This article concentrates on the sampled-data control problem by utilizing a novel mismatch parameter-dependent stabilization method for the Takagi–Sugeno (T-S) fuzzy system. First, taking the information on sampled-parameter and fuzzy weighted function into account, a novel sampled-parameter-dependent fuzzy Lyapunov function is proposed. Furthermore, an affine matched sampled-data controller is designed to contain an affine transformed membership function, thereby achieving larger stabilizable regions. Based on a novel fuzzy Lyapunov function and parameterized matrices bounding technique, a sufficient condition concerning the asymptotic stability of the closed-loop fuzzy system is formulated in the form of linear matrix inequality. In comparison with the prior works, the derived condition has less conservative and the largest sampling interval. Numerical experiments on Rosser's system confirm the advantages and benefits of the novel mismatch parameter-dependent stabilization approach.

In this article, an iterative cost-learning model predictive control (ICLMPC) is proposed for nonlinear networked control systems (NCSs) in the presence of aperiodic sampling. The proposed ICLMPC is useful not only to guarantee asymptotic stability of the closed-loop system with aperiodic sampling but also to improve control performance in the case of performing an iterative task. In the proposed method, the nonlinear system of NCSs is mathematically represented as an aperiodic sampled-data Takagi–Sugeno (T–S) fuzzy system. Based on this representation, the ICLMPC design is formulated in terms of a finite-horizon optimal control problem in which a new terminal cost function is considered. The terminal cost function is constructed by a Lyapunov function with a looped-functional and an iteratively minimized function (IMF). From the Lyapunov function with the looped-functional, it is possible to guarantee that the ICLMPC asymptotically stabilizes the aperiodic sampled-data T–S fuzzy system. To obtain an iteratively improved control performance, the IMF takes the minimized value among the integrals of the collected data at each iteration. The validity and effectiveness of the proposed method are illustrated by two practical examples in the simulation section.

Shape memory alloy (SMA) has superior actuation capability over the limit of the scale. However, inherently low controllability is a primary issue that hinders practical usage. To address this challenge, this paper presents an SMA-based artificial muscle actuator capable of the displacement sensing through the capacitive sensor. To realize sensing capability, the theoretical model-based design and fabrication process are proposed. Here, we show that the actuator can be controlled at intervals of 100 μm as well as maintaining sensing capability while lifting 90 times heavier than its weight. To exhibit the usefulness of the actuator to an optical device, we integrate the actuator into the mirror tilting device, which has 20 degrees tilting angle. We expect that the proposed actuator can overcome the scale limit of meso-scale devices, which require payload capacity and controllability, simultaneously.

Stabilization of type-2 fuzzy system in the presence of cyber attacks is investigated in this article. For a practical application, a class of nonlinear system can be represented by an interval type-2 fuzzy system through a set of membership functions. Unlike existing schemes, 1) affine membership functions are considered in the controller design; moreover, 2) a robust adaptive event-triggered control is proposed to avoid the unwanted triggering events, which makes the proposed scheme more reliable and relaxes the conservativeness of stability analysis.In the numerical simulation, the mass-spring-damper system and the tracking control system are considered to illustrate the robustness and effectiveness of the proposed approach.

In this paper, we propose a constrained H∞ controller design method for the active suspension systems (ASS) with aperiodic sampling. The ASS are controlled by a hydraulic actuator which is usually limited by physical constraints. To utilize the information of saturation level, we express the ASS as the linear parameter varying (LPV) system by adopting time-varying parameters which depend on the constrained input. For the implementation of digital controllers, we consider the sampled-data H∞control. The stabilization condition is derived by using looped functional approach. The proposed controller guarantees the desired performance of the ASS despite of aperiodic sampling and input saturation. To demonstrate the validity of the proposed method, the simulation results are compared with the case of passive suspension systems.