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Recent advancements in patient rehabilitation integrate both traditional and modern techniques to enhance treatment efficacy and accessibility. Hydrotherapy, leveraging water's physical properties, is crucial for reducing joint stress, alleviating pain, and improving circulation. The rehabilitation of upper limbs benefits from technologies like virtual reality and robotics which, when combined with hydrotherapy, can accelerate recovery. Exoskeletons, which support and enhance movement, have shown promise for patients with neurological conditions or injuries. This study focused on implementing and comparing proportional–integral–derivative (PID) and fuzzy logic controllers (FLCs) in a lower limb exoskeleton. Initial PID control tests revealed instability, leading to a switch to a PI controller for better stability and the development of a fuzzy control system. A hybrid strategy was then applied, using FLC for smooth initial movements and PID for precise tracking, with optimized weighting to improve performance. The combination of PID and fuzzy controllers, with tailored weighting (70% for moderate angles and 100% for extensive movements), enhanced the exoskeleton's stability and precision. This study also explored quantum computing techniques, such as the quantum approximate optimization algorithm (QAOA) and the quantum Fourier transform (QFT), to optimize controller tuning and improve real-time control, highlighting the potential of these advanced tools in refining rehabilitation devices. [ABSTRACT FROM AUTHOR]

This study introduces the improved fractional ordered Darwinian particle swarm optimization (IFODPSO) method combined with fuzzy logic controllers to optimize motor performance. By utilizing direct torque control (DTC) techniques controlled by IFODPSO, the system achieves quick response times and instantaneous torque generation. The research addresses the optimization of motor performance using the IFODPSO approach for combination with fuzzy logic controllers. The specific problem being addressed is the control of torque in motor applications. The scope of the research includes comparing the performance of the IFODPSO-FLC approach with common field-oriented control (FOC) as well as DTC approaches. The significance of the results attained is noteworthy as the IFODPSO-FLC approach demonstrates promising outcomes in terms of torque control compared to traditional FOC and DTC techniques. Additionally, the proposed PI-fuzzy opposition estimation helps improve system efficiency at low speeds by compensating for variations in stator resistance. The combination of fuzzy logic controllers and Darwinian particle swarm optimization of fractional order approaches presents a novel and efficient solution. The results are evaluated using MATLAB-Simulink, and the obtained performance demonstrates the potential for successful motor control applications. [ABSTRACT FROM AUTHOR]

In smart cities, bicycle-sharing systems have become an essential component of the transportation services available in major urban centers around the globe. Due to environmental sustainability, research on the power-assisted control of electric bikes has attracted much attention. Recently, fuzzy logic controllers (FLCs) have been successfully applied to such systems. However, most existing FLC approaches have a fixed fuzzy rule base and cannot adapt to environmental changes, such as different riders and roads. In this paper, a modified FLC, named self-tuning FLC (STFLC), is proposed for power-assisted bicycles. In addition to a typical FLC, the presented scheme adds a rule-tuning module to dynamically adjust the rule base during fuzzy inference processes. Simulation and experimental results indicate that the presented self-tuning module leads to comfortable and safe riding as compared with other approaches. The technique established in this paper is thought to have the potential for broader application in public bicycle-sharing systems utilized by a diverse range of riders. [ABSTRACT FROM AUTHOR]

This study introduces an innovative method to improve the effectiveness of Permanent Magnet Brushless DC Motor (PMBLDCM) systems by using a Single-Stage Power Factor Correction (PFC) converter. The suggested system employs a buck half-bridge DC-DC converter as a PFC converter to enhance power quality and regulate the voltage source inverter (VSI) that supplies PMBLDC motor. The PFC converter's front end consists of a diode bridge rectifier (DBR) that is coupled to a singlephase AC main. Therefore, the VSI functions just as an electrical switch for the PMBLDC motor, leading to energy-efficient operation. In addition, this study expands the inquiry by examining the utilization of fuzzy logic controller (FLC) in the suggested drive system. This research investigates the effectiveness of FLC in improving the performance of PMBLDC motor drives, which are often used in complex and nonlinear systems, unlike traditional applications. FLC are examined for their capacity to manage indeterminate system behaviors, hence enhancing control strategies that are more precise and dependable. The findings of the comprehensive investigation illustrate the adaptability and efficacy of FLC in enhancing the efficiency of the suggested drive system over a broad spectrum of speeds and input AC voltages. The results highlight the capability of this integrated method in attaining exceptional efficiency and energy preservation in PMBLDC motor drives. [ABSTRACT FROM AUTHOR]

Wind energy producing facilities are often located based on the presence of wind resources, resulting in their construction in remote places that are far from high voltage power transmission lines and large consumption centers. Wind farms that use squirrel cage induction generators (SCIG) directly linked to the power grid constitute a substantial proportion of wind energy conversion systems worldwide. However, in situations when wind farms are linked via medium voltage (MV) distribution lines because of controlled electricity output, a prevalent problem occurs called Wind Farm to Weak Grid Connection. This scenario is characterized by power production that is similar to the capacity of the grid for transportation, leading to inadequate control of voltage at the point where the grid is connected (PCC). This work presents a new compensation method that utilizes the Unified Power Quality Compensator (UPQC) with a unique internal control system based on fuzzy logic controllers. The aim is to tackle the difficulties related to connecting Wind Farms to weak grids. The UPQC, which is a Custom Power System (CUPS) device, presents a possible alternative for improving power quality in such situations. The suggested compensation technique entails the use of the UPQC series converter to regulate voltage at the WF terminals, while using the shunt converter to filter power provided by the WF and reduce voltage swings at the grid side. The internal control approach prioritizes the regulation of active and reactive power in converters and the transfer of power between them via the UPQC DC-Link. The suggested technique differs from traditional tactics by using both reactive power and DC-bus energy storage to optimize compensating capabilities. Additionally, it involves active power sharing amongst UPQC converters. The efficacy of the fuzzy logic improved UPQC compensating technique in enhancing both Power Quality and Wind Farm stability is proven via meticulous simulations. [ABSTRACT FROM AUTHOR]

Twin‐roll strip casting is a near‐net‐shape casting technology that can produce thin steel strips directly from molten steel. Stably controlling the molten steel level is regarded as an important issue to ensure strip quality and casting process stability. As the control of the molten steel level is a time‐varying, nonlinear, and multidisturbance complex system, it is difficult to establish an accurate process model for designing a model‐based controller. Top side‐pouring twin‐roll casting is a new kind of twin‐roll strip casting technology. This study introduces the control system of the top side‐pouring twin‐roll casting process. A fuzzy logic controller (FLC) with its fuzzy rules optimized by particle swarm optimization (PSO) is developed to regulate the molten steel level. Simulation results show that the performance of the FLC can be improved while its fuzzy rules are optimized by PSO. The objective function of PSO has a great influence on the optimization of the fuzzy rules. The top side‐pouring twin‐roll casting experiments are carried out using the FLC with its fuzzy rules optimized by PSO; the results show that strip quality and casting process stability are guaranteed. [ABSTRACT FROM AUTHOR]

the article introduces a method for controlling the speed of a six-phase permanent magnet synchronous motor (PMSM) drive with the help of fuzzy logic controllers (FLCs). By utilizing the vector control technique, the fuzzy logic controller achieves a high level of dynamic performance. The FLC is implemented in the current and speed controllers of the six-phase PMSM drive. To evaluate its effectiveness under different operating conditions, the FLC with PMSM drives is designed and simulated using MATLAB. The FLC proves to be more robust and capable of overcoming the nonlinearity issues associated with the six-phase PMSM. The simulation outcomes confirm the efficiency of the suggested control technique for the PMSM drive system.

Internet of things (IoT) devices are often located in difficult-to-access places without connection to the electrical grid. For this reason, some IoT devices usually incorporate a small stand-alone photovoltaic (PV) system to power only the IoT device. However, several IoT applications involve using other components, such as instrumentation, electrical motors, lighting bulbs, etc., which require additional electrical power. The objective of this study was to design and implement a battery controller integrated into a constrained resource device that allows powering not only other components of the IoT application but also the IoT device. In this way, the IoT device controls and monitors the PV system and executes other IoT applications such as lighting. Results show that the designed controller exhibits efficient behavior when compared with other regulators and can be integrated into resource-constrained devices, improving the life of batteries and reducing cost. [ABSTRACT FROM AUTHOR]

The double-inverted pendulum (DIP) constitutes a classical problem in mechanics, whereas the control methods for stabilizing around the equilibrium positions represent the classic standards of control system theory and various control methods in robotics. For instance, it functions as a typical model for the calculation and stability of walking robots. The present study depicts the controlling of a double-inverted pendulum (DIP) on a cart using a fuzzy logic controller (FLC). A linear-quadratic controller (LQR) was used as a benchmark to assess the effectiveness of our method, and the results showed that the proposed FLC can perform significantly better than the LQR under a variety of initial system conditions. This performance is considered very important when the reduction of the peak system output is concerned. The proposed controller equilibration and velocity tracking performance were explored through simulation, and the results obtained point to the validity of the control method. [ABSTRACT FROM AUTHOR]

Two wheeled inverted pendulum (TWIP) resembles many industrial as well as real life applications. TWIP is underactuated, nonlinear and unstable system. Therefore, it is widely used as benchmark for illustrating control concepts, theories, and algorithms that deal with these challenges. The main motivations of this paper are designing controllers that deal with lack of information that accompanying an inexpensive cheap TWIP and verified its stability, proper speed, robustness and smooth tracking. Consequently, intelligent technique instead of conventional ones that depends on mathematical models should be used. In this article hybrid controllers, consisting of fuzzy controllers and three mode controllers' (PID), are developed. Two major techniques are used, Mamdani-Like Fuzzy and Fusion-Based function, to design five controllers. Moreover, dynamic indices and steady state indices are implemented to choose the most proper controllers among various designed ones. A new criterion is defined to measure steady state performance of oscillation. All controllers were tested in real-time to control the angle of the TWIP for stability and disturbance rejection. The experimental results showed that both strategies of Mamdani-Like Fuzzy and Fusion-Based fuzzy can control and balance adequately the TWIP, but the PD-Like fuzzy yielded more better performance. [ABSTRACT FROM AUTHOR]

This paper presents the energy management tool of a power system operating in a smart grid that contains electric vehicles. The intention of this work is to make a comparison between a metaheuristic optimization technique and two fuzzy logic controllers, and with that highlight the advantages of using fuzzy logic and validate it to the detriment of other metaheuristic techniques. The optimization technique used was simulated annealing, in order to minimize the total energy cost of the system being studied. Tree charging strategies were adopted: peak charging, off-peak charging, and smart charging besides demand-side management techniques. In addition to the charging process will also be studied the battery electric vehicles discharging, preferably at the peak of the load curve, through the creation of a charging/discharging station. In this work, the system used is the IEEE-39 bus New England power system. Two fuzzy logic controllers have been developed, namely the charging station controller and the vehicle-to-grid controller. Together they decide the proper energy flow between the EVs and the grid. Energy discharge to the grid from EVs or energy required for charging EVs is controlled and tested for the real-time scenario. The results proved the effectiveness of the proposed method in studies of planning and expansion of electric energy systems that contain electric vehicles. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) devices are often located in difficult-to-access places without connection to the electrical grid. For this reason, some IoT devices usually incorporate a small stand-alone photovoltaic (PV) system to power only the IoT device. However, several IoT applications involve using other components, such as instrumentation, electrical motors, lighting bulbs, etc., which require additional electrical power. The objective of this study was to design and implement a battery controller integrated into a constrained resource device that allows powering not only other components of the IoT application but also the IoT device. In this way, the IoT device controls and monitors the PV system and executes other IoT applications such as lighting. Results show that the designed controller exhibits efficient behavior when compared with other regulators and can be integrated into resource-constrained devices, improving the life of batteries and reducing cost.

The huge power requirements of future railways require the usage of energy-efficient strategies towards amore intelligent railway system. The usage of on-board energy storage systems enables better usage of the traction energy with a higher degree of freedom. In this article is proposed a top-level charging controller forthe on-board and wayside railway energy storage systems. Its structure comprehends two processing levels: a real-time fuzzy logic controller for each energy storage system, and a genetic algorithm meta-heuristic, that remotely and automatically tune the fuzzy rules weight. As global results, the reduction of regenerated energy is 22.3% with the fuzzy logic controller. With the optimization strategy, this reduction can be further extendedto 28.7%. The need for a smart railway framework is also discussed towards a realistic implementation of such charging strategy. Thus, with a high degree of flexibility, the efficiency of railway energy systems can be increased with the proposed framework.

This article explains the design of fuzzy logic controllers (FLCs) for level processes which is generally used in numerous control operations. The main purpose of the proposed design is to maintain the liquid level in the tank at the desired level. In this paper, the fuzzy logic controller is chosen as the controller for the level process because of its fault tolerance, knowledge representation, expertise, nonlinearity, uncertainty, and real-time manipulation. Fuzzy logic controllers have been developed and compared in the Mamdani version. Performance on proportional derivatives (PD) and proportional-integral-derivatives (PID) controllers. Whereas traditional PD and PID controllers are simple, dependable and eliminate steady-state errors, fuzzy logic controllers are rule-based systems that are a logical model of human behavior in processes of the proposed design. The response is provided as follows: The LabVIEW software has been validated. It is used to simulate the proposed system. Comparing error indicators such as PD controller, PID controller, fuzzy logic controller integral absolute error, integral quadratic error, time and absolute error integral, time and quadratic error integral, fuzzy logic controller is observed from the simulation results. increase. It offers better performance than other controllers. [ABSTRACT FROM AUTHOR]

The huge power requirements of future railways require the usage of energy-efficient strategies towards a more intelligent railway system. The usage of on-board energy storage systems enables better usage of the traction energy with a higher degree of freedom. In this article is proposed a top-level charging controller for the on-board and wayside railway energy storage systems. Its structure comprehends two processing levels: a real-time fuzzy logic controller for each energy storage system, and a genetic algorithm meta-heuristic, that remotely and automatically tune the fuzzy rules weight. As global results, the reduction of regenerated energy is 22.3% with the fuzzy logic controller. With the optimization strategy, this reduction can be further extended to 28.7%. The need for a smart railway framework is also discussed towards a realistic implementation of such charging strategy. Thus, with a high degree of flexibility, the efficiency of railway energy systems can be increased with the proposed framework. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) emulator (PVE) is a non‐linear power supply that produces a similar output to that of the PV module. It requires a closed‐loop controller such as the proportional–integral (PI) and fuzzy logic controllers to operate properly. The PI controller has a low‐computational burden and is not robust since the transient response varies under different load conditions. Although the fuzzy logic controller is robust, it requires high‐computation capability. This study proposed an alternative approach called the shift controller for the PVE to easily compute and maintain a fast transient response under various load conditions. The performance of the proposed shift controller for the PVE is compared with the performance of the PVEs using the PI and fuzzy logic controllers, respectively. The controllers are simulated using MATLAB/Simulink and implemented using dSPACE ds1104 for experimental validation. The results show that the proposed shift controller computes up to 18 times faster than the fuzzy logic controller. The proposed controller also responds faster to load change than the PI controller. [ABSTRACT FROM AUTHOR]

• Introduction of the notion of semantic similarity controller. • Design of semantic similarity controllers being both accurate and inter- pretable. • Evaluation using a pool of methods and datasets for semantic similarity measurement. Recent advances in machine learning have been able to make improvements over the state-of-the-art regarding semantic similarity measurement techniques. In fact, we have all seen how classical techniques have given way to promising neural techniques. Nonetheless, these new techniques have a weak point: they are hardly interpretable. For this reason, we have oriented our research towards the design of strategies being able to be accurate enough but without sacrificing their interpretability. As a result, we have obtained a strategy for the automatic design of semantic similarity controllers based on fuzzy logics, which are automatically identified using genetic algorithms (GAs). After an exhaustive evaluation using a number of well-known benchmark datasets, we can conclude that our strategy fulfills both expectations: it is able of achieving reasonably good results, and at the same time, it can offer high degrees of interpretability. [ABSTRACT FROM AUTHOR]

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Utilisation of renewable energy sources (RES) is increasing day by day to reduce greenhouse emissions. The toxic emission from ship is the main concern in marine sector. Here, utilisation of renewable energy for propulsion and electrification of accessories in a ship are proposed. Microgrid with AC and DC bus is developed using solar panels, wind mills, fuel cell, diesel generator, and energy storage devices. Energy management system with two fuzzy logic controllers (FLCs) is used to select and manage energy in the microgrid. Selection of source is decided by FLC1 based on the availability of RES. Generation of control pulses for inter‐linking converters is decided by FLC2 based on variation in solar irradiance and wind velocity. The microgrid with RES is simulated using MATLAB/SIMULINK. The results show that uncertainty in RES can be handled by FLCs to provide a continuous power supply for transportation of ship and its accessories. [ABSTRACT FROM AUTHOR]

Bu çalışmada, Aralık değerli tip-2 bulanık PID (ADT2-BPID) kontrolörlerin içyapıları incelenmiş olup ve de yeni bir öz-ayarlama önerilmiştir. Bu amaçla ilk olarak geleneksel yani tip-1 bulanık PID (T1-BPID) kontrolörler ile ADT2-BPID kontrolörlerin yapısal özellikleri ve tasarım parametreleri incelenmiştir. T1-BPID kontrolörler için önerilmiş olan bir öz-ayarlama yöntemi olan fonksiyon tabanlı öz-ayarlama yöntemi ADT2-BPID kontrolör yapılarına uygulanmıştır. Bu öz ayarlama yöntemi yardımıyla ADT2-BPID kontrolörün ölçekleme çarpanlarının çevrimiçi ayarlanabileceği gösterilmiştir. Önerilen öz-ayarlamalı ADT2-BPID tasarımında sırasıyla T1-BPID, ADT2-BPID kontrolörleri tasarlanmıştır. Benzetim çalışmasında önerilen öz-ayarlamalı yapı tip-1 ve aralık değerli tip-2 eşdeğerleriyle doğrusal olmayan bir konik tank sistemi üzerinde karşılaştırılmıştır. Önerilen öz ayarlamalı ADT2-BPID kontrolör yapısı ile hem ADT2-BPID kontrolör içyapısından gelen fazladan serbestlik derecesi hem de fonksiyon ayarlayıcı tabanlı öz ayarlama yöntemi sayesinde tip-1 bulanık ve tip-2 bulanık eşdeğerlerine kıyasla daha iyi sonuçlar vermiştir.

In this paper, an integration of fuzzy logic controllers (FLCs) with hybrid genetic algorithms (HGAs) is developed with a view to make the design process fully automatic, without requiring any human expert and numerical data. Our approach consists of two phases: first phase involves selection and definition of fuzzy control rules as well as adjustment of membership functions parameters, while the second phase performs an optimal selection of membership function types corresponding to fuzzy control rules. Learning both parts concurrently represents a way to improve the accuracy of the FLCs to minimize the errors. It has been argued that the performance of FLCs greatly depends on the parameters as well as types of membership functions. Thus, the aforementioned HGAs are a viable solution for designing an efficient adaptive FLCs system. To demonstrate the effectiveness of the proposed method for optimal design of the FLCs, the proposed approach is applied to a well-known benchmark controller design tasks, car and truck-and-trailer like robot system. Simulation results illustrates that proposed optimization approach can find optimal fuzzy rules and their corresponding membership functions types with a high rate of accuracy. The new HGAs optimized adaptive FLCs outperforms not only a passive control strategy but also human-designed FLCs, a neural coded controller with clustering and a neural-fuzzy control algorithm. [ABSTRACT FROM AUTHOR]

The integration of variable renewable energy sources such as wind and solar power has brought a significant challenges and uncertainty in power system operation, making it essential to take measures for advanced coordinated control. Moreover, by using the batteries of plug-in electrical vehicles (PEVs), the active and reactive power support functions can be provided at certain buses in a power system. Hence, parking lots with a large number of PEVs, referred to as SmartParks, can provide these functions at bulk level. Thus, by appropriate control of SmartParks, the active and reactive power flows in a power system can be regulated to achieve the functionalities of oscillation damping control, transmission line power flow regulation and bus voltage control. In this paper, in order to realize these three functionalities concurrently by using SmartParks, fuzzy-logic based controllers are proposed. Wide area measurements from a power system can be used to generate the active and reactive power output dispatch command signals for SmartParks. The parameters of the fuzzy logic controllers are heuristically determined to provide optimal system-wide performances for oscillation damping, transmission line power flow regulation and bus voltage control. Typical results are provided on a 12-bus power system with 400 MW wind farm implemented on a real-time simulation platform. [ABSTRACT FROM AUTHOR]

This paper presents a study on a sensorless decoupled P-Q control for a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) with intelligent controllers via Matlab/Simulink. In the absence of mechanical sensors, an algorithm based on modified phase locked loop (MPLL) can estimate the rotor speed and position. An experimental validation of the proposed speed algorithm in steady state was conducted and is presented. In addition, this study focuses on the implementation of fuzzy logic controllers combined with the conventional PI (Proportional-Integral) for the adjustment of active power, reactive power, rotor speed and voltage in dc-link. The combination of fuzzy logic and PI concerns the use of the first as gain tuner of the second one, bearing in mind the significance and simultaneously the difficulty of gain finding. The simulation results of this study are presented to clarify the enhanced effectiveness of the combination of fuzzy logic with PI controllers. Finally, a comparative study between three control strategies, namely, case 1 – ‘with mechanical sensors and PI controllers’, case 2 – ‘sensorless with PI controllers’ and case 3 – ‘sensorless with fuzzy logic controllers’ was conducted and is also presented. [ABSTRACT FROM AUTHOR]

The progression over wireless technologies paves the way for the emergence of wireless body area networks (WBAN) towards several motivating applications. Specifically, in terms of health concern applications, both the performance and reliability is regarded as the essential elements of WBANs. Many of the soft computational methodologies employed the manual modeling of fuzzy logic controllers (FLCs) by evolutionary algorithms in WBAN. This existing model encodes the entire control parameters of “FLCs” membership functions. This leads to the degradation of network performance by maximizing the latency. In order to rectify this issue, here we propose a hybrid firefly grey wolf optimizer (hybrid FGWO) approach for the optimal modeling of “FLC”. The major goal behind our proposed work relays on the optimal selection of control parameters from the “FLCs” with hybrid FGWO. The modeling of “FLCs” is carried out with CLFB (cross-layer fuzzy logic dependent back-off controller) mechanism to control the frequent access of channels. The efficiency of the “FLCs” model is enhanced by utilizing the coding technique known as unrestricted coding scheme. The performance of our hybrid FGWO approach is contrasted with three conventional “EAs”. Two major modeling goals are established whereas, the initial goal aims for the modeling of “FLCs” on particular configuration of network and the second goal aims on the modeling of “FLCs” over multiple network configurations. The “FLCs” modeled by means of our proposed hybrid FGWO approach exhibits its performance in terms of throughput, latency and packet delivery ratio with some of the challenging algorithms. [ABSTRACT FROM AUTHOR]

Direct torque control (DTC) is a simple and robust control algorithm for high performance industrial applications. The conventional DTC suffers from major drawbacks like high torque and flux ripples, variable switching frequency, current distortions during changes in switching sector and poor performance during low speed operations. The problem of the variable switching frequency and the reduction in torque and flux ripples can be achieved using space vector modulation based direct torque control (SVM-DTC). The dynamic performance of the SVMDTC is improved by replacing the constant gain PI speed, flux and torque controllers with fuzzy controllers. In this paper, both SVM-DTC with PI controller and fuzzy controllers are implemented using FPGA. The complete VHDL code for both DTC with PI and Fuzzy controllers is initially developed and simulated and then synthesized using Xilinx ISE 14.3 design tool. The complete developed code of the proposed algorithm is implemented on Spartan 6 XC6SLX25 board. The experimental results depict that an improved dynamic response of the induction motor is achieved in the SVM-DTC of the induction motor using fuzzy logic controllers. [ABSTRACT FROM AUTHOR]

Soft computing techniques including fuzzy logic have been successfully applied to wireless body area networks (WBANs). However, most of the existing research works rely on manual design of the fuzzy logic controller (FLC). To address this issue, in this paper, we propose an evolutionary approach to automate the design of FLCs for cross layer medium access control in WBANs. With the goal of improving network reliability while keeping the communication delay at a low level, we have particularly studied the usefulness of three coding schemes with different levels of flexibility during the evolutionary design process. The influence of fitness functions that measure the effectiveness of each possible FLC design has also been examined carefully in order to achieve a good balance between reliability and performance. Moreover, we have utilised surrogate models to improve the efficiency of the design process. In consideration of practical usefulness, we have further identified two main design targets. The first target is to design effective FLCs for a specific network configuration. The second target focuses on designing FLCs to function across a wide range of network settings. In order to examine the usefulness of our design approach, we have utilised two widely used evolutionary algorithms, i.e. particle swarm optimisation (PSO) and differential evolution (DE). The FLC designed by our approach is also shown to outperform some related algorithms as well as the IEEE 802.15.4 standard. [ABSTRACT FROM AUTHOR]

This paper describes the performance of a simplified dynamic controller with fuzzy logic controllers. The six degree-of-freedom simulation study focuses on the results with and without fuzzy logic controller. One area of interest is the performance of a simulated the cross coupling effect. The controller uses explicit models to produce the desired commands. In this paper the effect of the cross-coupling between channels on the overall performance of the flight system has been considered. Two fuzzy controllers have been added to the system to improve its performance. This paper presents the development and simulation of a modified system is presented using MatLab Simulink. Also it focuses on the use of fuzzy logic controller in model-based control of multiple-input, multiple-output systems. Here, we address the question of how the overall performance of the system is affected when both fuzzy logic controllers are applied at the same time. Simulation and experimental results of a flight system , as an illustrative example, are presented.

This paper presents a numerical strategy to reanalyze the modified frequency stability analysis of friction induced vibration problem. The stability analysis of a mechanical system relies on several coupling steps, namely a non-linear static analysis followed by linear and complex eigenvalue problems. We thus propose a numerical strategy to perform more rapidly multiple complex eigenvalue analyses. This strategy couples three methods namely, Fuzzy Logic Controllers to manage frictional contact problem, homotopy developments and projection techniques to reanalyze the projection matrices and component mode synthesis to calculate the modified eigensolutions. A numerical application is performed to highlight the efficiency of the strategy and a discussion is proposed in terms of precision and computational time. [ABSTRACT FROM AUTHOR]

Smart buildings lately have gained momentum due to its ability to drive, manage and control energy conservation measures. Fuzzy based controllers in Building Management Systems (BEMS) can use the latest and the most innovative control strategies in order to achieve a comfortable life style while savings energy and reducing greenhouse gas emissions. This system uses real life events as a point of control. Those real life events may include day-light usage (natural light), occupancy profile, passive cooling techniques and the usage of ambient atmosphere based on its ambient temperature and humidity. This paper develops a BMS using advanced fuzzy based controllers with integration of day light. This control strategy is based on quantifying the outside and the inside illuminance and allowing an add-on controller to perform a photometric calculation and comparison to decide rooms openings (windows). The paper also analyses energy savings and recommend suitable markets for this control strategy either during the design stage of a building, or after the building construction or retrofitting. [ABSTRACT FROM AUTHOR]

In this study, the general structure of interval type-2 fuzzy PID (IT2-FPID) controllers and a self-tuning mechanism for IT2-FPID controller is presented. In this context, we will present and examine the controller structures of the type-1 fuzzy PID (T1-FPID) and IT2-FPID controllers on a generic a symmetrical 3x3 rule base. Then, an online self-tuning mechanism for IT2-FPID controllers is presented. The presented selftuning mechanism, which was firstly presented for T1-FPID, controllers, tunes the scaling factors of IT2-FPID with respect to the current error value of the control system. A systematic design approach has been also presented for the self-tuning IT2-FPID structure. The performance of the T1-FPID, IT2-FPID and Self-Tuning IT2-FPID structures has been investigated on a simulation study conducted on a nonlinear tank system. The results have shown that, since the proposed approach has more extra degree of freedom provided by its interval type-2 fuzzy sets and self-tuning mechanism, the self-tuning IT2FPID resulted with a superior control performance in comparison with its type-1 and interval type-2 counterparts. [ABSTRACT FROM AUTHOR]

The possibilities and prospects of using fuzzy logic in control systems for electro-technological equipment are analyzed based on a comparative study of standard temperature controllers of resistance furnaces and fuzzy logic controllers. It is shown that it is effective to use fuzzy logic control systems for different kinds of technological equipment. The development of fuzzy logic control systems and characteristics of their application are analyzed. The basic structures of systems constructed using fuzzy logic control are considered, is the way in which they are classified. The simulation models of temperature controllers for resistance furnaces are studied using the Matlab/Simulink software including the Fuzzy Logic Toolbox application. It is shown that a use of fuzzy controllers for simple deterministic control systems like temperature controllers of resistance furnaces does not lead to any substantial improvement in accuracy and control quality, but needs a sophisticated algorithm for control and response speed of the control system. It is assumed that a use of fuzzy logic controllers to control more sophisticated equipment characterized by the multifactor processes with a high degree of uncertainty and difficulties in determination of output parameters will allow one to obtain better results than do conventional systems. [ABSTRACT FROM AUTHOR]

The future wireless communication systems require an intelligent and service adaptive network selection to perform vertical handover (HO) in heterogeneous environments. Since multiple parameters are considered to select the best suitable network, reducing the complexity in multi‐attribute decision making algorithm becomes apparent. In the proposed scheme, fuzzy logic controllers are used to estimate the necessity of HO and to determine the user satisfaction degree based on the critical parameters, such as mobile terminal speed, network load and service cost. Fuzzy analytic hierarchy process is used to calculate the weights of each attribute according to the traffic. Since it handles the uncertainty and vagueness in mapping the user preference to the priority scales, consistency level of network selection can be improved. Quality of service factor is calculated for each network by processing the weighted decision matrix using principal component analysis. Simulation results show that proposed scheme can ensure the best network selection by providing trade‐off between complexity and consistency. [ABSTRACT FROM AUTHOR]

In recent years, Multi-Objective Evolutionary Algorithms ( moeas) that consider diversity as an objective have been used to tackle single-objective optimisation problems. The ability to deal with premature convergence has been greatly improved with these schemes. However, they usually increase the number of free parameters that need to be tuned. To improve results and avoid the tedious hand-tuning of algorithms, the use of automated parameter control approaches that are able to adapt parameter values during the course of an evolutionary run are becoming more common in the field of Evolutionary Computation ( ec). This research focuses on the application of parameter control approaches to diversity-based moeas. Two external parameter control methods are investigated; a novel method based on Fuzzy Logic and a recently proposed Hyper-heuristic. These are compared to an internal control method that uses self-adaptation. An extensive comparison of the three methods is carried out using a set of single-objective benchmark problems of diverse complexity. Analyses include comparisons to a wide range of schemes with fixed parameters and to a single-objective approach. The results show that the fuzzy logic and hyper-heuristic methods are able to find similar or better solutions than the fixed parameter methods for a significant number of problems, with considerable savings in computational resources and time, whereas the self-adaptive strategy provides little benefit. Finally, we also demonstrate that the controlled diversity-based moea outperforms the single-objective scheme in most cases, thus showing the benefits of solving single-objective problems through diversity-based multi-objective schemes. [ABSTRACT FROM AUTHOR]

In this paper, we present the automatic design methods with rule base size reduction for fuzzy logic controllers (FLCs) through real and binary coded coupled genetic algorithms (GAs). The adaptive schema is divided into two phases: the first phase is concerned with optimizing the FLCs membership functions and second phase called rule learning and reducing phase which automatically generates the fuzzy rules as well as determines the minimum number of rules required for building the fuzzy models. In the second phase, the redundant rules are removed by setting their all consequent weight factor to zero and merging the conflicting rules during the learning process. The first and second phases are carried out by the real and binary coded coupled GAs, respectively. Optimizing the MFs with learning and reducing rule base concurrently represents a way to maximize the performance of a FLC. The control algorithm is successfully tested for intelligent control of two degrees of freedom inverted pendulum. Finally, the simulation studies exhibits the better or competitive performance of the proposed method when compared with the existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to dynamically manage the traffic lights cycles and phases in an isolated intersection. The target of the work is a system that, comparing with previous solutions, offers improved performance, is flexible and can be implemented on off-the-shelf components. The challenge here is to find an effective design that achieves the target while avoiding complex and computationally expensive solutions, which would not be appropriate for the problem at hand and would impair the practical applicability of the approach in real scenarios. The proposed solution is a traffic lights dynamic control system that combines an IEEE 802.15.4 Wireless Sensor Network (WSN) for real-time traffic monitoring with multiple fuzzy logic controllers, one for each phase, that work in parallel. Each fuzzy controller addresses vehicles turning movements and dynamically manages both the phase and the green time of traffic lights. The proposed system combines the advantages of the WSN, such as easy deployment and maintenance, flexibility, low cost, noninvasiveness, and scalability, with the benefits of using four parallel fuzzy controllers, i.e., better performance, fault-tolerance, and support for phase-specific management. Simulation results show that the proposed system outperforms other solutions in the literature, significantly reducing the vehicles waiting times. A proof-of-concept implementation on an off-the-shelf device proves that the proposed controller does not require powerful hardware and can be easily implemented on a low-cost device, thus paving the way for extensive usage in practice. [ABSTRACT FROM AUTHOR]

This paper describes the design of a high speed min/max architecture based on a new current comparator. The main advantage of the proposed circuit which employs a novel preamplifier-latch comparator is the higher operating frequency feature in comparison with previous works. Because the comparator can work in voltage mode, the min/max structure can be redesigned either in voltage or current mode. The designed comparator is refreshed without any external clock. Therefore, it does not degrade the speed performance of proposed min/max structure. These features along with low power consumption qualify the proposed architecture to be widely used in high speed fuzzy logic controllers (FLCs). Post-layout simulation results confirm 3.4 GS/s comparison rate with 9-bit resolution for a 0.9 V peak-to-peak input signal range for the comparator and 800 MHz operating frequency for min/max circuit. The power consumption of whole structure is 912 μW from a 1.8 V power supply using TSMC 0.18-μm CMOS technology. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to the stable design of fuzzy logic control systems that deal with a general class of chaotic processes. The stable design is carried out on the basis of a stability analysis theorem, which employs Lyapunov's direct method and the separate stability analysis of each rule in the fuzzy logic controller (FLC). The stability analysis theorem offers sufficient conditions for the stability of a general class of chaotic processes controlled by Takagi-Sugeno-Kang FLCs. The approach suggested in this paper is advantageous because inserting a new rule requires the fulfillment of only one of the conditions of the stability analysis theorem. Two case studies concerning the fuzzy logic control of representative chaotic systems that belong to the general class of chaotic systems are included in order to illustrate our stable design approach. A set of simulation results is given to validate the theoretical results. [ABSTRACT FROM AUTHOR]