Stabilization of type-2 fuzzy system in the presence of cyber-attacks is investigated in this paper. For a practical application, a class of nonlinear system can be represented by an interval type-2 (IT2) 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 (ETC) 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 (MSD) system and the tracking control system are considered to illustrate the robustness and effectiveness of the proposed approach.

Online monitoring of external torque/force is receiving much attention with the increasing demand in industries, where robot manipulators are required to cooperate with humans, assemble product parts or perform certain tasks that involve the interaction with fragile objects. Estimation of external torque when the robot manipulator interacts with the external environment is investigated in this article. A higher order sliding-mode-based observer is designed to estimate the external torque online against nonlinear friction. Moreover, a Luenberger observer is designed to stabilize the general momenta error dynamics by using sum of squares programming. Lyapunov analysis is considered to demonstrate the stability of an overall system and the convergence of angular position tracking errors. Simulation as well as experimental results using 7-DOF Sawyer robot under various practical scenarios demonstrate the effectiveness of the proposed scheme.

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.

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. Firstly, taking the information on sampled-parameter and fuzzy weighted function into account, a novel sampled-parameterdependent 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 matrixinequality (LMI).In comparisonwith theprior 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 parameterdependent stabilization approach.