In this paper, an extended disturbance observer-based integral sliding-mode control for a nonlinear system is proposed by the Takagi-Sugeno (T–S) fuzzy model approach. The proposed method focuses on two points: First, improving the design flexibility of integral fuzzy sliding-mode control (IFSMC) by transforming a conventional T–S fuzzy model structure into a novel form. Second, expanding a previous disturbance observer-based control (DOBC) concept by introducing a Fourier analysis to deal with the non-periodic form of disturbance. By using the modified form of the fuzzy model, a novel sliding surface combined with estimated disturbance through several fuzzy disturbance observers (FDOB) is suggested. Besides, a sufficient condition for guaranteeing asymptotic stability of the sliding dynamic with $H_\infty $ control performance is proposed by applying a proper Lyapunov function with the linear matrix inequality (LMI) concept. Furthermore, a reachability problem of the proposed sliding surface is also handled by introducing another Lyapunov function. Finally, the effectiveness of the proposed method is demonstrated by comparing previous studies, which focused on robust control techniques, based on the simulation results of an inverted-pendulum system.

Food Science of Animal Resources (Food Sci. Anim. Resour.) is an international, peer-reviewed bimonthly journal publishing original research and reviews on basic and applied aspects of the use of animal resources including the meat, egg, dairy, and edible insect/worm products. Food Sci. Anim. Resour. is published and distributed by the Korean Society for Food Science of Animal Resources.

Based on the intelligent digital redesign (IDR) technique, this paper proposes a novel method for designing a sampled-data fuzzy observer estimating a state vector of a nonlinear oscillating system. The proposed method guarantees not only the state estimation performance but also the state matching performance of the designed sampled-data fuzzy observer. The state matching aims at making the difference between the state trajectories of a redesigned sampled-data fuzzy observer and the given continuous-time fuzzy observer small enough. Thus, it is assumed that the continuous-time fuzzy observer was appropriately designed in advance. To solve the IDR problem, we propose the linear matrix inequality (LMI)-based design condition that simultaneously guarantees both the state estimation and the state matching performance. Also, the conservativeness of the design condition is relaxed by developing a novel integral inequality condition and a fuzzified time-dependent Lyapunov–Krasovskii functional (LKF). Finally, the superiority of the proposed methodology is demonstrated by showing the state estimation and the state matching performance.

This study aims to propose a sampled-data control technique, utilizing a linear matrix inequality (LMI) approach, to achieve string-stable vehicle platooning in a cooperative adaptive cruise control (CACC) system with communication delays. To do this, a decentralized sampled-data controller design technique that combines one controller using sensor measurements and another one utilizing vehicle-to-vehicle (V2V) communication, ensuring both individual and string stability, is proposed first. Next, a memory sampled-data control (MSC) approach is presented to account for transmission delays in V2V communication. Additionally, an improved Lyapunov–Krasovskii functional (LKF) is presented to improve computational complexity and sampling performance. The design conditions are formulated as linear matrix inequalities (LMIs) in the time domain, facilitating efficient stability analysis and optimization. Finally, vehicle platooning simulations are provided to validate the effectiveness and feasibility of the proposed technique.