Disturbance observer-based fuzzy event-triggered ISMC design: Tracking performance.

This article presents a disturbance observer (DO)-based event-triggered integral sliding mode tracking control (ISMTC) for continuous-time Takagi-Sugeno (T-S) fuzzy model (TSFM) subject to external disturbances. Merging the event-triggered control (ETC) with integral sliding mode control (ISMC) approach is led to reach the better accommodate the features of ISMC. To do this, two forms of fuzzy integral sliding surfaces (FISSs) are proposed. The first is the periodic-time-based FISS that is presented to provide the robustness of the tracking performance from the initial moment. The second is the event-triggered-based FISS which is proposed to obtain the fuzzy event-triggered ISMTC law. To more decrease the chattering effects and compensate the tracking performance against the disturbances, a fuzzy disturbance observer (FDO) is proposed to estimate the mismatched disturbances. Compared with the existing works, a more practical controller is proposed based on the asynchronous premise variables. Utilizing the common quadratic Lyapunov function, appropriate conditions are derived to verify that the tracking error is robust from the initial moment. Furthermore, by adopting a decaying triggering threshold, it is guaranteed that the system is Zeno-free during the process. To verify the effectiveness of the suggested event-triggered ISMTC, a practical system including continuous stirred tank reactor (CSTR) is simulated and the results are compared.

Multi-objective T-S fuzzy control of Covid-19 spread model: An LMI approach.

Due to the importance of control actions in spreading coronavirus disease, this paper is devoted to first modeling and then proposing an appropriate controller for this model. In the modeling procedure, we used a nonlinear mathematical model for the covid-19 outbreak to form a T-S fuzzy model. Then, for proposing the suitable controller, multiple optimization techniques including Linear Quadratic Regulator (LQR) and mixed H 2 - H ∞ are taken into account. The mentioned controller is chosen because the model of corona-virus spread is not only full of disturbances like a sudden increase in infected people, but also noises such as unavailability of the exact number of each compartment. The controller is simulated accordingly to validate the results of mathematical calculations, and a comparative analysis is presented to investigate the different situations of the problem. Comparing the results of controlled and uncontrolled situations, it can be observed that we can tackle the devastating hazards of the covid-19 outbreak effectively if the suggested approaches and policies of controlling interventions are executed, appropriately.

Reliable fuzzy control of uncertain nonlinear networked systems under actuator faults.

This study investigates a reliable fuzzy static-output feedback control (SOFC) scheme for uncertain Takagi-Sugeno fuzzy model (TSFM)-based nonlinear systems under the networked induced delays, information package losses and actuator faults, simultaneously. For this purpose, firstly, a comprehensive model for the actuator fault is suggested to enhance the performance of the actuator in the networked control systems (NCSs). More precisely, the suggested model contains an additive stochastic perturbation term in the actuator to further realize the control scheme. Besides, the Markov chain (MC) is employed to model the networked induced arbitrary delays and the information package losses. Hence, the corresponding closed-loop system lies in the Markovian jump systems (MJS). Then, based on the Lyapunov theory, the stochastic robust stability of the obtained system is studied and necessary conditions are extracted in new offline linear matrix inequalities (LMIs). Since the accurate value of the transition probabilities (TP) of the MC is not definite and even the identification is difficult, in view of practical applications in our control strategy, partly unknown TPs are assumed. Finally, to validate the superiorities of the suggested control approach, a truck-trailer system is adopted and simulated. The simulation results confirm the superiorities of the suggested control approach.

New stability and stabilization conditions for nonlinear systems with time-varying delay based on delay-partitioning approach.

This paper focuses on stability analysis and stabilization of nonlinear systems with interval time-varying delay, modeled by Takagi-Sugeno (T-S) fuzzy approach. To achieve more relaxation in the feasibility region, delay-partitioning approach is used for all integral terms in the Lyapunov-Krasovskii functional (LKF). A fuzzy Lyapunov function is proposed instead of non-integral term in LKF, and moreover, some slack matrices variables are offered to enlarge the design space. By doing this, new delay-dependent stability criteria are obtained. During the derivation of stability conditions, Jensen's integral inequality is applied to deal with integral terms. Furthermore, in this paper the problem of controller design via the parallel distributed compensation (PDC) scheme is studied. Stability and stabilization conditions with less conservative are achieved in terms of linear matrix inequality (LMI). Finally, two numerical examples are presented to show the effectiveness of the proposed results.