This paper focuses on the state-of-charge (SOC) estimation of the Lithium-ion battery in electric vehicles based on an H _∞ -based nonlinear observer. First, the second-order RC equivalent circuit model is introduced by utilizing the physical behavior of the battery. Then, the parameters in the second-order RC model are identified as polynomial functions with respect to SOC. Meanwhile, the battery model with constant parameters is also introduced for comparison. Due to that the battery model is undetectable, an one-sided Lipschitz condition is proposed to ensure that the nonlinear function in output equation can play a positive role in the observer design. After that, a nonlinear observer design criterion is presented based on the H _∞ method, which is formulated as linear matrix inequalities. Compared with existing nonlinear observer-based SOC estimation methods, the proposed observer design criterion does not depend on any estimates of the unknown variables. Consequently, the convergence of the proposed nonlinear observer is guaranteed for various operating conditions. Finally, one static and three dynamic operation conditions are given to show the efficiency of the proposed nonlinear observer by comparing with the classic extended Kalman filter and the nonlinear observer for constant parameters.

This work is focused on the state of charge (SOC) estimation of a lithium-ion battery based on a nonlinear observer. First, the second-order resistor-capacitor (RC) model of the battery pack is introduced by utilizing the physical behavior of the battery. Then, for the nonlinear function of the RC model, a one-sided Lipschitz condition is proposed to ensure that the nonlinear function can play a positive role in the observer design. After that, a nonlinear observer design criterion is presented based on the $H_{\infty }$ method, which is formulated as linear matrix inequalities (LMIs). Compared with existing nonlinear observer-based SOC estimation methods, the proposed observer design criterion does not depend on any estimates of the unknown variables. Consequently, the convergence of the proposed nonlinear observer is guaranteed for any operating conditions. Finally, both the static and dynamic experimental cases are given to show the efficiency of the proposed nonlinear observer by comparing with the classic extended Kalman filter (EKF).

The feedforward controller plays an important role in the achievement of high servo performance of wafer scanning. In this paper, a novel data-driven feedforward tuning method is developed in the presence of noise. Three distinguished features make it different from the existing methods: 1) high extrapolation capability to tasks; 2) low requirement on the system model; and especially 3) high noise tolerant capability. These superiorities are achieved by a high-order iterative feedforward tuning algorithm based on instrumental variables. It utilizes error data from all past iterations via an integrator in the learning law, yet without the need of the plant model or the sensitivity function. Furthermore, ${H}_{2}$ optimization with specified convergence speed constraint is proposed to design the learning gain. Connections and differences between the proposed algorithm and the existing ones are discussed finally. Experimental results validate the proposed method and confirm its effectiveness and superiority.

This paper focuses on the low-cost configuration of the lateral active suspension system for the highspeed train. First, the full-scale railway vehicle dynamics with 17-degree-of-freedom are introduced, where the actuator saturation, suspension deflection, and random and periodic track irregularities are considered. Then, based on the internal model principle, some classic active vibration control methods, e.g., the PID, positive position feedback, repetitive, and resonant controls, are unified as an internal model (IM)-based form and an IM-based resonant control is further presented. After that, to suppress various vibrations, three principles are presented to set the damping ratio and natural frequency of the IM-based resonant controller according to the properties of the controlled plant and/or the external disturbances. In consequence, an active suspension with the IM-based resonant controller is designed to improve the ride quality of the high-speed train, where the H _∞ method is used to calculate the unique gain. Moreover, the configuration of the active suspension is low cost and consists of two independent control systems, where one control system only has a pair of actuators and an accelerometer. Finally, the random and periodic track irregularities are employed to show the efficiency of the proposed resonant-control-based active suspension by comparing with the linear quadratic Gaussian (LQG) and H _∞ controllers.

An accurate state of charge (SOC) estimation is the basis of the Battery Management System (BMS). In this paper, a new estimation method which considers fractional calculus is proposed to estimate the lithium battery state of charge. Firstly, a modified second-order RC model based on fractional calculus theory is developed to model the lithium battery characteristics. After that, a pulse characterization test is implemented to obtain the battery terminal voltage and current, in which the parameter identification is completed based on least square method. Furthermore, the proposed method based on Fractional Unscented Kalman Filter (FUKF) algorithm is applied to estimate the battery state of charge value in both static and dynamic battery discharging experiment. The experimental results have demonstrated that the proposed method shows high accuracy and efficiency for state of charge estimation and the fractional calculus contributes to the battery state of charge estimation.

This work is focused on the active vibration control of piezoelectric cantilever beam, where an adaptive feedforward controller (AFC) is utilized to reject the vibration with unknown multiple frequencies. First, the experiment setup and its mathematical model are introduced. Due to that the channel between the disturbance and the vibration output is unknown in practice, a concept of equivalent input disturbance (EID) is employed to put an equivalent disturbance into the input channel. In this situation, the vibration control can be achieved by setting the control input be the identified EID. Then, for the EID with known multiple frequencies, the AFC is introduced to perfectly reject the vibration but is sensitive to the frequencies. In order to accurately identify the unknown frequencies of EID in presence of the random disturbances and un-modeled nonlinear dynamics, the time–frequency-analysis (TFA) method is employed to precisely identify the unknown frequencies. Consequently, a TFA-based AFC algorithm is proposed to the active vibration control with unknown frequencies. Finally, four cases are given to illustrate the efficiency of the proposed TFA-based AFC algorithm by experiment.