Fuzzy Logic Optimization Research Articles

Fuzzy Logic Optimization of Composite Brake Control Strategy for Load-Isolated Electric Bus

To improve the energy recovery rate and increase the driving range of load-isolated electric buses, a composite brake control strategy based on fuzzy logic optimization is proposed. Considering the influence of the battery SOC value and the braking intensity on the braking ratio, a fuzzy controller is designed for small and medium braking strengths to optimize the control strategy. The vehicle simulation model of the load-isolated electric bus was built with the AVL CRUISE software platform. The AVL CRUISE-Simulink cosimulation was carried out under the original control strategy and the optimized control strategy. The simulation results show that, under the premise of ensuring the stability of braking, the driving range of the vehicle with the optimized control strategy is increased by 7.74% and the energy recovery rate is increased by 11.05%.

An Effective Secure Mechanism using Fuzzy Hybrid DCR Technique for MANET

Due to various Denial-of-Service (DoS) attacks like blackhole and grayhole attacks, Mobile Ad-Hoc Networks (MANET) performance is degraded rapidly. These attacks have been detected and prevented separately by different techniques. In earlier research, hybrid black/grayhole attack detection was proposed in which blackhole and grayhole attacks were detected and prevented simultaneously based on the detection threshold. However, some malicious nodes are still present in the network by faking the threshold value and forwarding the fake message to the other nodes. Therefore, the hybrid black/grayhole attack detection is enhanced by integrating network metric measurements. In this paper, the Data-to-Control packet Ratio (DCR) is measured for removing malicious nodes from the network and also avoiding the false detection. In addition, fuzzy-based mobility and traffic measurement is integrated with a hybrid DCR detection technique for removing malicious node links. Moreover, the optimal path for packet transmission is selected by measuring the queue delay based on fuzzy logic optimization. Finally, the efficiency of the proposed hybrid blackhole/grayhole attack detection technique is illustrated through the simulation results based on the throughput, packet drop rate, packet delivery ratio and routing overhead.

Методика нечіткого оцінювання для систем підтримки прийняття проектних рішень на етапах створення зразків озброєння та військової техніки

У статті приведено розроблену методику нечіткого оцінювання для систем підтримки прийняття рішень в процесі створення зразків озброєння та військової техніки. Практична цінність методики полягає в тому, що на її основі було проведено розробку програмних засобів підтримки вибору рішень. Для досягнення зазначеною мети були використані основні положення методів штучного інтелекту, складних технічних систем, нечіткої логіки та багатопараметричної та багатокритеріальної оптимізації.

Assessment of the level of requirements to key competencies of enterprises under conditions of digital economy

The main subject of the research is the key competencies of innovation-active enterprises in the digital economy. Approach to the identification of core competencies of the enterprises on the basis of the inverse engineering of the requirements to competencies satisfying valuable characteristics of a product is formulated. It is proposed to consider the network enterprise as an intermediate link upon transition from the creation of the traditional enterprise to virtual. Decomposition of the value chain of the product into activities corresponding to the core competencies in the context of the network enterprise participants is executed. The technique of assessment of requirements to core competencies of the enterprises on the basis of comparison to the value offer on the creation of an innovative product is developed. Criteria of optimization of the network enterprise structure on the basis of estimates of the level of requirements to core competencies are developed. The distinctiveness of the study consists in the use of expert assessment methods, fuzzy logic, multisets, and multi-criteria optimization during its conducting. The practical implementation of the research results presented in this article will improve the efficiency of innovatively active enterprises.

Optimization of fuzzy logic (Takagi-Sugeno) blade pitch angle controller in wind turbines by genetic algorithm

Wind turbines have become popular with the recent interest in renewable energy sources. At wind speeds above nominal wind speeds, the blade pitch angle is controlled to ensure that the wind turbines operate safely and the output power is stable. Since the wind turbines are nonlinear systems, the blade pitch angle controller must also be suitable for such cases. In this respect, the fuzzy controller can accommodate such nonlinearities, making it a suitable candidate for wind turbine blade controls. In this study, a fuzzy controller designed to control the wind turbine blades is optimized with a genetic algorithm that is improved. New features are added to improve Advanced Intelligent Genetic Algorithm’s (AIGA’s) performance. One of these is the addition of acceptable error concept (AEC). The conversion from binary to decimal and from decimal to binary are performed based on the amount of this acceptable error. Although an approximate value can be obtained, conversion from decimal to binary may not be accurately performed especially for the digits following the decimal. These inaccuracies may lead to small errors especially, during back conversion from binary back into decimal in IGA. This is removed by AEC implemented in AIGA. Furthermore, maximum number of crossover points in AIGA is determined as a function of the length of chromosome. This implementation improved algorithm. Simulation results show that optimization makes the output power even better.

An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment

The Mamdani fuzzy inference method is one of the most important fuzzy logic control (FLC) techniques and has several applications in different fields. Despite its applications, the Mamdani fuzzy inference method has some core issues which still require solutions. The most critical issue is the selection of accurate shape and boundaries of membership functions (MFs) in the universe of discourse. In this work, we introduced a methodology called learning to control (LtC) to resolve the problem. The proposed methodology consisted of two main modules, namely, a control algorithm (CA) module and a learning algorithm (LA) module. In the CA module, the Mamdani FLC method has been used, whereas, in the LA module, we have used the artificial neural network (ANN) algorithm. Inputs into the ANN were the error difference between environmental temperature and the required temperature. The output of the ANN was the MF set to the FLC. Inputs into the fuzzy logic controller (FLC) were the error difference between environmental temperature and required temperature (D), and the output was the required power for the fan actuator. The purpose of the ANN was to tune the MFs of the FLC to improve its efficiency. The proposed learning-to-control method along with the conventional fuzzy logic controller method was applied to the data to evaluate the model’s performance. The results indicate that the proposed model’s performance is far better than that of conventional fuzzy logic techniques.

Current correction and fuzzy logic optimizations of Perturb & Observe MPPT technique in photovoltaic panel

This paper presents a two-way optimization of the Perturb & Observe (P&O) maximum power point tracking (MPPT) technique using current correction and fuzzy logic techniques. In fact, photovoltaic (PV) energy has become more and more coveted today. In the future, it will become a necessity. To ensure its optimization, maximum operating point tracking method is considered as a technological key in PV systems. One of the most used MPPT methods is the P&O technique. In this paper, we will focus on optimizing this method based on two techniques. A first attempt has been made to estimate a current correction of the P&O algorithm in case of illumination variation. Then, fuzzy logic optimization attempt had been highlighted to improve power loss. It is shown that both proposed techniques are very effective and allow considerable improvement of accuracy and are less affected by sudden variation of climatic parameters. The proposed approaches are tested via Matlab software and compared the classical P&O algorithm. Through applications, we could conclude that the two optimized proposed methods offer a remarkable improvement concerning power losses.

Particle Swarm Optimization based Fuzzy Logic MPPT Inverter Controller for Grid Connected Wind Turbine

The wind energy sources as an alternative to conventional sources to generate power has been increasingly popular due to abundant in nature and pollution free. The wind energy sources are undepletable, however, they are not able to generate as much electricity due to their intermittent nature. Therefore, a wind energy conversion system (WECS) with accurate maximum power point tracking (MPPT) inverter controller is essential in order to improve the wind energy capturing capability. This paper aims to develop an optimal Fuzzy logic MPPT inverter controller for grid connected wind turbine so that the energy capturing efficiency of the system can be maximized. In this research work, the MPPT inverter controller is designed on the basis of fuzzy logic control and heuristic particle swarm optimization (PSO) algorithm. The performance of WECS are demonstrated with the generated output voltage, current and power waveforms, the rectifier current waveform, DC link voltage waveform, and the generator speed. According to the results obtained for both normal Fuzzy logic MPPT control and optimized Fuzzy logic MPPT control WECS, the performance of PSO optimized Fuzzy logic MPPT inverter controller has better maximum power point performance and efficiency in tracking wind energy in terms of much smooth, less distortion and fewer fluctuation outcomes at the various step wind speed conditions. The proposed optimal Fuzzy logic MPPT inverter controller has a good potentiality for WECS in sustainable grid application.

Optimized Fuzzy Logic Controller for Responsive Charging of Electric Vehicles

Abstract This paper contributes to the theme of intelligent integration of energy storage and the control of prosumer resources. The paper illustrates the use of a zero-order Sugeno fuzzy model to perform bidirectional real power control from an electric vehicle (EV), i.e., vehicle to grid (V2G) and grid to vehicle (G2V). The paper proposes an initial design of the fuzzy logic controller (FLC) following which the FLC is optimized using genetic algorithm (GA) for better performance under varying charging speed requirements of the user and better efficiency. A perturb and observe (P&O) algorithm based reactive power control is also proposed for voltage support through these EV chargers. The designed FLC can not only respond to grid conditions based on the time of use (TOU) but also to users charging speed requirements. Also, the initial design of an FLC may not suit a particular user’s requirement but can be optimized to meet the requirements.

OPTIMASI PENEMPATAN RECLOSER UNTUK MEMPERBAIKI KEANDALAN PADA PENYULANG LEMBONGAN MENGGUNAKAN METODE PARTICLE SWARM OPTIMIZATION-FUZZY

In Bali, especially at Tiga Nusa, the need for electricity is increasing along with the development of the construction of resorts and hotels. In this study, a sample of cases was taken in 2014 regarding the Lembongan feeder network system which was still installed with several LBS (Load Break Switch) which used to localize interference. Therefore, to anticipate interference and improve the reliability of feeders, one of many ways is to add an automatic back cover (recloser). This study aims to determine the optimal recloser location in the Lembongan feeder by using a combined method of fuzzy logic and particle swarm optimization with the help of MATLAB software. The results of this study obtained the best location of the recloser in group 5, namely the area between LBS SD 3, LBS Celagi Empak, and LBS Lembongan by adding one recloser. The SAIFI value is 1,9471 times / customer / year and the SAIDI value is 1,4596 hours / customer / year where the world class service parameter standard for the SAIFI reliability index is 3 times / customer / year and the SAIDI reliability index is 2,5 hours / customer / year.

Energy efficient clustering protocol for WSNs based on bio-inspired ICHB algorithm and fuzzy logic system

This paper explores the capabilities of Intelligent cluster head selection based on bacterial foraging optimization (ICHB) algorithm and fuzzy logic system (FLS) for searching better cluster head (CH) nodes without using any randomized algorithms in the network. ICHB-HEED is one of the recent clustering based protocol in the field of wireless sensor networks (WSNs). In this paper, the clustering procedures of ICHB-HEED is further improved by applying the combination of ICHB algorithm and FLS system based on residual energy, node density and distance to base station (BS) parameters which results in ICHB-Fuzzy Logic based HEED (ICFL-HEED) protocol. It alleviates the formation of holes and hot-spots in the network, delays the death of sensor nodes (SNs), minimizes the energy consumption of SNs, forms even-sized clusters and extends the network lifetime competently. The proposed ICFL-HEED protocol is compared with existing HEED & ICHB-HEED protocols and observed that the performance of ICFL-HEED is far better than these protocols.

Tracking-Error Fuzzy-Based Control for Nonholonomic Wheeled Robots

In this paper, the trajectory to be tracked by a differentially driven wheeled mobile robot (DDWMR) is controlled. The considered DDWMR has a chassis with two active wheels and a front idle wheel. After introducing the kinematic model of the robot, the robot trajectory tracking problem using fuzzy and optimal fuzzy logic methods will be analyzed. Also, the same mission will be conducted using model predictive control (MPC) method. Minimizing the path tracking error is the objective of the controllers design. Moreover, the velocity and acceleration constraints are included in the proposed controllers design procedure to prevent the DDWMR from slipping and path curvature deviation. Finally, tracking error results for fuzzy, optimal fuzzy and model predictive controllers are compared. The tracking error analysis of the obtained simulation results, in MATLAB software, reveals the better performance of the designed fuzzy controller (FC) over the MPC, and the better performance of the designed optimal fuzzy controller over the FC.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

The Control of an Active Seat Suspension Using an Optimised Fuzzy Logic Controller, Based on Preview Information from a Full Vehicle Model

The use of suspension preview information obtained from a quarter vehicle model (QvM) to control an active seat has been shown by the authors to be very promising, in terms of improved ride comfort. However, in reality, a road vehicle will be subjected to disturbances from all four wheels, and therefore the concept of preview enhanced control should be applied to a full vehicle model. In this paper, different preview scenarios are examined, in which suspension data is taken from all or limited axles. Accordingly, three control strategies are hypothesized—namely, front-left suspension (FLS), front axle (FA), and four wheel (4W). The former utilises suspension displacement and velocity preview information from the vehicle suspension nearest to the driver’s seat. The FA uses similar preview information, but from both the front-left and front-right suspensions. The 4W controller employs similar preview information from all of the vehicle suspensions. To cope with friction non-linearities, as well as constraints on the active actuator displacement and force capabilities, three optimal fuzzy logic controllers (FLCs) are developed. The structure of each FLC, including membership functions, scaling factors, and rule base, was sequentially optimised based on improving the seat effective amplitude transmissibility (SEAT) factor in the vertical direction, using the particle swarming optimisation (PSO) algorithm. These strategies were evaluated in simulation according to ISO 2631-1, using different road disturbances at a range of vehicle forward speeds. The results show that the proposed controllers are very effective in attenuating the vertical acceleration at the driver’s seat, when compared with a passive system. The controller that utilised suspension preview information from all four corners of the car provided the best seat isolation performance, independent of vehicle speed. Finally, to reduce the implementation cost of the “four suspension” controller, a practical alternative is developed that requires less measured preview information.

Fuzzy Logic Optimization of a Centralized Energy Management Strategy for a Hybrid PV/PEMFC System Feeding a Water Pumping Station

This paper shows a developed fuzzy logic supervisor used to manage energy flow in a hybrid renewable energy system composed of a photovoltaic generator and a proton exchange membrane fuel cell. The hybrid installation is used to feed a pumping system used to store water in a tank in order to use it whenever needed. This system is widely used especially for agriculture purposes but batteries are used to maintain continuous water pumping when the solar irradiance quantity is not enough for the photovoltaic generator to provide needed power for the pumping system. The proposed solution guarantees continuous power production and water availability with the least possible costs thanks to the developed management strategy. The obtained results prove the efficiency of the studied technique in reducing fuel consumption by minimizing its utilization as maximum as possible and that continuous water pumping process is guaranteed.

Fuzzy Logic Optimization for Low Sulphur and Phosphorus in Submerged Arc Weldments

Fuzzy Logic Optimization for Low Sulphur and Phosphorus in Submerged Arc Weldments

Design of Solving Similarity Recognition for Cloth Products Based on Fuzzy Logic and Particle Swarm Optimization Algorithm

Design of Solving Similarity Recognition for Cloth Products Based on Fuzzy Logic and Particle Swarm Optimization Algorithm

Power-Frequency Balance in Multi-generation System Using Optimized Fuzzy Logic Controller

This paper emphasizes the development of control strategy for inter-area oscillation suppression for a unified two-area hydro–thermal deregulated power system. A proportional derivative-type fuzzy logic controller with integral (PDFLC+I) controller was proposed for automatic generation control. Further comparisons among conventional integral controller, proportional integral derivative controller, and PDFLC+I are carried out, where the PDFLC+I controller is optimized by four different optimization techniques namely, algorithm, ant colony optimization, classical particle swarm optimization, and adaptive particle swarm optimization. In PDFLC+I controller optimization, scaling parameters of controllers are optimized. A comparative study shows that the proposed PDFLC+I controller has a better dynamic response following a step load change for the combination of PoolCo and bilateral contract-type transaction in deregulated environment. Proposed controller performance has also been examined for ±30% variation in system parameters. Non-linearity in the form of governor dead band is taken into account during simulation.

Practical application of collocation method in power flow study of South Australia grid

In this paper stochastic collocation method is proposed to solve probabilistic power flow (PPF) model of South Australia (SA). And this model is based upon historical acquisition of power system data of SA. In SA, numbers of wind farms are installed and the variability of wind speed brings more uncertainties into the power system. The traditional deterministic power flow (DPF) computation does not take into account the probabilistic nature of power system uncertainties, so PPF computation is imperative. However, as a commonly used PPF simulation method, Monte Carlo simulation (MCS) has a very high computation cost. Hence, in this paper sparse grid interpolation (SGI) is presented to accomplish PPF analysis with striking high computation efficiency. Meanwhile, instead of using theoretical wind power generation model, probabilistic collocation method (PCM) is used to construct realistic relationship between wind speed and wind power. In addition, fuzzy logic optimization is applied to PCM to improve the accuracy of the model output. The paper concludes with presentation of an aggregated DC power flow model of SA to compare the computation efficiency of the SGI and MC.