Ruey-Huei Yeh

This work studies the novel sliding mode control (SMC) of discrete nonlinear stochastic switching models under semi-Markovian parameter and incomplete semi-Markovian kernel (SMK). The characteristic of nonlinear system is described by an interval type-2 fuzzy (IT2F) model that can be recognized as a collection of several type-1 fuzzy models. The uncertainties in system parameters is efficiently captured using the lower and upper grades of membership. Based on the mode-dependent Lyapunov function and incomplete SMK, sufficient conditions are proposed to ensure the stability of sliding dynamics. Moreover, an IT2F SMC law based on learning strategy is developed such that the state signals are guided onto the predetermined sliding region and the mode switchings-induced chattering is effectively reduced. Finally, the IT2F SMC strategy is validated through the simulation of truck-trailer model.

This article studies the adaptive fuzzy asynchronous (AFA) stabilization of discrete networked hidden stochastic semi-Markovian switching power systems under cyber attacks. Due to the complex network environment, cyber-attacks are taken into account, in which the fuzzy logic rule is adopted to describe the unknown deception attacks. Considering the mismatch mechanism between the controller and the system, an adaptive fuzzy controller runs asynchronously with the system, where the hidden semi-Markovian model is used to characterize the asynchronous mechanism. Based on the detected mode and the fuzzy logic rule, an AFA stabilizing controller is designed for the underlying system. Using the stochastic Lyapunov function related to the detected mode and system mode, sufficient criteria are given for the AFA controller design, ensuring that the underlying system is bounded stable in the mean square. Finally, the proposed scheme is verified by the simulated example.

Experts in traditional pricing literature often assume that consumers are rational in their purchasing decisions, and tend to ignore the effect of psychological behavior. One of the fundamental irrational psychological behaviors is overconfidence, which refers to overestimation. The benefits of three-part tariffs under demand uncertainty caused by overconfident consumers have been demonstrated in various forms. In the present study, we investigate the design of a three-part tariff pricing plan when consumers are overconfident in the sense of overestimating their amount of service usage or consumption. An analytical model and a numerical analysis are carried out using a genetic algorithm approach to determine the optimal three-part tariff and to compare other tariff structures (e.g., two-part tariff and menu of three-part tariff) to investigate the critical factors influencing their performance. With overconfidence in consumers, service providers need clear direction on how to optimally implement a three-part pricing strategy. Specifically, we observe the effect of overconfidence on the profitability of service providers, and their other performance in general. Thus, service providers are given clear guidance on when to offer three-part tariffs and alternative structures to overconfident consumers and push marketing plans to increase their profits and market share.

This article addresses the practically predefined-time adaptive fuzzy tracking control problem of strict-feedback nonlinear stochastic systems, where the system under consideration includes stochastic disturbances and uncertain parameters. First, in this study, practically predefined-time stochastic stabilization (PPSS) in the $p$ th moment sense is introduced, and a Lyapunov-type criterion for PPSS is proposed to assure the stabilization of the system considered. With these ideas, based on the backstepping design method, a semiglobally practically predefined-time adaptive fuzzy tracking control algorithm is proposed with a fuzzy system used to approximate the unknown part of the system. Moreover, the settling time of the system response can be arbitrarily adjusted in a mean-value sense, and such freedom can be used to improve the stochastic finite-/fixed-time control results. Finally, a practical example and a numerical example of a comparison are provided to validate the effectiveness of the proposed control strategy.

The event-triggered sliding-mode control (SMC) for discrete-time networked Markov jumping systems (MJSs) with channel fading is investigated by means of a genetic algorithm. In order to reduce resource consumption in the transmission process, an event-triggered protocol is adopted for networked MJSs. A key feature is that the signal transmission is inevitably affected by fading phenomenon due to delay, random noise, and amplitude attenuation in a networked environment. With the aid of a common sliding surface, an event-triggered SMC law is designed by adjusting the system network mode. Under the framework of stochastic Lyapunov stability, sufficient conditions are constructed to ensure the mean-square stability of the closed-loop networked MJSs, and the sliding region is reached around the specified sliding surface. Moreover, based on the iteration optimizing accessibility of objective function, an effective SMC approach under genetic algorithm is proposed to minimize the convergence region around the sliding surface. Finally, the effectiveness of the proposed method is proved by the F-404 aircraft model.

Background: Exploring the impact of combining Industry 4.0 technologies and Lean Manufacturing tools on organizational performance has been a popular topic in recent years. Design/Methodology/Approach: We propose a novel Smart and Sustainable Manufacturing System (SSMS) to provide management insights related to social impact, economic performance, and environmental impact. Some tools called Dynamic Lean 4.0 tools, such as Sustainable Value Steam Mapping (VSM), Extended Single Minute Exchange of Die (SMED), and Digital Poka-Yoke, are presented as outputs of synergistic relationships that optimize production processes. Originality/Research gap: There are few studies on the application of SSMS. This work presents a case study, aiming to fill this gap. A case study of vacuum degassing equipment fabrication is presented to demonstrate the improvement of utilizing the Define-Measure-Analyze-Improve-Control (DMAIC) method with Digital Poka-Yoke. Key statistical results: The implementation of this project increased the process capability index, Cpk, from 1.278 to 2. Practical Implications: It was concluded that the company successfully implemented a smart and sustainable manufacturing system, and created a safer working environment and new job opportunities, while increasing production yield from 99.44% to 100%, improving worker utilization, and directly saving NT$68,000. Limitations of the investigation: This paper is the use of a single case study. More applications of Dynamic Lean 4.0 tools in SSMS should be explored.

In this article, the problem of reachable set estimation (RSE) and the design of aperiodic sampled-data controller for Takagi–Sugeno fuzzy Markovian jump systems (T–S FMJSs) with unit-energy bounded disturbance (UEBD) and unit-peak bounded disturbance (UPBD) inputs are taken into consideration. First, sufficient conditions that all states of the T–S FMJSs are encompassed by ellipsoids under zero initial conditions are acquired via constructing a mode-dependent two-sided loop-based Lyapunov function and applying a linear matrix inequality approach. Second, the RSE is taken into account in the design of the state feedback aperiodic sampled-data controller with the aim that the resulting ellipsoid encompasses the reachable set of the closed-loop system. Finally, a nonlinear mass-spring model and a tunnel diode circuit model demonstrate the efficiency of the presented approach. In addition, this method is able to obtain a larger sampled-data period than other literature, thus saving bandwidth and reducing communication resources.

Global greenhouse gas emissions must be reduced to achieve net-zero carbon emissions. One of the solutions for the reduction of greenhouse gas emissions is the adoption and transition from conventional vehicles to electrical vehicles (EVs). Previously, most research on EVs have been from a consumer adoption perspective, few of them are from industry transition and consumer adoption perspectives simultaneously. This also highlights the importance of SDG 12 (responsible for consumption and production). Additionally, the analyses were mostly obtained using one methodology and demonstrated only by weighting without relationships among factors. To consider the problem of adoption and transition, a systematic method should be developed. Therefore, this study intends to identify, prioritize, and display the relationship between EV adoption barriers from an automotive industry perspective using an analytic network process (ANP) and the decision-making trial and evaluation laboratory (DEMATEL) method. The research results show two contributions: First, the identified and prioritized barriers that automakers encounter in EV transition also explored the interrelationships among these barriers. Second, a model comparison of two multicriteria decision-making approaches was conducted to prioritize and identify the interlinkages among EV uptake barriers.