Extended Dissipative Filter for Delayed T-S Fuzzy Network of Stochastic System with Packet Loss

This research investigates a time-varying delay-based adaptive event-triggered dissipative filtering problem for the interval type-2 (IT-2) Takagi-Sugeno (T-S) fuzzy networked stochastic system. The concept of extended dissipativity is used to solve the ,  and dissipative performances for (IT-2) T-S fuzzy stochastic systems in a unified manner. Data packet failures and latency difficulties are taken into account while designing fuzzy filters. An adaptive event-triggered mechanism is presented to efficiently control network resources and minimise excessive continuous monitoring while assuring the system’s efficiency with extended dissipativity. A new adaptive event triggering scheme is proposed which depends on the dynamic error rather than pre-determined constant threshold. A new fuzzy stochastic Lyapunov-Krasovskii Functional (LKF) using fuzzy matrices with higher order integrals is built based on the Lyapunov stability principle for mode-dependent filters. Solvability of such LKF leads to the formation of appropriate conditions in the form of linear matrix inequalities, ensuring that the resulting error mechanism is stable. In order to highlight the utility and perfection of the proposed technique, an example is presented

Observer–Based Control for a New Stochastic Maximum Power Point tracking for Photovoltaic Systems With Networked Control System

This study discusses the new stochastic maximum power point tracking (MPPT) control approach towards the photovoltaic cells (PCs). PC generator is isolated from the grid, resulting in a direct current (DC) microgrid that can provide changing loads. In the course of the nonlinear systems through the time-varying delays, we proposed a Networked Control Systems (NCSs) beneath an event-triggered approach basically in the fuzzy system. In this scenario, we look at how random, variable loads impact the PC generator’s stability and efficiency. The basic premise of this article is to load changes and the value matching to a Markov chain. PC generators are complicated nonlinear systems that pose a modeling problem. Transforming this nonlinear PC generator model into the Takagi–Sugeno (T–S) fuzzy model is another option. Takagi–Sugeno (T–S) fuzzy model is presented in a unified framework, for which 1) the fuzzy observer–based on this premise variables can be used for approximately in the infinite states to the present system, 2) the fuzzy observer–based controller can be created using this same premises be the observer, and 3) to reduce the impact of transmission burden, an event-triggered method can be investigated. Simulating in the PC generator model for the realtime climate data obtained in China demonstrates the importance of our method. In addition, by using a new Lyapunov–Krasovskii functional (LKF) for combining to the allowed weighting matrices incorporating mode-dependent integral terms, the developed model can be stochastically stable and achieves the required performances. Based on the T-P transformation, a new depiction of the nonlinear system is derived in two separate steps in which an adequate controller input is guaranteed in the first step and an adequate vertex polytope is ensured in the second step. To present the potential of our proposed method, we simulate it for PC generators

Stochastic Stability Analysis for Networked Markov Jump System

552-557In the paper, we describe the development and implementation of reliable H∞filters for a class of Networked Markov Jump Systems (NMJS) with random sensor failures that are triggered by events. The plant's nonlinear dynamic is approximated with a NMJS. Failures of sensors are described using stochastic variables. The Event-Triggered Mechanism (ETM) is introduced to NCS, which offers some positive points over other schemes. Using the event-triggered mechanism, data of sensors from the plant will be only transmitted if it contradicts the specified condition. By considering the effects of an ETM and the sensor faults, the event-based filter is developed for NMJS. The design parameters of the filter as well as sufficient conditions for its existence are given accurately based on Linear Matrix Inequality (LMI)

Quantized Dissipative Observer-Based Output Feedback Control for a Class of Markovian Descriptor Jump Systems with Communication Delay

This paper investigates the problem of quantized dissipative observer-based output feedback control of Markovian descriptor jump systems with unavailable states, appearing networked-induced delay. The descriptor systems are presented as Markovian jump systems which give a more realistic presentation for a variety of nonlinear dynamical systems than conventional state-space representation. To accomplish the objective, a uniform framework is employed to design the delayed Markov observer-based controller and event-triggered scheme. Additionally, we provided the ℋ∞ and ℒ2-ℒ∞ and dissipative performance indices which are robust against the disturbances with time-varying delays. Moreover, a novel Lyapunov–Krasovskii functional is considered to guarantee the closed loop for stochastic stability analysis of the Markovian descriptor jump system. The solvability of Lyapunov–Krasovskii functional results in the formation of linear matrix inequalities. The controller and observer gains can be obtained by solving the linear matrix inequalities. Simulations are performed to validate the proposed scheme

Fault detection for asynchronous T–S fuzzy networked Markov jump systems with new event‐triggered scheme

Abstract In this article, an adaptive event‐triggered fault detection problem for the asynchronous Takagi–Sugeno fuzzy networked Markov jump systems is investigated based upon the time‐varying delays. The purpose of designing a fault detection filter is to detect the fault signal under the influence of disturbance with network transmission. In the design process, one essential factor, time‐varying delay in the fuzzy filter with appearing in the residual signal, is taken into consideration. In order to rationally utilise network resources and elaborately avoid unnecessary continuous monitoring, an adaptive event‐triggered scheme is designed to guarantee the Takagi–Sugeno fuzzy networked Markov jump systems. Thus it helps to lower the energy consumption of communication while ensuring the performance of the system. Different from the conventional triggering mechanism, in this article, the parameters of the triggering function are based on a new adaptive law which is obtained online rather than a predefined constant. Based on the associated Lyapunov stability theory and appropriate inequality, some sufficient criteria in the form of linear matrix inequalities are obtained to ensure the stability of the resulting error system. Finally, a tunnel diode example is employed to illustrate the effectiveness of the proposed methods

Extended Dissipative Filter for Delayed T-S Fuzzy Network of Stochastic System with Packet Loss

450-459This research investigates a time-varying delay-based adaptive event-triggered dissipative filtering problem for the interval type-2 (IT-2) Takagi-Sugeno (T-S) fuzzy networked stochastic system. The concept of extended dissipativity is used to solve the ∞, ∞ and dissipative performances for (IT-2) T-S fuzzy stochastic systems in a unified manner. Data packet failures and latency difficulties are taken into account while designing fuzzy filters. An adaptive event-triggered mechanism is presented to efficiently control network resources and minimise excessive continuous monitoring while assuring the system’s efficiency with extended dissipativity. A new adaptive event triggering scheme is proposed which depends on the dynamic error rather than pre-determined constant threshold. A new fuzzy stochastic Lyapunov-Krasovskii Functional (LKF) using fuzzy matrices with higher order integrals is built based on the Lyapunov stability principle for mode-dependent filters. Solvability of such LKF leads to the formation of appropriate conditions in the form of linear matrix inequalities, ensuring that the resulting error mechanism is stable. In order to highlight the utility and perfection of the proposed technique, an example is presented

Single Magnetic Element-Based High Step-Up Converter for Energy Storage and Photovoltaic System with Reduced Device Count

The multiport DC-DC power converter is a prominent area of research in power electronics due to its highly dense design, reduced device count, and high energy efficiency. In this paper, a nonisolated single magnetic element-based high step-up three-port converter for an energy storage system is presented. The proposed converter has two input ports and one output port. The coupled inductor with switched capacitor is used to achieve high voltage gain. The key features of the proposed converter are high conversion gain, low voltage stress, zero voltage switching (ZVS), and zero current switching (ZCS). The detailed theoretical analysis and operation of the converter are elaborated. The energy efficiency of the proposed converter is calculated and compared with the other counterparts. Ansys Maxwell is used for the coupled inductor finite element modeling. To verify the applicability and functionality of proposed converter, a 100 W converter with two inputs (48 V and 96 V) and one output 360 V at 100 kHz is tested in the laboratory

Robust Stability Analysis for Class of Takagi-Sugeno (T-S) Fuzzy With Stochastic Process for Sustainable Hypersonic Vehicles

Recently, the rapid development of Unmanned Aerial Vehicles (UAVs) enables ecological conservation, such as low-carbon and “green” transport, which helps environmental sustainability. In order to address control issues in a given region, UAV charging infrastructure is urgently needed. To better achieve this task, an investigation into the T–S fuzzy modeling for Sustainable Hypersonic Vehicles (SHVs) with Markovian jump parameters and H∞ attitude control in three channels was conducted. Initially, the reentry dynamics were transformed into a control–oriented affine nonlinear model. Then, the original T–S local modeling method for SHV was projected by primarily referring to Taylor’s expansion and fuzzy linearization methodologies. After the estimation of precision and controller complexity was assumed, the fuzzy model for jump nonlinear systems mainly consisted of two levels: a crisp level and a fuzzy level. The former illustrates the jumps, and the latter a fuzzy level that represents the nonlinearities of the system. Then, a systematic method built in a new coupled Lyapunov function for a stochastic fuzzy controller was used to guarantee the closed–loop system for H∞ gain in the presence of a predefined performance index. Ultimately, numerical simulations were conducted to show how the suggested controller can be successfully applied and functioned in controlling the original attitude dynamics