In this study, we explore the robust H ∞ control for uncertain T-S fuzzy systems with random time-varying delay and packet dropouts. The novel fuzzy model is presented to account for the stochastic nature of delays. A new Lyapunov-Krasovskii functional (LKF) is developed upon a delay decomposition technique. Less conservative results are obtained by segmenting the delay interval and considering the probability distribution within each segment. Once the time-varying delay is known, conservatism is further reduced. We derive two sufficient criteria using reciprocally convex inequalities. A new delay-derivative dependent method is proposed. Unlike previous research, this approach effectively incorporates delay derivative information to build the system with stochastic time-varying delay. The superiority of our approach is thoroughly demonstrated by simulations involving three examples as well as a model of truck-trailer.

In this paper, the problem of state estimation for a fractional-order neural networks system with uncertainties is studied by a sampled-data controller. First, considering the convenience of digital field, such as anti-interference, not affected by noise, a novel sampled-data controller is designed for the fractional-order neural network system of uncertainties with changeable sampling time. In the light of the input delay approach, the sampled-data control system of fractional-order is simulated by the delay system. The main purpose of the presented method is to obtain a sampled-data controller gain K to estimate the state of neurons, which can guarantee the asymptotic stability of the closed-loop fractional-order system. Then, the fractional-order Razumishin theorem and linear matrix inequalities (LMIs) are utilized to derive the stable conditions. Improved delay-dependent and order-dependet stability conditions are given in the form of LMIs. Furthermore, the sampled-data controller can be acquired to promise the stability and stabilization for fractional-order system. Finally, two numerical examples are proposed to demonstrate the effectiveness and advantages of the provided method.

In this paper, a dual closed-loop active disturbance rejection control (ADRC) scheme based on the swarm intelligent technique is investigated for quadrotors to address several challenging control problems. First, a composite control system is designed to guarantee a satisfied quadrotor flight. Then, an improved particle swarm optimization (IPSO) algorithm with constriction factors, simulated annealing (SA) and adaptive jump strategy is developed to enhance its capacity of escaping from the local optimal trap and thoroughly exploring the search space. Simultaneously, the IPSO algorithm is employed to obtain the optimal parameters of ADRC controller. Comparative simulation and platform experiments show that the proposed IPSO-based ADRC controller can satisfy the control requirements of the inner-outer loop well.

In this paper, we address the asymptotic stability problem for the fractional-order neural networks system with uncertainty based on sampled-data control. First, considering the influence of uncertainty and fractional-order on the system, a new sampled-data control scheme with variable sampling period is designed. According to the input delay approach, the dynamics of the considered fractional-order system is modeled by a delay system. The main purpose of the problem addressed is to design a sampled-data controller, such that the closed-loop fractional-order system can guarantee the asymptotic stability. Then, the fractional-order Razumishin theorem and linear matrix inequalities (LMIs) are used to derive the stable conditions. The new delay-dependent and order-dependent stability conditions are presented in the form of LMIs. Furthermore, the sampling controller can be obtained to ensure the stability and stabilization of fractional-order system. Finally, a numerical example is given to demonstrate the effectiveness and advantages of the proposed method.

This paper investigates the problem of stability and stabilization for Takagi-Sugeno (T-S) fuzzy systems with adaptive event-triggered scheme (AETS) based on sampled-data control. AETS is used to relieve network congestion and save bandwidth resources. A novel Lyapunov–Krosovskii functional (LKF) is proposed by introducing the available information of fuzzy membership functions (FMFs) and sampling instant. The FMFs approach, extended reciprocal convex inequality technique and some slack matrices are fully utilized to deal with the derivative of the LKF. Then, an improved criterion with less conservatism is obtained to guarantee the stability of T-S fuzzy system. Moreover, the standard conditions are given in the form of linear matrix inequalities by the matrix decoupling technique. Finally, the feasibility and effectiveness of the proposed method can be demonstrated through a numerical simulation.