-degree-of-freedom (2DOF) system composed of the actuator and the VCO instead of a model of the actual controlled object to realize a model-free vibration controller. Then a self-tuning method based on the simultaneous perturbation stochastic approximation (SPSA) is introduced to the VCO-based model-free controller to obtain sufficient damping performances for various controlled objects. The model-free controller design is easily achieved because the traditional model-based control theory can be applied to the 2DOF system composed of the actuator and the VCO. Moreover, the proposed self-tuning mechanism provides sufficient vibration suppression effects for various controlled objects without manual controller tunings. Simulation studies compare the damping performance of the VCO-based model-free adaptive control scheme with that of the conventional approach. The simulations employ five controlled objects with different structures and characteristics, including time-varying properties. The proposed control scheme provides better damping effects than the conventional method for all controlled objects. [ABSTRACT FROM AUTHOR]

[Display omitted] • A model-free vibration control strategy is considered. • A parameter tuning procedure is constructed for the model-free controller. • The proposed parameter tuning method is free from manual trial-and-error works. • The effectiveness of the present method is demonstrated by experiments. A parameter tuning technique without manual trial-and-error procedures is proposed for a controller in a model-free vibration control system based on a virtual controlled object (VCO), which is defined as a single-degree-of-freedom (SDOF) system. The model-free control system is constructed by inserting a VCO between the actuator and the actual controlled object. A reference controlled object (RCO), which is also expressed as an SDOF system, is defined for the configured model-free control system. Then the loss function, which is calculated using the RCO vibration control simulation results, is used to evaluate the vibration suppression performance. The simultaneous perturbation stochastic approximation (SPSA) adjusts the controller tuning parameters to minimize the loss function. The SPSA- and RCO-based tuning procedures automatically tune the model-free controller without manual trial-and-error procedures. Simulations and experiments demonstrate that a model-free linear quadratic regulator designed by the proposed approach provides sufficient vibration reduction. [ABSTRACT FROM AUTHOR]

Dual-ball-screw feed drive systems (DBSFDSs) are designed for most high-end manufacturing equipment. However, the mismatch between the dynamic characteristic parameters (e.g., stiffness and inertia) and the P-PI cascade control method reduces the accuracy of the DBSFDSs owing to the structural characteristic changes in the motion. Moreover, the parameters of the P-PI cascade controller of the DBSFDSs are always the same even though the two axes have different dynamic characteristics, and it is difficult to tune two-axis parameters simultaneously. A new application of the combination of the grey wolf optimization (GWO) algorithm and the P-PI cascade controller is presented to solve these problems and enhance the motion performance of DBSFDSs. The novelty is that the flexible coupling model and dynamic stiffness obtained from the motor current can better represent the two-axis coupling dynamic characteristics, and the GWO algorithm is used to adjust the P-PI controller parameters to address variations in the positions of the moving parts and reflect characteristic differences between the two axes. Comparison of simulation and experimental results validated the superiority of the proposed controller over existing ones in practical applications, showing a decrease in the tracking error of the tool center and non-synchronization error of over 34% and 39%, respectively. [ABSTRACT FROM AUTHOR]