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This study aims to present a method for the conceptual design and simulation of an aircraft flight control system.

The design methodology is based on particle swarm optimization (PSO). PSO can be used to improve the performance of conventional controllers. The aim of the present study is threefold. First, it attempts to detect and isolate faults in an aircraft model. Second, it is to design a proportional (P) controller, a proportional derivative (PD) controller, a proportional-integral (PI) controller and a fuzzy controller for an aircraft model. Third, it is to design a PD controller for an aircraft using a PSO algorithm.

Conventional controllers, an intelligent controller and a PD controller-based PSO were investigated for flight control. It was seen that the P controller, the PI controller and the PD controller-based PSO caused overshoot. These overshoots were 18.5, 87.7 and 2.6 per cent, respectively. Overshoot was not seen using the PD controller or fuzzy controller. Steady state errors were almost zero for all controllers. The PD controller had the best settling time. The fuzzy controller was second best. The PD controller-based PSO was the third best, but the result was close to the others.

This study shows the implementation of the present algorithm for a specified space mission and also for study regarding variation of performance parameters. This study shows fault detection and isolation procedures and also controller gain choice for a flight control system. A comparison between conventional controllers and PD-based PSO controllers is presented. In this study, sensor fault detection and isolation are carried out, and, also, root locus, time domain analysis and Routh–Hurwitz methods are used to find the conventional controller gains which differ from other studies. A fuzzy controller is created by the trial and error method. Integral of squared time multiplied by squared error is used as a performance function type in PSO.

This paper aims to examine a congestion situation of a certain type of restaurant in a theme park (Tokyo Disney Resort) by a simulation based on Little’s law, which is a basic principle in Queueing theory. In the restaurant, a guest (customer) lines up to order, pay and receive dishes. A problem is that even when a guest can easily find vacant tables, it takes a long time to receive dishes. Because guests can see there are vacant tables, there are many tweets of complaints. This situation is a factor to undermine customer satisfaction.

This paper proposes dedicated special menu lines only providing special set as one solution that can be realized at a low cost to reduce vacant tables. Here, if the number of special menu lines is fixed, the difference between a queue in regular lines and that in special menu lines will be big. To shorten the difference, the author proposes a technique to regulate by using feedback control (Proportional control or Fuzzy control).

The simulation result shows that the number of vacant tables decreases by about 16 per cent compared with the current situation.

This paper considers a specific restaurant, but the proposed method can be applied to the same type of restaurant in the theme park. If the restaurant in the theme park is crowded, the feedback control of the queue brings new possibilities.

本论文旨在检验主题公园（日本迪士尼）内饭店的拥堵情境。本论文采用同步模拟的方式，以等候理论的基础原则—科特尔法则为基础，来进行检验。在一家饭店中，客人（顾客）排队点餐、付款、以及拿到食物。其中问题是，即使客人能够轻松找到空桌位，而等餐的时间非常长。因为客人看到有空桌位，所以客人会有很多抱怨。这种情境会严重影响顾客满意度。

本论文提出一种特别菜单行数设计来用低成本的方式解决空桌位的问题。如果特别菜单行数是固定的，那么使用常规菜单和特别菜单所产生的排队现象会大有不同。本论文借助反馈控制（比例控制或模糊控制），提供一种调节方法以减少这种差别。

模拟结果表明空桌位的数量同现有情境相比，减少了16%.

本论文采用特定的饭店，但是提出的方法可以推广到同种类型的主题公园饭店。如果主题公园饭店人满为患，那么使用反馈控制来调价排队情况将带来新可能。

主题公园、饭店、等候理论、科特尔法则、模糊控制、比例控制

The purpose of this paper is to look at the problem of fault tolerant control (FTC) for discrete time nonlinear system described by Interval Type-2 Takagi–Sugeno (IT2 TS) fuzzy model subjected to stochastic noise and actuator faults.

An IT2 fuzzy augmented state observer is first developed to estimate simultaneously the system states and the actuator faults since this estimation is required for the design of the FTC control law. Furthermore, based on the information of the states and the faults estimate, an IT2 fuzzy state feedback controller is conceived to compensate for the faults effect and to ensure a good tracking performance between the healthy system and the faulty one. Sufficient conditions for the existence of the IT2 fuzzy controller and the IT2 fuzzy observer are given in terms of linear matrix inequalities which can be solved using a two-step computing procedure.

The paper opted for simulation results which are applied to the three-tank system. These results are presented to illustrate the effectiveness of the proposed FTC strategy.

In this paper, the problem of active FTC design for noisy and faulty nonlinear system represented by IT2 TS fuzzy model is treated. The developed IT2 fuzzy fault tolerant controller is designed such that it can guarantee the stability of the closed-loop system. Moreover, the proposed controller allows to accommodate for faults, presents a satisfactory state tracking performance and outperforms the traditional type-1 fuzzy fault tolerant controller.

Service-oriented architecture is an emerging software architecture, in which web service (WS) plays a crucial role. In this architecture, the task of WS composition and verification is required when handling complex requirement of services from users. When the number of WS becomes very huge in practice, the complexity of the composition and verification is also correspondingly high. In this paper, the authors aim to propose a logic-based clustering approach to solve this problem by separating the original repository of WS into clusters. Moreover, they also propose a so-called quality-controlled clustering approach to ensure the quality of generated clusters in a reasonable execution time.

The approach represents WSs as logical formulas on which the authors conduct the clustering task. They also combine two most popular clustering approaches of hierarchical agglomerative clustering (HAC) and k-means to ensure the quality of generated clusters.

This logic-based clustering approach really helps to increase the performance of the WS composition and verification significantly. Furthermore, the logic-based approach helps us to maintain the soundness and completeness of the composition solution. Eventually, the quality-controlled strategy can ensure the quality of generated clusters in low complexity time.

The work discussed in this paper is just implemented as a research tool known as WSCOVER. More work is needed to make it a practical and usable system for real life applications.

In this paper, the authors propose a logic-based paradigm to represent and cluster WSs. Moreover, they also propose an approach of quality-controlled clustering which combines and takes advantages of two most popular clustering approaches of HAC and k-means.

This paper aims to investigate the failure modes present in the dangerous operations and modify processes. After the identification of potential failures, prioritization will be made to rank them for quick attention and immediate correction in the operation/modification.

The methodology utilizes the strength of hazard and operability (HAZOP) study and failure mode and effect analysis (FMEA) to identify and prioritize the hidden potential failures present in the system. Fuzzy linguistics approach has been applied to enhance the performance of the study. This study correlates HAZOP study, FMEA and fuzzy system.

This proposed technique is used to find the better ranking of the failure modes. Risk priority number and fuzzy weighted geometric mean of the risk factors are used to improve the performance of the risk evaluation. This makes the assessment easier to be carried out effectively for critically operated systems.

This proposed approach could be very useful for the systematic and rational risk assessment of passive systems.

A new decision‐making approach is used to prioritize the failure modes present in the process industry with use of a case study.

The purpose of this paper is to present a fuzzy logic-based control system for controlling the protected cultivation and describes its advantages over the traditional greenhouse automation control systems.

In terms of systems theory, the greenhouse represents a complex non-linear system with emphasized subsystem interactions. Applying a non-linear control encompasses a number of difficulties due to incomplete knowledge of system dynamic. System decoupling is used in order to obtain simplified control structures for independent control loops. This gives limited results due to strong interaction between system variables and such control system does not allow optimization of system behavior primarily in terms of energy efficiency and/or water consumption.

The paper presents a design of fuzzy logic-based controller, which optimizes the greenhouse energy and water consumption. The design includes the main linguistic variables for sensor and actuator subsystems. Membership functions of Fuzzy Inference System (FIS) are generated and simulation and analysis of the behavior of the designed control system is performed.

Obtained result shows that the designed control system beside its relative simplicity is flexible and adaptive, taking into account the differences in crop varieties and growth stages.

Preliminary simulation of energy savings compared with the costs on actual field shows also good results. Still, number of different control strategies has to be applied in order to increase system flexibility regarding the different varieties and different stages of their growth.

Obtaining an integrated controller based on fuzzy logic will highly improve possibilities for mass production of cheap technology. It will be easy to use by growers enabling them to incorporate their own informal growing knowledge and to create actual growing control strategy.

The purpose of this paper is to present a novel approach in terrain following (TF) flight using fuzzy logic. The fuzzy controller as presented in this work decides where and how the aircraft needs to change its altitude. The fast decision‐making nature of this method promises real‐time applications even for tough terrains in terms of shape and peculiarities. The method could always assist to design trajectories in an off‐line manner.

To achieve the aforementioned goal, the method effectively incorporates the dynamics of the aircraft. Basically, the mathematical method employs special relationships among existing slope of the terrain and its derivative together with aircraft flying speed and height above the ground to construct suitable fuzzy rules. The fuzzification method is based on Sugeno and three rule‐sets are used for fuzzy structure. These rules are implemented using Fuzzy Logic Toolbox in MATLAB.

Different case studies conducted for flights in XZ‐plane show the effectiveness of the method as compared to other existing methods available to the authors. The results illustrate a good tracking based on the fuzzy approach while using both 18 and 27 rules with respect to the optimal approach. Furthermore, it is shown that decreasing number of rules from 27 to 18 rules causes only minor changes in the solution.

The current work offers a new approach in low‐level flights where maintaining a suitable height above the ground is essential. This is especially important for civil aircraft approaching an airport with low or non‐visibility and during aborted landing manoeuvres. The domain of the current work is however confined to only planning of TF manoeuvres. Nevertheless, the work could be expanded into TF/terrain avoidance and three‐dimensional manoeuvres which are not in the scope of the current work.

The current work addresses the problems associated with low‐level flight; such as TF using artificial intelligence and fuzzy logic. The provided intelligence helps the aircraft conduct TF manoeuvres by understanding the general patterns of the existing terrain. The method is fast enough to be applied for real‐time applications.

Effectuation, which articulates the process of entrepreneurial action based on nonpredictive control logic, is receiving extensive scholarly attention. What drives the effectual entrepreneurship is featured with high complexity. However, existing studies ignored the complex driving forces underlying entrepreneurial decision-making. Building on a configurational perspective, the purpose of this study was to examine the combinative effects of environmental uncertainty and entrepreneurs’ means on effectual entrepreneurship.

Drawing on 54 entrepreneurs who are launching new ventures in China, this study adopts a fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate two sets of antecedent conditions and how they form different combinations for a highly effectual entrepreneurship.

Our findings disclose four highly effectual entrepreneurship paths involving novice–specialist effectual entrepreneurship in a highly uncertain environment, socialite–specialist effectual entrepreneurship in a highly uncertain environment, pure-specialist effectual entrepreneurship and resourceful effectual entrepreneurship, and one path of barefoot noneffectual entrepreneurship in a highly uncertain environment, which reveals the complex nature of environmental uncertainty and entrepreneurs’ means in driving entrepreneurs to adopt effectuation.

Our study makes the following contributions. First, by taking a configurational perspective, we are able to obtain an elaborate view of the combined effects of environmental uncertainty and entrepreneurs’ means on effectual entrepreneurship. Second, we expand prior thinking on the relationship between environmental uncertainty and effectuation. Third, our study offers a more delicate understanding of entrepreneurs’ means in driving effectuation by splitting means into three separate factors.

This paper is concerned with reducing the barriers imposed on the flexibility and responsiveness of automated manufacturing systems by current control software technology. The general question that is addressed by this research is, how can insights be gained from the manufacturing system that can assist the control system in meeting this goal of responsive behavior? The approach that is taken is to investigate appropriate means of integrating available manufacturing system information into the control system. A framework for integrating status information to control an automated assembly line is introduced that combines both transient information (e.g. station queue length) and steady‐state information (e.g. station gradient estimates) obtained by observing the operation of the assembly line. It is shown that, through the use of an appropriately designed fuzzy‐logic controller (FLC), the combined information results in flow time performance superior to that achieved using the transient or steady‐state measures individually.

This paper aims to present a hybrid actuator controller to obtain fast and stiff position response without any overshoot by blending input signals of a DC servomotor and a particle brake.

The hybrid actuator controller has a module to estimate instantaneous changes in inertia and a blending algorithm that adjusts input signals to the motor and the brake so that together, as a hybrid actuator, they can achieve a fast, stiff position response without overshoot. The control logic implemented in the controller is derived from the kinematics of the system. For the blending algorithm, two separate cases are explored in which the user has the option to either utilize the full‐braking capacity or specify a safe deceleration limit for the system.

The blending algorithm enables the system to operate nearly twice as fast as the motor‐only case without any overshoot or oscillations. The controller can reject inertial load changes and significant external disturbances.

Such hybrid actuators along with the developed controller can be used in robotics and automation to increase the system accuracy and operational speed resulting in higher production rates. In addition, much stiffer haptic force feedback interfaces for virtual reality applications can be designed with smaller actuators. The blending algorithm provides considerable improvements and uses a physics‐based simple and easy‐to‐implement structure.