Fuzzy Logic Control Strategy Research Articles

Stick-slip stabilization in oil well drill-strings via optimal hybrid fractional order fuzzy logic control scheme

Stick-slip vibration is regarded as one of the major factors that negatively influence the efficiency of the drilling processes. Therefore, it would be highly preferable to overcome the stick-slip vibration challenge through an effective control scheme. This study aims to address the stick-slip vibration problem in oil well drill-strings through a novel concept of an optimal hybrid fractional order fuzzy logic control (OH-FOFLC) scheme. By combining the robustness of fuzzy logic with the convergence speed of the fractional order method, the proposed scheme may enhance tracking performance in drill-strings under various operating conditions. In addition, the fuzzy logic controller for the OH-FOFLC scheme was designed by considering the minimum rules to decrease the computational burden as well as to reduce the controller’s cost. Furthermore, a particle swarm optimization algorithm was implemented for optimizing the coefficients of the OH-FOFLC scheme. To illustrate the overall performance and effectiveness of the OH-FOFLC, four different controllers, including proportional–integral–derivative (PID), fractional order PID, sliding mode control, and optimal hybrid fuzzy logic control approaches, are considered. The validity of the suggested OH-FOFLC was demonstrated through numerical simulations and comprehensive comparative studies which demonstrated that the OH-FOFLC scheme performs better than the four other controllers under various operating conditions.

Stabilization of liquid level in a tank system based on fuzzy logic controller

<p>Process industry needed a fast executed automatic control system capable of handling uncertain, vague problems and nonlinear control variables. Liquid level control is one of the emerging control problems getting the interest of technical experts in the area of control. This paper is based on a fuzzy logic control strategy to maintain and stabilize the liquid level in a tank system that deals with pumping of liquid in tanks as well as regulating liquid level and pushing off the liquid into another tank. Fuzzy controller attains optimum performance by eliminating perturbation in steady state and vanishing the overshoot as compared to proportional, integral, and derivative (PID) controller. The proposed fuzzy logic controller shows minimal steady error as compared to PID controller. The defuzzification of the proposed scheme is based on the centroid method to obtain optimum results. The settling time is nearly 50 second while using fuzzy logic control as compared to 80 seconds in PID control strategy. The overshoot observed is minimal, nearly less than 1% using a fuzzy logic control scheme.</p>

Fuzzy logic-based energy management strategy on dual-source hybridization for a pure electric vehicle

This paper presents a fuzzy logic controller (FLC) based energy management strategy (EMS), combined with power filtering for a pure electric vehicle. The electrical power supply is provided by a hybrid energy storage system (HESS), including Li-Ion battery and supercapacitors (SCs), adopting a fully active parallel topology. The vehicle model was organized and constructed using the energetic macroscopic representation (EMR). The main objective of this work is to ensure an efficient power distribution in the proposed dual source, in order to reduce the battery degradation. To evaluate the impact of the developed design and the efficiency of the developed EMS, the proposed FLC strategy is compared to a classical EMS using SCs-filtering strategy and architecture based on battery storage model. To validate the proposed topology, simulation results are provided for the new European driving cycle (NEDC) using MATLAB/Simulink environment.

Design of intelligent control strategies for full-scale wastewater treatment plants with struvite unit

In this study, intelligent (fuzzy logic) control strategy is designed for plant-level wastewater treatment plants (WWTP) with an aim to reduce the operational cost and improve the effluent quality. Benchmark simulation model (BSM2PSFe) with an additional resource recovery unit (Struvite unit) is considered as a working platform. Binary-level control topology is designed in which lower-level PI controllers are implemented to control dissolved oxygen (DO) in the first, second, and third aerobic reactor and fuzzy logic controller (FLC) is designed at the supervisory level. This supervisory level considers the ammonia (NH3) in the second and third aerobic reactor to alter the DO set-points in the lower level. Six control approaches (CA1, CA2, CA3, CA4, CA5, and CA6) are developed by using different combinations of PI and FLC Both operational cost index (OCI) and effluent quality index (EQI) were improved by 9.9 % and 1.14 % respectively in the case of CA2 when compared with the existing open-loop. Nevertheless, EQI was improved by 7.1 % with a slightly higher OCI in the case of CA5. In most cases, greenhouse gas emissions were also decreased. The ammonia, total nitrogen, and total phosphorus concentration removal rates were improved by 87.14 %, 5.6 %, and 5.8 % respectively.

Chaotic-Billiards Optimization Algorithm-Based Optimal FLC Approach for Stability Enhancement of Grid-Tied Wind Power Plants

Globally,wind power plants (WPPs) installations are dramatically increasing, leading to a large-scale integration into the power grids. Huge endeavors are devoted to achieving an efficient operation of WPPs. This paper offers a novel chaotic-billiards optimizer (C-BO) algorithm to properly design the fuzzy logic control (FLC) strategy for stability enhancement of grid-integrated WPPs. The C-BO algorithm has distinct features such as simple construction, low numbers of parameters required to design, high convergence speed, and low computational burden. The permanent-magnet synchronous generator (PMSG)-based WPP is interlinked into the electric power grid through power converters. A cascaded FLC approach, which is optimally fine-tuned by the C-BO, is offered as the control methodology for these converters. The C-BO algorithm fine-tunes the gain factors of multiple FLCs at a rapid convergence speed. A simulation-based optimization approach is applied, in which the integrated square error criterion is chosen as an objective function. For the sake of preciseness, the PMSG-based WPP is connected to the IEEE 39-bus New England test system. The effectiveness of the optimal FLC using the C-BO algorithm is compared with that achieved using the water cycle algorithm-based optimal FLC, the genetic algorithm-based optimal FLC, and the marine predator algorithm-based optimal proportional-integral controller, taking into account severe grid disturbances. Moreover, real wind speed measured data captured from Zaafarana Station in Egypt are extensively performed in the system studies. The validity of the offered control approach is widely verified by the simulation analyses using the MATLAB/Simulink environment.

Energy Management of Parallel Hybrid Electric Vehicle Based on Fuzzy Logic Control Strategies

Currently, the parallel hybrid electric vehicle (PHEV) is the most common type of architecture on the hybrid vehicle market. Therefore, a PHEV can be a solution to reduce emission and fuel consumption. The main challenge in the development of HEVs is the power management between the components that ensure vehicle movement. Energy management is now highly necessary by applying a control strategy (CS) in the vehicle’s traction chain, which directly affects the PHEV emission and fuel economy. The CSs have different performances, namely the control of the different power sources operation mode and the control of the battery state of charge. For this purpose, we propose a fuzzy logic CS to optimize emissions (FLCS-em) for PHEV. To assess this approach, we compare it with the most commonly used and recent EMS, in particular the strategy to optimize fuel use (FLCS-f), the efficiency optimization strategy (FLCS-eff) and the electric assist CS (EACS), in urban and highway driving cycles. The results show that the elaborate FLCS-em, characterized by a limited number of rulers, provide significant advantage than CSs mentioned in terms of the efficiency of PHEV performance and emissions and fuel consumption minimization.

A Dual Control Regenerative Braking Strategy for Two-Wheeler Applications-A Review

In this paper, Review on bi-directional DC/DC buck-boost converter with dual control strategy during regenerative braking is used for a two-wheeler application. To reduce the harmful emissions from automobiles and massive surges in fuel prices, automotive electric vehicles are an effective alternate solution. In this paper, a cascaded bi-directional DC/DC buck-boost converter with dual control strategy during regenerative braking is used for atwo-wheeler application. The dual control strategy with the cascaded converter is used to increase the average power stored during the braking period and to reduce the vehicle’s stopping time. The converter with the proposed control strategy used in this work has made it possible to charge the battery even when the back emf of the machine is less than the battery voltage. A fuzzy logic control strategy is used to consider the non-linear factors like SOC, speed of the vehicle and the required brake force. This is done in order to make the system more reliable and realistic. implementing the dual control strategy, the average power stored by the battery is increased by 2.5 times and the vehicle comes to halt faster in comparison with the existing control strategy. The versatility of the strategy is shown by examining three different scenarios during the regenerative braking process.

Adaptive and Optimal Overcurrent Protection of Wind Farms With Improved Reliability

This article presents a new adaptive and optimal overcurrent protection for large-scale wind farms using hybrid grey wolf optimizer and rule-based fuzzy logic controller (GWO-FLC) scheme. The proposed GWO-FLC based adaptive protection scheme overcomes the limitation of conventional protection scheme that suffers from maloperation of relays that damages the power components in the system during fault. Furthermore, due to the intermittent nature of wind, the generated current feeding the grid during extreme high and low wind speeds creates a significant difference in magnitudes of the generated current. Thus, the existence of only one group of settings for the overcurrent relays (OCRs) would cause drastic miscoordination and false tripping during fault. In order to test the performance of the proposed GWO-FLC scheme, several OCRs based on standard Schweitzer Engineering Laboratories industrial protection relay (SEL-751) are developed in MATLAB/Simulink. A large-scale wind farm model is also developed to perform fault analysis, relay setting, and coordination calculation. The GWO is implemented to attain optimal OCR coordination for the defined quadruple group settings for all relays, and the FLC is developed to provide adaptive feature for the relays so that the OCR group settings for each relay are intelligently updated according to the variation of wind speed. Finally, in a laboratory environment, the proposed protection scheme is implemented in real time using field-programmable gate array DE2-115 board equipped with Cyclone IV-E device (EP4CE115F29C7). It is found from both simulation and experimental results that the performance of the proposed hybrid GWO-FLC is robust, reliable, efficient and extremely satisfactory.

Control Performance Analysis of Mining Ship Heave Compensation System Based on Fuzzy Logic Algorithm

With the depletion of land resources, demands for marine resources mining is increasing, the mining vessels which is known as one of the most important offshore equipment are widely concerned. The motions of wave surges in the marine environment will affect the operation of mining vessels, especially for the heave motion which will cause serious disturbances to the mining vessels during operation, and even the damage of the hull structure and related mining facilities. Hence, it is very necessary to compensate for the heave motion of the mining vessel, and a hydraulic cylinder-based active-passive integrated composite heave compensation system is designed in this paper. Firstly, the structural composition and working principle of the heave compensation system are introduced, and its mathematical model is also established. Secondly, the frequency domain and time domain analysis are carried out using AQWA software to obtain the heave displacement response of the ship under regular and irregular waves of six sea states as the desired compensation displacement. Finally, the controller of the active-passive heave compensation system is designed, and a fuzzy logic control strategy based on Mamdani type is proposed, and the control performance is analyzed. The results show that the accuracy of the fuzzy logic control algorithm is better than the traditional PID control algorithm, and the laws and methods of PID adjustment are obtained, which proves that the fuzzy logic control algorithm has obvious advantages for engineering applications.

Vibration control of laminated composite cantilever beam operating in elevated thermal environments using fuzzy logic controller

In the present study, vibration control of laminated composite cantilever beam operating in the elevated thermal environment is achieved using combined experimental and numerical techniques. The impact hammer test is performed on the glass-epoxy cantilever beam at different temperatures. Experimentally recorded impact hammer force signals and piezoelectric accelerometer time-domain signals are processed through a system identification toolbox in MATLAB to obtain transfer functions of the plant models. A robust fuzzy logic controller is developed to accomplish the effective vibration control of a cantilever composite beam operating at different temperatures. The fuzzy logic controller with two inputs and one output is designed using the 20 if-then rules. The results are presented in both frequency and time domain, keeping the vibration attenuation of the fundamental frequency as the point of interest. The results indicate the proposed fuzzy logic control strategy can attenuate the vibrations of a cantilever composite beam for a wide temperature range.

Hierarchical energy management system with multiple operation modes for hybrid DC microgrid

This paper presents a developed energy management system (EMS) with a hierarchical three-level distributed control approach proposed for a photovoltaic/wind turbine/diesel generator with energy storage in an islanded hybrid DC microgrid (MG). In the hierarchical three-level control structure, the power to be transferred by the distributed energy sources (DER) is provided by controlling the converters in the primary control layer. The secondary control layer is used to achieve DC bus voltage restoration and increase current-sharing accuracy simultaneously. In the tertiary control layer, it is aimed to minimize the production cost by performing optimum load-sharing. For the operation of this control structure, a strategy is proposed to optimally manage the energy demand of the hybrid system autonomously under variable load conditions. The EMS with three different operating modes (Mode-I, Mode-II, Mode-III) is proposed to share power between DC MG sources optimally. In Mode-I, renewable energy sources (RES) are enabled to operate with MPPT operation. Energy consumption minimization strategy (ECMS)-based linear programming (LP), genetic algorithm (GA) and pattern search (PS) algorithms are used to minimize the fuel consumption and carbon emissions of the diesel generator (DG) in Mode-II operating Mode. In Mode-III operating mode, DG is not needed, and the power flow between DERs is optimized by using the fuzzy logic controller (FLC) and state machine control strategy (SMCS). The amount of fuel consumed, the cost of fuel consumption and ESS's SOC level are used as the EMS's performance criteria for the MG. The minimum fuel consumption of the DG is calculated as 2.328 L for the operation of LP-based ECMS, and the fuel cost, in this case, is found to be $3.26. Considering the charging of the ESS, the highest SOC level is achieved with 64.87% for the PS-based ECMS-SMCS combination. By considering fuel minimization and SOC level together, it can be seen that the best result is achieved with the LP-based ECMS-SMCS combination.

A Dual Control Regenerative Braking Strategy for Two-Wheeler Applications with Different Modes of Operation -A Review

In this paper, Review on bi-directional DC/DC buck-boost converter with dual control strategy during regenerative braking is used for a two-wheeler application. To reduce the harmful emissions from automobiles and massive surges in fuel prices, automotive electric vehicles are an effective alternate solution. In this paper, a cascaded bi-directional DC/DC buck-boost converter with dual control strategy during regenerative braking is used for a two-wheeler application. The dual control strategy with the cascaded converter is used to increase the average power stored during the braking period and to reduce the vehicle’s stopping time. The converter with the proposed control strategy used in this work has made it possible to charge the battery even when the back emf of the machine is less than the battery voltage. A fuzzy logic control strategy is used to consider the non-linear factors like SOC, speed of the vehicle and the required brake force. This is done in order to make the system more reliable and realistic. implementing the dual control strategy, the average power stored by the battery is increased by 2.5 times and the vehicle comes to halt faster in comparison with the existing control strategy. The versatility of the strategy is shown by examining three different scenarios during the regenerative braking process.

Preview Dynamic Chassis Control Based on Road Condition Recognition Employed Machine Vision

In order to solve the problem that the dynamic chassis control needs to passively respond after the tire touches the target object, this paper proposes a dynamic chassis control method based on fuzzy logic and road condition recognition employed machine vision. The fuzzy logic control strategy is first conducted to suppress vertical and pitch motion. Then, the camera fixed on the car window is used to take pictures of bumps or potholes and form a training data set. Based on the powerful pattern recognition capabilities of the neural network, the MATLAB neural network toolbox is used to build the convolutional neural network for data training and form the recognition operator and transfer the recognition results to the dynamic chassis controller in the form of CAN signals to make it act in advance. Based on the vibration dose value of the vertical acceleration comparison with the one without preview control of the vehicle passing the same bump or potholes, the method reduces the vertical acceleration at different vehicle speeds to corresponding degrees, which proves that the method is effective for improving the ride and comfort of the vehicle in such conditions.

Improved DTC strategy with fuzzy logic controller for induction motor driven electric vehicle

<abstract><p>In the near future, zero-emission transportation is anticipated to be implemented in an effort to reduce the major pollutants caused by road transportation. This enormous endeavor will be impossible until all modes of transport are electrified. The induction motor-fed direct torque controller is widely used for EV applications due to its fast torque response and simplicity. However, ripples in torque and flux and current harmonics are the major issues related to DTC. The fuzzy-based DTC replaces the hysteresis comparators and the switching table with fuzzy logic blocks to realize fuzzy DTC control, which improves the system's performance. This paper presents an enhanced fuzzy logic control strategy of induction motor for electric vehicle applications. The main objective is to enhance the system's performance by reducing torque and flux ripples. Both the conventional and fuzzy-based DTC are simulated with MATLAB/SIMULINK, followed by a comparative assessment to validate the effectiveness of the proposed approach for both steady-state and transient operations. The results indicate improvements in torque ripple, flux ripple, and speed ripples by 69%, 10%, and 85%, respectively. Due to the reduction in ripples, there is also an improvement in the THD of the stator current by 17%. During transient, an average improvement of integral square error for torque and speed is 8% and 12%, respectively. Further, the proposed method is validated using EUDC and HWFET drive cycles, demonstrating a reduction in battery energy demand.</p></abstract>

Adaptive Lane Keeping Assist for an Autonomous Vehicle based on Steering Fuzzy-PID Control in ROS

An autonomous vehicle is a vehicle that can run autonomously using a control. There are two modern autonomous assistant systems that are proposed in this research. First, we introduce a real-time approach to detect lanes of the streets. Based on a series of multi-step image processing through input data from the camera, the vehicle’s steering angle is estimated for lane keeping. Second, the steering control system ensures that autonomous vehicles can operate stably, and smoothly, and adapt to various road conditions. The steering controller consists of a PID controller and fuzzy logic control strategy to adjust the controller parameters. The simulation experiments by Gazebo simulator of the Robot Operating System (ROS) not only indicate that the vehicle can keep the lane safely, but also demonstrate that the proposed steering angle controller is more stable and adaptive than the conventional PID controller.

Overall fuzzy logic control strategy of direct driven PMSG wind turbine connected to grid

The fuzzy logic strategies reported in the literature about the control of direct drive permanent magnet synchronous generator (PMSG) connected to grid are limited in terms of inclusiveness and efficiency. So an overall control based on fuzzy logic and anti-windup compensation is proposed in this paper. Aiming at the inadequate of hill climb search (HCS) MPPT with fixed step size, the fuzzy logic is introduced in the stage of "generating rotor speed reference" to overcome the oscillations and slowness in traditional method. PI controllers are replaced by anti-windup fuzzy logic controllers in the "machine side control" stage and in "grid side control" stage to pertinently regulate the reference parameters. Then comparison tests with classical methods are implemented under varying climatic conditions. The results obtained demonstrate that the developed control is superior to other methods in response time (less than 4.528E-04 s), precision (an overshoot about 0.41%) and quality of produced energy (efficiency is 91%). The study verifying the feasibility and effectiveness of this algorithm in PMSG wind turbine connected to grid.

Applying fuzzy logic control to analyze real-time control for charging and discharging power of electric vehicles

The disorderly charging of large-scale electric vehicles will aggravate the peak-valley difference of the power grid, and affect the power quality and life of the transformer. The fuzzy logic control strategy for charging and discharging optimization of charging vehicles under the framework of fuzzy logic control from the perspective of the group is considered in this article. A real-time control method based on the clustering characteristics of the charging end time is proposed according to the different charging requirements of the connected electric vehicles and fuzzy logic control is adopted to solve the problem of optimal charging and discharging power of the entire cluster and a single electric vehicle. A fuzzy logic control model considering the charging and discharging of electric vehicles is established orienting at minimize daily load fluctuations and control penalties in the upper layer. The charging and discharging cost of electric vehicle owners is considered to solve the optimal control problem of the charging and discharging power of a single electric vehicle. Taking the data of the typical regional distribution network load as an example, it is verified that the real-time charging optimization strategy under fuzzy logic control through simulation can ensure the reliable operation of the power grid while considering the interests of all parties.

Interval Type-2 Fuzzy Logic Anti-Lock Braking Control for Electric Vehicles under Complex Road Conditions

To simultaneously track the ideal slip rate and realize ideal energy recovery efficiency under different complex road conditions, an electro-hydraulic compound anti-lock braking system based on interval type-2 fuzzy logic control strategy and its corresponding braking torque allocation strategy have been developed for electric vehicles. The proposed interval type-2 fuzzy logic controller aims to calculate the ideal total braking torque by four steps, namely, fuzzification, fuzzy inference, type reduction, and defuzzification. The slip rate error and the change rate of slip rate error are utilized as inputs in the fuzzification, and then, the membership degree interval of fuzzy variables determined by the upper and lower membership functions is used to calculate the activation degree interval of different fuzzy rules in the fuzzy inference process, which enhances the anti-interference ability to external uncertainties and internal uncertainties. The braking torque allocation strategy is proposed to maintain the maximum energy recovery efficiency on the premise of safe braking. The software of MATLAB/Simulink is applied to simulate the process of anti-lock braking control under two complex road conditions. Simulation results corroborate the proposed interval type-2 fuzzy logic anti-lock braking control system can not only obtain better slip rate control effect and outstanding robustness but also achieve ideal regenerative braking energy recovery efficiency under both joint-μ and split-μ road surfaces.

Grey wolf fuzzy optimal energy management for electric vehicles based on driving condition prediction

An energy management strategy (EMS) plays a decisive role in the performance of pure electric vehicles. Meanwhile, the effectiveness of EMS is directly affected by prospective driving conditions. This paper conducted research on grey wolf fuzzy optimal energy management strategy optimization for electric vehicles based on driving condition prediction. In order to improve the accuracy of driving condition prediction, a combined prediction method was proposed which combines fixed state transition matrix prediction and rolling prediction based on cycling conditions. The evaluation results show that the proposed solution has better prediction accuracy than previous approach. Then, this paper adopted the fuzzy logic control strategy based on driving condition prediction, which is optimized by using the grey wolf optimizer (GWO) algorithm. The results of simulation analysis revealed that the energy management strategy combining condition prediction and GWO algorithm had significant improvement in energy consumption rate and other indicators. Finally, the hardware-in-the-loop (HIL) experiments were conducted and the results had good coherence to that of the simulation. This verified the feasibility and effectiveness of the proposed control strategy in a real-time environment.

Solar module emulator based on a low-cost microcontroller

The aim of this paper is to present an experimental analysis of a solar module emulator based on a low-cost microcontroller. An Arduino low-cost microcontroller is used to mimic several solar module technologies with high accuracy. The estimation and extraction of the single-diode photovoltaic model parameters is performed by a developed software. Two control strategies are applied to the photovoltaic emulation, one based on the digital proportional integral controller and the second one based on fuzzy logic controller. The emulator is able to follow the module I-V curves for fast and simultaneous real-time change of solar radiation, cell temperature and load, based on an online resolution of the single-diode mathematical model of the photovoltaic module. Finally, the experimental and modeled I-V curves of four different photovoltaic module technologies are compared and assessed the efficiency of the proposed fuzzy logic control strategy compared to the proportional integral control strategy.