The main contribution of this paper is to develop a new flowmeter fault detection approach based on optimized non-singleton type-3 (NT3) fuzzy logic systems (FLSs). The introduced method is implemented on an experimental gas industry plant. The system is modeled by NT3FLSs, and the faults are detected by comparison of measured end estimated signals. In this scheme, the detecting performance depends on the estimation and modeling performance. The suggested NT3FLS is used because of the existence of a high level of measurement errors and uncertainties in this problem. The designed NT3FLS with uncertain footprint-of-uncertainty (FOU), fuzzy secondary memberships and adaptive non-singleton fuzzification results in a powerful tool for modeling signals immersed in noise and error. The level of non-singleton fuzzification and membership parameters are tuned by maximum correntropy (MC) unscented Kalman filter (KF), and the rule parameters are learned by correntropy KF (CKF) with fuzzy kernel size. The suggested learning algorithms can handle the non-Gaussian noises that are common in industrial applications. The various types of flowmeters are investigated, and the effect of common faults are examined. It is shown that the suggested approach can detect the various faults with good accuracy in comparison with conventional approaches.

In the paper, mathematical models of the supercapacitors are investigated. The models are based on electrical circuits in the form of RC ladder networks. The elementary cell of the network may consist of resistances and capacitances that are connected in series or parallel. The dynamic behavior of the circuit is described using fractional-order differential equations and its properties are analyzed. The identification procedure with quadratic performance index is performed in time domain to identify the parameters of the supercapacitor models. The results of numerical simulations are compared with the results measured experimentally in the physical system. In addition, an example from the automotive industry is used for an experimental evaluation of the theoretical analysis and to present a perspective on the applicability of the approach for other industrial projects.

This research manuscript proposes an Improved Manta Ray Foraging Optimization (IMRFO) algorithm for the power system congestion cost problem. The goal of the proposed Congestion Management (CM) strategy is twofold: firstly, the Generator Sensitivity Factors (GSF) is determined to select and involve the most influential power system generators that will reschedule their real power to alleviate the excess power flow in congested transmission lines. Secondly, the IMRFO has been developed and applied to attain the minimum possible congestion cost. The IMRFO has been formulated with the inclusion of correction factors in the exploration and exploitation phases to improve the coordination between these phases. The effectiveness of IMRFO has been measured considering its effective performance on the 23 conventional benchmark functions. 39 bus New England and IEEE-118 bus test system has been utilized to authenticate the effectiveness of the CM approach with the application of IMRFO. The outcomes highlight that the congestion cost achieved with IMRFO has been reduced by, of 16.08%, 13.73%, 11.78%, and 4.48 % for the 39-bus system and 14.84%, 12.97%, 9.63%, and 6.85% for 118 bus system when compared to the Bacteria Forge Optimization (BFO), Grey Wolf Optimization (GWO), Sine-Cosine Algorithm (SCA), and Original MRFO. The results gained with the implementation of IMRFO on the CM problem portrays appreciable minimization in the congestion cost, enhancement in the system voltage and losses, generates better convergence profile and computational time when contrasted with the recent optimization methods.

This paper designs and implements a chattering-free terminal sliding mode control approach for a class of chaotic systems with unknown uncertainties. It considers sliding mode control (SMC) to deal with the dynamic model uncertainties of the chaos system, and uses a combination of SMC with an adaptive control approach to solve the upper boundaries problem of unknown model uncertainties and their estimation. Chattering is completely eliminated without over estimating the control gains by adopting an adaptive continuous barrier function in the dynamic switching function. Using the Lyapunov’s stability theory, it was shown that the proposed scheme can guarantee the convergence of system states to the vicinity of the sliding surface in finite time. Additionally, the adoption of a sliding surface with a nonlinear and integral switching function resulted in removing the reaching phase of the sliding surface and yielding a controller that is robust to uncertainties from the start. The effectiveness of the proposed control method was assessed using three scenarios implemented to a Liu’s uncertain chaotic system in MATLABSimulink environment. The obtained results confirmed the ability of the proposed approach to achieve continuous and smooth control rules for such chaotic systems. Among the main attributes of the proposed control method are its ability to completely eliminate chattering and yield a robust performance against model uncertainties and unknown external disturbances; common issues in chaotic systems.

Given the various advantages of electric vehicles compared to conventional gasoline vehicles in terms of energy efficiency and environmental pollution (among others), this paper studies the factors affecting customers’ willingness to purchase electric vehicles. An integrated discrete choice and agent-based approach is applied to model the customers’ choice for the valuation of electric vehicles based on the internal reference price. The agent-based model evaluates customers’ preferences for a number of personal and vehicle attributes, according to which vehicle they chose. Data from 376 respondents are collected to estimate a random-parameter logit model where customers are asked to reveal their preferences about five attributes of electric vehicles, including travel range, top speed, charge cost, government incentives, and price. The role of social networks of customers and their threshold purchase price is also examined in the agent-based model. The scenario simulation results indicate that the allocation of government incentives for electric vehicles, decreasing electric vehicle/non-electric vehicle price gap, expanding electric vehicle travel range, increasing gasoline prices, and enhancing electric vehicle top speed stimulate electric vehicle market shares, respectively.

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.