Fuzzy Logic Control Algorithm Research Articles

A robust adaptive-fuzzy-proportional-derivative controller for a rehabilitation lower limb exoskeleton

Achieving high performance controller for multi-joints actuators on rehabilitation lower limb exoskeleton (RLLE) is difficult due to its non-linear characteristics. The controller performance with less tracking error is a key challenge in their controller. Therefore, this paper presents a new particle swarm optimization based initialization of model reference adaptive for fuzzy logic proportional derivative controller (Adaptive-FLC-PD), used in RLLE for passive mode rehabilitation exercise. The RLLE modelling, which integrates a lower-limb exoskeleton coupled with a direct current motor as joint actuator and a patient leg model, was simulated in MATLAB. The lower-limb exoskeleton motion is realised via a trajectory tracking method that imitates a therapist-administered manual activity during passively rehabilitation exercise. An Adaptive-FLC-PD was designed to control the direct current motor and drive the hip and the knee of the lower-limb exoskeleton. The stability analysis of Adaptive-FLC-PD has been shown by the applied Lyapunov function. The performance of the Adaptive-FLC-PD was compared with the fuzzy logic controller (FLC) and FLC-proportional derivative (FLC-PD) algorithms. The numerical analysis ascertained the performance of the Adaptive-FLC-PD in designing, tuning and simulating control system of RLLE.

An adaptive fuzzy logic control of green tea fixation process based on image processing technology

In postharvest processing of green tea, fixing is the first and the most critical step for forming green tea's specific appearance and fragrance. In this study, green tea sample was firstly fixed at constant pot temperatures. The image information was captured to reflect colour changes. By observing the disappearance of the enzymatic activity, the fixation process was divided into the first and the last stage, where different concerns were emphasised on. After fixation, chemical components including enzymatic activity, tea polyphenols, amino acids and chlorophyll were analysed. The MLP, Grid-Search-SVR and GA-SVR models were constructed based on image information to predict chemical compositions. Results demonstrated GA-SVR could achieve superior performance than Grid-Search-SVR and MLP (1.9742>0.9167>0.7797 for average MSE, 0.3829< 0.5969<0.6557 for average R-Square, and 15.7132>8.8603> 8.5081 for average MAPE at first fixation stage; 0.3343>0.2552>0.2460 for MSE, 0.7393<0.8317<0.8658 for R-Square, and 5.2898 > 3.9612>3.8259 for MAPE at last fixation stage). An adaptive fuzzy logic control algorithm was designed to adjust the pot temperature during the fixation process. The pre-processed image information was fed into the controller to control the pot temperature continuously. With the intelligently controlled high temperature in the first stage, the enzymatic reaction could be suppressed and more polyphenols and chlorophyll were retained. In the last fixation stage, the temperature was automatically adjusted to low values to minimise oxidation, and more amino acids were formed. Consequently, the final product quality was significantly improved in terms of appearance, liquor colour, fragrance, taste, and residue colour, with the adaptive fuzzy logic controller.

Dynamic routing, modulation, and spectrum assignment based on fuzzy logic control in elastic optical networks.

Considering the constantly changing network resources and randomly generated spectrum fragmentation problem, a dynamic routing, modulation, and spectrum assignment based on the fuzzy logic control (RMSA-FLC) algorithm is proposed in this paper. A fuzzy logic control (FLC) system based on the degree of fragmentation optimization and the degree of link selection is constructed. The path-control weight (PW) is achieved as the output of the FLC system, and the path and spectrum allocation scheme with the maximum PW is selected for the immediate reservation requests. The simulation results show that the proposed RMSA-FLC algorithm can effectively reduce the blocking probability and improve the spectrum resource utilization in a dynamic elastic optical network.

Simulation analysis of distributed micro-energy clusters participating in voltage regulation and peak regulation

The influence of distributing micro-energy access on power grid voltage and peak is analyzed. For the over-voltage and under-voltage problems caused by the distributed micro-power access distribution network, which proposes a reactive control algorithm based on fuzzy logic. In this algorithm, the fuzzy logic control algorithm is used to control the reactive power,which is based on the sensitivity matrix. For the peak shaving problem caused by distributed power access to the distribution network, a strategy of storing photovoltaic power generation energy in an air source heat pump is proposed. By setting air source heat pumps with different parameters, a multi-objective scheduling model of control strategy for photovoltaic power generation energy consumption and the air source heat pump are proposed. And the improved non-dominated sorting genetic algorithm NSGA-II (Non-dominated Sorting Genetic Algorithms) is used to optimize the model. Through the introduction of fuzzy logic control to the research on the node voltage, the no-voltage control and the change of the node voltage of the introduction of fuzzy logic control are analyzed. The voltage fluctuation range of the introduction of fuzzy control is between 0.9–1.05, which is 0.1v less than the no-voltage control. The introduction of fuzzy logic control can solve the problem of voltage over-limit. In the process of peak shaving simulation analysis, using the improved algorithm NSGA-II, through the joint operation of wind power, photovoltaic power generation, air source heat pump and so on. The output power of the net load is 500 MW lower than the output power of the load, which is realized through the coordinated operation of various energy sources. The net load output power is relatively stable. From the analysis of the simulation results, the proposed algorithm can solve the voltage and peak problems, which caused by the participation of distributed resources in the micro-energy grid. And it realizes the balance of local energy production and energy load.

Reliable Control Applications with Wireless Communication Technologies: Application to Robotic Systems.

The nature of wireless propagation may reduce the QoS of the applications, such that some packages can be delayed or lost. For this reason, the design of wireless control applications must be faced in a holistic way to avoid degrading the performance of the control algorithms. This paper is aimed at improving the reliability of wireless control applications in the event of communication degradation or temporary loss at the wireless links. Two controller levels are used: sophisticated algorithms providing better performance are executed in a central node, whereas local independent controllers, implemented as back-up controllers, are executed next to the process in case of QoS degradation. This work presents a reliable strategy for switching between central and local controllers avoiding that plants may become uncontrolled. For validation purposes, the presented approach was used to control a planar robot. A Fuzzy Logic control algorithm was implemented as a main controller at a high performance computing platform. A back-up controller was implemented on an edge device. This approach avoids the robot becoming uncontrolled in case of communication failure. Although a planar robot was chosen in this work, the presented approach may be extended to other processes. XBee 900 MHz communication technology was selected for control tasks, leaving the 2.4 GHz band for integration with cloud services. Several experiments are presented to analyze the behavior of the control application under different circumstances. The results proved that our approach allows the use of wireless communications, even in critical control applications.

Fuzzy Logic Control Automation for Traffic Signalization System

Automation systems are important structure that provides needs in many sectors such as manufacturing, construction, electricity, recycling, energy, electronics and informatics. Recently the automation systems have become larger and more complex. Situations that are completely specific and certain can be easy to resolve. However, the solution of problems that contain uncertainty and can’t be fully defined is relatively difficult. Therefore, conventional logic is not sufficient because of its strictly defined boundaries. Fuzzy Logic Control is provided by executing the control algorithm in many areas at industrial automation. In this paper, we developed the Fuzzy Logic Control algorithm for a low-level PLC without Fuzzy Logic Control Module. Then, we realized PLC based fuzzy logic control for intersection traffic signaling control.

Control of Vapor Pressure Deficit (VPD) on Black Sesame Seed (Sesamum indicum L.) Sprout Production in a Micro-Greenhouse Using Intelligent Control

The demand for fresh and healthy food has been increasing, and different options for growing sprouts have been presented to solve this, such as traditional techniques and cultivation under controlled conditions. However, sprout farming has not explored all the tools available to produce these foods under controlled conditions. This study presents an alternative to produce sesame seed sprouts in a micro-greenhouse applying intelligent control algorithms for vapor pressure deficit. There was an improvement of 56% in the germination percentage, 2.59 in the germination index, 9.7% in the production of proteins, 1.1% in ash and an increase of 77.03 mm in the sprouts’ length collected in the micro-greenhouse in comparison with the traditional technique. This was achieved by maintaining a mean error for soil moisture at 87% and 0.93 kPa for vapor pressure deficit by applying proportional–integral–derivative, fuzzy logic and neural network control algorithms in the micro-greenhouse. The study shows that the nutritional content, the measured germination parameters and the size are improved in sesame sprout production by applying intelligent control algorithms for vapor pressure deficit in a micro-greenhouse.

Experimental and numerical study of energy and thermal management system for a hydrogen fuel cell-battery hybrid electric vehicle

The aims of this study were fourfold: firstly, develop a fuzzy logic control algorithm for enhancing overall energy efficiency of the fuel cell hybrid electric vehicle, secondly, balance the energy consumptions of the battery and fuel cell with aid of gradient descent optimization method, thirdly, use the waste heat of hydrogen vessels by controlling the fuel flow, and finally, operate two energy sources at cold and hot ambient temperatures safely and efficiently. In the current study, a fuzzy logic controller is used to keep of operate the hydrogen fuel cell and battery pack at their optimum temperatures. A detailed mathematical model is established, which includes electric vehicle dynamics, thermal behavior of battery, hydrogen fuel cell and vessel, and the efficiency model of electric motor. Furthermore, numerical simulations related to vessel temperature changes and energy consumption of the hybrid electric vehicle under actual road conditions are validated by experiments. The results demonstrate that total energy consumption including the propulsion system, thermal management system, and fuel supply system is decreased by 9,1% at 18 °C thanks to the fuzzy logic control algorithm. Besides, energy efficiency is increased around 7% and 11% at −10 °C and 35 °C ambient temperatures, respectively, which are extreme scenarios for electric vehicles. Moreover, the developed algorithm provides a successful thermal management strategy regarding waste heat recovery from hydrogen vessels, the temperature of which is controlled by the fuel supply system.

Intelligent controller based optimum load shedding for photovoltaic and wind energy integrated power systems

ABSTRACT Power generation by renewable energy sources plays a major role to meet the consumer demand. The proposed renewable energy sources, Photovoltaic (PV) and wind energy sources are integrated with power grid using Voltage Source Converter (VSC). The renewable energy sources have been simulated and evaluated under various operating conditions using MATLAB environment. The main objective of this research paper is to investigate the renewable energy system and its effectiveness in load shedding using fuzzy logic control algorithm. In this study, three type of load priority has been labelled, high priority, medium priority and low priority power load. The fuzzy controller will select the priority of the load, based on the availability of PV and wind power supply. The controller performance analysis under various operating conditions are evaluated by IEEE 1547 standards for proving the effectiveness of proposed control algorithm.

Dissimilarity Clustering Algorithm for Designing the PID-like Fuzzy Controllers

Fuzzy logic controller is one of the most prominent research fields to improve efficiency for process industries, which usually stick to the conventional proportional-integral-derivative (PID) control. The paper proposes an improved version of the three-term PID-like fuzzy logic controller by removing the necessity of having user-defined parameters in place for the algorithm to work. The resulting non-parametric three-term dissimilarity-based clustering fuzzy logic controller algorithm was shown to be very efficient and fast. The performance study was conducted by simulation on armature-controlled and field-controller DC motors, for linguistic type and Takagi-Sugeno-Kang (TSK) models. Comparison of the created algorithm with fuzzy c-means algorithm resulted in improved accuracy, increased speed and enhanced robustness, with an especially high increase for the TSK type model.

A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning

Due to the limitation of mobile robots’ understanding of the environment in local path planning tasks, the problems of local deadlock and path redundancy during planning exist in unknown and complex environments. In this paper, a novel algorithm based on the combination of a long short-term memory (LSTM) neural network, fuzzy logic control, and reinforcement learning is proposed, and uses the advantages of each algorithm to overcome the other’s shortcomings. First, a neural network model including LSTM units is designed for local path planning. Second, a low-dimensional input fuzzy logic control (FL) algorithm is used to collect training data, and a network model (LSTM_FT) is pretrained by transferring the learned method to learn the basic ability. Then, reinforcement learning is combined to learn new rules from the environments autonomously to better suit different scenarios. Finally, the fusion algorithm LSTM_FTR is simulated in static and dynamic environments, and compared to FL and LSTM_FT algorithms, respectively. Numerical simulations show that, compared to FL, LSTM_FTR can significantly improve decision-making efficiency, improve the success rate of path planning, and optimize the path length. Compared to the LSTM_FT, LSTM_FTR can improve the success rate and learn new rules.

Implementation of reduced induction machine fuzzy logic control based on dSPACE-1104 R&amp;D controller board

In this paper, we present our contribution in Induction Machine control field. The control we designed is based on fuzzy logic theory, this choice is motivated by the fact that this technique of control is suitable for the control of systems characterized by its parameters uncertainties and variations. The proposed control is optimized by using a genetic algorithm for fuzzy logic controller (FLC) gains tuning and by a good choice of calculation techniques used in FLC. Three versions of IM fuzzy logic control were validated by simulation. After that in order to be able to experimentally implement this control on dSPACE-1104, we proposed an optimized and reduced structure of the control. The experimental results proof the effectiveness and the satisfied performance of the proposed IM fuzzy control.

Research on equalization strategy of lithium-ion batteries based on fuzzy logic control

A new equalization topology using Cuk equalizer combined with double-layer selector switch is proposed in this paper. The feature of this topology is that the energy transfer among arbitrary batteries can be realized quickly by controlling switches, and the complexity of energy transmission path can be reduced. In order to equalize the battery in the whole State of Charge range and reduce the influence of inaccurate equalization caused by a single equalization variable, according to the characteristics of the Open Circuit Voltage - State of Charge curve, the piecewise equalization method is adopted, with voltage and State of Charge as equalization variables, and the Fuzzy Logic Control algorithm is designed to dynamically adjust the equalization current to reduce equalization time and energy loss. The topology and control strategy are analyzed in detail. Matlab/Simulink software is used to build the model and simulate it. The experimental results show that compared with the traditional Cuk equalization topology, the equalization topology proposed in this paper can shorten the equalization time by 22.17%. In addition, compared with the mean difference algorithm, the Fuzzy Logic Control algorithm improves the time efficiency by more than 30% and the energy efficiency by about 11% under static and charge-discharge conditions, demonstrating the feasibility of the balancing scheme.

Techno-Economic Analysis of a Fuzzy Logic Control Based Hybrid Renewable Energy System to Power a University Campus in Japan

In order to reduce the load demand of buildings in Japan, this study proposes a grid-tied hybrid solar–wind–hydrogen system that is equipped with a maximum power point tracking (MPPT) system, using a fuzzy logic control (FLC) algorithm. Compared with the existing MPPTs, the proposed MPPT provides rapid power control with small oscillations. The dynamic simulation of the proposed hybrid renewable energy system (HRES) was performed in MATLAB-Simulink, and the model results were validated using an experimental setup installed in the Chikushi campus, Kyushu University, Japan. The techno-economic analysis (TEA) of the proposed system was performed to estimate the optimal configuration of the proposed HRES, subject to satisfying the required annual load in the Chikushi campus. The results revealed a potential of 2% surplus power generation from the proposed HRES, using the FLC-based MPPT system, which can guarantee a lower levelized cost of electricity (LOCE) for the HRES and significant savings of 2.17 million yen per year. The TEA results show that reducing the cost of the solar system market will lead to a reduction in LCOE of the HRES in 2030.

A genetic fuzzy contention window optimization approach for IEEE 802.11 WLANs

The role of IEEE 802.11 wireless local area networks (WLANs) become vital due to its low cost deployment and the aspiration to improve its performance has become the need of the day. Among existing challenges the most prominent are success ratio, packet loss ratio, collision rate, fairness index, and energy consumption, advances in wireless technologies stress to surmount these challenges. To attain these, performance enhancement, genetic fuzzy-contention window optimization (GF-CWO) approach, is proposed which combined the fuzzy logic controller (FLC) and genetic algorithm (GA), through this way GA optimally tuned the FLC. For this purpose three alogrithms are proposed, namely Algorithm-1: GF-CWO for WLANs, Algorithm-2: GA for GF-CWO, and Algorithm-3: FLC for GF-CWO. Proposed GF-CWO approach is tested for binary exponential back-off (BEB), selected being a de facto standard algorithm, and for Channel Status based Sliding Contention Window (CS-SCW) algorithm, selected being a fuzzy logic based algorithm, implemented in MATLAB. Recorded the resultant values for 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 nodes in different files for BEB, CS-SCW, and GF-CWO, respectively. Later on these files were used to evaluate success ratio, packet loss ratio, collision rate, fairness index and energy consumption and also generated the results graphically. The results generated through simulated test confirmed that the GF-CWO has effectively enhanced the performance.

Parameter Optimization of Hole-Slot-Type Magnetron for Controlling Resonant Frequency of Linear Accelerator 6 MeV by Reverse Engineering Technique

This paper presents the parameter optimization of a twelve-hole-slot-type magnetron based on a reverse engineering technique to improve a 6-MeV linear accelerator (LINAC) operation for fruit sterilization. The magnetron structural dimensions are measured by a coordinate measuring machine (CMM) that has tolerance on a dimension of 0.5 µm to analyze the resonant frequency with a desired operating point of the magnetron in the dominant mode. There are two methods of analysis using a proper parameter for the magnetron operation. The first method is mathematical model analysis of an equivalent resonant parallel circuit. The other is 3D-model drawing of the magnetron based on particle-in-cell (PIC) using computer simulation technology microwave studio (CST). The results are demonstrated by the position of the resonant frequency of each mode of operation, and the radius and tuner slot distance of a cavity within the structures of the magnetron cause a resonant frequency change. The suitable parameters of the voltage and magnetic field supply are desired to control a resonant frequency at 2.9982 GHz by using the Takagi–Sugeno fuzzy logic control (FLC) algorithm to control a resonant frequency at 2.9982 GHz. The results of the FLC algorithm application show that the LINAC can produce X-rays with a constant dose rate for an hour with a disturbance in the range of 38 to 42 °C temperature and 1 × 10−9 to 5 × 10−8 torr vacuum pressure.

An optimized Energy Cost Analysis using Fuzzy Logic Control Algorithm for the autonomous Microgrid

In this paper, the significance of Fuzzy Logic Control Algorithm is proposed for determining the effective cost analysis of power generation with respective to the load demand in microgrids. There are many conventional algorithms are developed in the past to evaluate the cost analysis with system constraints, however, they are complex and time consumed. Also, these algorithms are mainly used for conventional systems and are not suitable for microgrids. To overcome these disadvantages, a simple Fuzzy Logic Control Algorithm is used to define the cost analysis effectively. The features of these algorithms are eaisly adapted and incorporated with the objectives of Demand Side Management stratigies in microgrids. In addition, the correlation and an Artificial Bee Colony Optimization technique is proposed between the variables of PV and Wind turbine to obtain the highly accurate power generation samples with respect to the load demands. The analysis of optimization and Fuzzy algorithms are developed using MATLAB Simulation software and the cost analysis are shown for different load variations.

Design and Verification of the LQR Controller Based on Fuzzy Logic for Large Wind Turbine

In this study, a linear quadratic regulator based on the fuzzy logic (LQRF) control algorithm for a variable-speed variable-pitch wind turbine was designed. In addition, to verify the optimum performance of the controller, simulations and wind tunnel tests were conducted. In the simulation, the performances of the proportional-integral (PI) and LQRF algorithms were compared in the transition region and the rated power region. In the wind tunnel test, the applicability of the LQRF algorithm was verified by comparing it with the conventional PI algorithms. The results showed that when compared with the PI control, the proposed LQRF control reduced the tower vibration by up to 12.50% depending on the operating region. Furthermore, the power deviation was reduced by 38.93%. These tests confirmed that the proposed LQRF control increases the power performance and structural stability of wind turbines compared with conventional PI controls.

Maximum Power Point Tracker Controller Using Fuzzy Logic Control with Battery Load for Photovoltaics Systems

The target of this paper is to model a Maximum Power Point Tracker (MPPT) using a Fuzzy Logic Control (FLC) algorithm and to investigate its behavior with a battery load. The advantage of this study over other studies in this field is that it considers a battery load rather than the commonly used resistive load especially when we deal with the relationship between MPPT and system load. The system is about 60 kW which is simulated under various environmental conditions by Matlab/Simulink program. For this type of non-linear application, FLC naturally offers a superior controller for the real load case. The artificial intelligence approach also benefits from this method for overcoming the complexity of nonlinear system modelling. The results show that FLC provides high performance for MPPT of PV system with battery load due to its low settling time and limited oscillation around the steady state value. These are assistant factors for increasing battery life.

Multi-Agent Fuzzy Logic Controller applied to One Lever Variable Pitch Turboprop Aircraft

Modern aircraft turboprop engines are generally managed by pilots by using the well-established two-levers approach which allows the pilot to control independently the engine thrust (power lever) and the propeller rotational speed (condition lever). On the other hand, the twolevers governing system presents several disadvantages in terms of pilot training, fuel consumption and aircraft maintenance. The one-lever approach tries to solve such drawbacks regulating at the same time the engine power and the propeller pitch. In the present work a Multi-Agent Fuzzy Logic control algorithm has been proposed to implement the one-lever approach for the control of the variable pitch turboprop aircraft propulsion system. The whole aircraft ecosystem has been developed in Simulink® framework to verify the feasibility and performance of the proposed one-lever approach.