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In this study, a new systematic approach is proposed to design the fuzzy controller for a class of Takagi–Sugeno fuzzy‐partial differential equation (TS fuzzy‐PDE) systems which describe the non‐linear distributed parameter system formulated by first‐order semi‐linear hyperbolic PDEs. In this study, non‐quadratic Lyapunov function is utilised and some slack matrices are introduced to derive stability conditions in terms of linear matrix inequalities (LMIs). The proposed approach has three main features. First, stability conditions are not derived in the form of spatial differential LMI. Second, conservativeness of LMI conditions is reduced. Third, there is no restriction on the form of semi‐linear hyperbolic PDE systems and therefore more semi‐linear systems classes can be stabilised. Also, the proposed approach is more suitable for practical implementation compared with the recently published papers. [ABSTRACT FROM AUTHOR]

In this paper, in order to control a class of nonlinear uncertain power systems, a new simple indirect adaptive general type-II fuzzy sliding mode controller (IDAGT2FSMC) is proposed. For handling dynamic uncertainties, the proposed controller utilizes the advantages of general type-2 fuzzy logic systems (GT2FLS) to approximate unknown nonlinear actions and noisy data. Implementing general type-2 fuzzy systems is computationally costly; therefore, to decrease computational burden, the proposed method uses a recently introducedα-plane representation so that GT2FLS can be seen as a composition of several interval type-2 fuzzy logic systems (IT2FLS) with a corresponding level of α for each. The globally asymptotic stability of the closed-loop system is mathematically proved. To evaluate the superiority of the proposed controller, performance of the proposed method is compared with those of Indirect Adaptive type-1 Fuzzy Sliding Mode (IDAFSM) controller, Indirect Adaptive Interval Type-II Fuzzy Sliding Mode (IDAT2FSM) controller, conventional Sliding Mode controller (SMC) and PID controller results which are all among the most recent methods applied to the issue in question. Finally, the proposed method is applied to an uncertainly chaotic power system as a case study. Simulation indicates the effectiveness of the proposed controller while facing of dynamic uncertainties and external disturbances. [ABSTRACT FROM AUTHOR]

The control of electrical vehicles (EVs) should be designed robustly and adaptively to improve the system on both dynamic and steady‐state performances, because EV systems are basically time variant (e.g. the operation parameters of EV and the road condition are always varying) that make the operation of controlling an EV difficult. In this study, general type‐2 fuzzy logic sets and the modified harmony search algorithm techniques for adaptive tuning of the most popular existing proportional–integral (PI) controller is integrated in order to address these uncertainties. Although it is computationally expensive to carry out general type‐2 fuzzy systems, it can be examined as a composition of several interval type‐2 fuzzy logic systems with a corresponding level of α for each by using a new plan which is presented, general type‐2 fuzzy set. The controller uses the linguistic rules directly. The achieved results are compared with optimal fuzzy‐PI controller and optimal interval type‐2 fuzzy‐PI controller results which are the most recent researches into in the present issue to evaluate the proficiency of the proposed controller. The simulation results demonstrate the successfulness and effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

The intensive non‐linear system and plants of modern industry highly motivating researcher to extend and evolve non‐linear control systems. In this study, in order to control a class of non‐linear uncertain power systems in the presence of large and fast disturbances, a new simple indirect adaptive proportional‐integral is proposed. For handling dynamic uncertainties, the proposed controller utilises the advantages of least squares support vectors regression (LS‐SVR) to approximate unknown non‐linear actions and noisy data. The LS‐SVR is used to approximate the non‐linear uncertainties which must be bounded, whereas no requirement needs for bounds to be known. The globally asymptotic stability of the closed‐loop system is mathematically proved by using Lyapunov synthesis. To show the merits of the proposed approach, a non‐linear electric vehicle (EV) system is considered as a case study. The goal is to force the speed of EV to track a desired reference in the present of structured and unstructured uncertainties. The experimental data, new European driving cycle, is used in order to examine the performance of proposed controller. The simulation studies on a second‐order EV with the presence of fast against slow and large against small disturbances demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy logic controller for power management of hybrid wind turbine (WT), photovoltaic (PV), fuel cell (FC), and ultra-capacitor (UC) for stand-alone applications. WT and PV are the primary power sources of the system, and an FC is expected to provide long-term energy balance, whereas the UC is employed as buffer storage for the short-term compensation. The improvement in fuzzy logic strategies enhances dynamic response of the FC/UC power system under various load conditions by suitable rule base design for fuzzy controller. The combined use of FC and UC has the potential for better energy efficiency and lifetime increment of FC technology. These rules are designed such that charging and discharging the UC voltage is controlled automatically. The fuzzy controller creates a balance between production and consumption with determination of the exact reference values for WT, PV, and FC. This causes a good frequency controlled in the hybrid generation power system. The fuzzy logic strategies performance under different condition has been verified by using real weather data practical load demand profile. To demonstrate the effectiveness of the proposed fuzzy logic strategies, comparison with improved frequency control method was performed using matlab/simulink by integrating the detailed mathematical and electrical models of the hybrid generation power system. [ABSTRACT FROM AUTHOR]