Robust Mixed Performance Control of Uncertain T-S Fuzzy Systems With Interval Time-Varying Delay by Sampled-Data Input

Abstract

In this paper, a sampled-data parallel distributed compensator (PDC) is proposed to guarantee mixed H 2 /H ∞ performance of uncertain T-S fuzzy systems with interval time-varying delay and linear fractional perturbations. A full matrix formulation approach is developed to present our main results in LMI conditions. To achieve better results, new inequality and Lyapunov-Krasovskii functional are developed to improve the conservativeness of the proposed results. Finally, some numerical examples are illustrated to show the use of our main results. In this paper, interval time-varying delay and interval sampling are considered instead of constant delay and periodic sampling in published literatures.

Robust H∞ Controller Design of Switched Delay Systems with Linear Fractional Perturbations by Synchronous Switching of Rule and Sampling Input

“…It is interesting to note that fractional-order systems with time delay [28,29] and sampling input for uncertain systems with actuator saturation [30] will be challenging research topics in our future work. Synchronous switching of rule and input to reach mixed H 2 , H ∞ , and passive performances of uncertain fractional-order switched delay systems can be investigated in the future [25].…”

“…We conclude that system (1) with ( 2) and ( 24) stabilizes with H ∞ performance γ = √ γ= 0.5177 using the switchingrule in (25) and sampling input in (26) with K i = Ki Û−T in (27). Some comparisons are made in Table 1 to show the improvement using the proposed results.…”

Mixed H∞ and passivity guaranteed cost proportional‐integral control for interval‐valued type‐2 fuzzy systems with input delay

“…Hence, T-S fuzzy-model-based systems have attracted many researchers which leads to an abundance of data. [3][4][5] However, the type-1 FLCS cannot be applied to the scrutiny of nonlinear plants with parameter uncertainties. For such cases, in 1999, Mendel et al 6 proposed the IT-2 fuzzy logic system which employs the lower and upper grades of membership functions to effectively manipulate the nonlinearity and uncertainties of a plant and thus provide better performance than that of the type-1 approach.…”

“…This framework addresses nonlinear systems in which the dynamics are characterized by the average weight sums of simple linear subsystems, the weights of which are defined by type‐1 fuzzy membership functions. Hence, T‐S fuzzy‐model‐based systems have attracted many researchers which leads to an abundance of data 3‐5 . However, the type‐1 FLCS cannot be applied to the scrutiny of nonlinear plants with parameter uncertainties.…”

Robust (Q, S, R)‐γ‐dissipative control of Takagi–Sugeno fuzzy systems with interval time‐varying delay and sampling

“…T‐S fuzzy models can be applied to design the nonlinear control schemes which will be used to achieve the performance requirements of systems under consideration. This designed schemes for feedback state for T‐S fuzzy control systems will be the useful tools to stabilize the practical nonlinear models; such as autonomous surface vehicle, motor control system, neural network predictive feedback controller, nonlinear modeling problems, a diesel engine controller, sliding mode learning controller, models for truck‐trailer, wind turbine control systems 7–9 . This designing approach will be a good connection for the linear control and the fuzzy logic.…”