This paper investigates the design of an event-triggered-based integral sliding mode control for a class of multi-input Takagi-Sugeno (T-S) fuzzy model (TSFM)-based nonlinear systems with disturbance. Incorporating integral sliding mode control scheme can be used along with the event-triggered control (ETC) to deal with the simultaneous external disturbances and unnecessary waste of system resources. To further accommodate the characteristic of event-triggered integral sliding mode control (ETISMC), two fuzzy integral switching surfaces are established. One is the triggered-state dependent switching surface which is proposed to obtain the ETISMC law. The other one is the conventional continuous-state dependent switching surface which is designed to ensure the robustness of the controlled system from the initial time instance. The proposed ETISMC is applicable for both singleinput and multiinput, and despite the previous studies, a more reasonable ETISMC law is proposed with asynchronous premise variables. Based on Lyapunov function and adopting a decreasing time-varying triggering threshold in the event-triggered condition, the ultimately bounded stability of the closed-loop system is guaranteed. Sufficient conditions for the ultimate bounded of the trajectories of the system states are formulated in terms of new offline linear matrix inequalities (LMIs). Moreover, the positive lower bound of the internal execution is ensured, which means that there is no Zeno phenomenon. To demonstrate the superiority of the proposed technique, the suggested ETISMC is applied to a two-degree freedom helicopter system and the simulation results verify the validity of the proposed control strategy.

This paper introduces the design of an artificial fuzzy Lyapunov-based model predictive integral sliding mode control (MPISMC) to achieve the stability of the closed-loop Takagi-Sugeno (T-S) fuzzy model-based nonlinear systems. First, to reach proper performance against the model uncertainties, a fuzzy integral sliding mode controller (FISMC) is designed. Then, the fuzzy model predictive control is developed based on a fuzzy Lyapunov function (FLF) by considering a contractive constraint and an ellipsoidal terminal constraint. A systematic method is developed to reach the recursive feasibility of the model predictive control (MPC) optimization problem based on an ellipsoidal terminal set. Also, a contractive fuzzy Lyapunov condition is imposed on the fuzzy model predictive control problem to guarantee the stability of closed-loop systems which is led to a linear matrix inequality (LMI)-based generalized eigenvalue minimization problem. In the proposed approach, FISMC greatly improves the robustness property of the fuzzy Lyapunov-based model predictive control (FLMPC) and asymptotic stability of the closed-loop system is achieved in comparison with tube-based MPC. In addition, the proposed robust MPC has less computational burden and improves the equilibrium point attractivity compared with the max-min MPC and the equality terminal constraint-based MPC. To illustrate the superiority of the proposed strategy, the suggested robust MPC is applied to a truck-trailer system and a numerical example. The simulation results show the capabilities of the proposed robust MPC.

This article proposes a new dynamic observer-based event-triggered integral sliding mode control incorporating disturbance observer (DO) for the load frequency control of a multiarea interconnected power system (IPS) considering load disturbances and uncertainties. To improve the system performance and reduce the cost implementation, first a dynamic observer is designed for the IPS to achieve the state estimation. Then, to reduce the chattering effect and improve the robust performance, a DO is designed to obtain the estimated of the unmatched disturbances. To further reduce the chattering effect and computational burden, based on the obtained state estimation and the estimated disturbances, two integral sliding surfaces are proposed. The periodic-time one that is proposed to ensure the robust performance from the initial-time and the time-triggered one that is proposed to obtain the event-triggered control law. Utilizing the Lyapunov theory, the ultimate boundedness stability of the controlled system is studied and sufficient conditions are derived by solving a set of offline linear matrix inequalities. During the system implementation, it is also verified that the controlled system is Zeno-free. Finally, the proposed control strategy is compared with some existing studies and the simulation results demonstrate the effectiveness and the superiority over the existing approaches.

This paper introduces a new data-driven MPC structure based on two offline and online parts to achieve the robust and constrained performance in an optimal scheme. In the first step, according to the model matching condition, an offline data-driven controller is designed to reach the tracking performance. In addition, to reduce the effects of the external disturbance, a data-driven-based disturbance observer is presented to estimate the external disturbance. Therefore, the robustness against the external disturbances is achieved in an offline procedure. Then, a data-driven model predictive control (MPC) is structured based on a data-driven-based model of a stabilized system. In other words, the overall controller is configured such that the limitations of the system states and control input are considered in the control design process. Moreover, by employing the move blocking strategy, the online computational burden of the suggested controller is greatly reduced. To further improve of the feasibility problem, an ellipsoidal terminal (ET) constraint is considered. The rows number of the blocking matrix influences on the ET set which leads to feasibility enhancement. So, the main contributions of the presented data-driven controller are feasibility improvement and reducing online computational burden in an optimal and constrained scheme which are illustrated in the simulation section.

This paper proposes a novel observer-based event-triggered integral sliding mode control (SMC) strategy for Takagi-Sugeno fuzzy systems (TSFM) subject to unknown inputs, external disturbances, and model uncertainties. To ensure robust state estimation, a newly formulated unknown input observer (UIO) is developed that accommodates nonlinear output functions, thereby overcoming the limitations of existing methods that assume linear outputs. Additionally, a disturbance observer is introduced to estimate unmatched external disturbances effectively. Two fuzzy integral sliding surfaces are designed, a periodic-time surface to enhance robustness and a time-triggered surface to reduce computational burden and control signal chattering. An event-triggered mechanism based on a time-decaying threshold is incorporated to optimize control update instances, ensuring Zeno-free behavior. Lyapunov-based stability analysis confirms the ultimate boundedness stability (UBS) of the closed-loop system, and controller gains are computed through linear matrix inequalities (LMIs) and steady-state output theory. Simulation studies, including a numerical and practical truck-trailer system, validate the superior performance of the proposed control strategy in terms of disturbance rejection, reduced computation, and robustness compared to conventional methods.