Fuzzy Logic Control Strategy Research Articles

Speed Control Of The Interior Permanent Magnet Synchronous Motor Over A Wide Range Using Fuzzy Logic Controller

This paper presents a Fuzzy Logic Controller (FLC) scheme for speed control of an interior permanent magnet synchronous motor (IPMSM) drive over a wide speed range. The proposed FLC is designed based on maximum torques per ampere operation below the rated speed and the field weakening operation above the rated speed, respectively. The main features of the proposed scheme using a fuzzy controller to control both flux and torque simultaneously, in whole speed range. The effectiveness of the proposed FLC is investigated for a 40kW IPMSM and simulated using MatLab/Simulink. The simulation results are compared to those obtained from the conventional PI controller base drive at various conditions such as: step change of command speed and load torque, the disconnection of one phase and parameter variations. Simulation results demonstrate the appropriate dynamic response, robustness again changes, capability in removing load disturbances, without overshoot and undershoot, minimum settling time and lowest steady-state error of the proposed controller over a wide speed range.

Implementation of a Fuzzy Logic Control Strategy on a Harvester’s Controller Based on MATLAB Environment

Due to the nonlinear process of grain harvesting, there is no precise mathematical model to describe the behavior of the cleaning system of a harvester. Both the classical control and modern control methods cannot fulfil the requirements. Owing to this, the intelligent control algorithm was proposed, and the fuzzy logic control (FLC) method is a type of this. At present, most FLC algorithms are proposed in a MATLAB environment. However, the control problems in reality are controlled by microcomputer controllers with different chips. The control language of the microcomputer controller is usually written in C language. It is impossible to directly migrate the algorithm between these two different languages. Therefore, it is an important issue to transplant the FLC algorithm procedure written by MATLAB to the microcomputer controller. To realize the above target, we have built a complete set of control systems for our harvester’s cleaning system based on an upper computer and an STM32 core-chip controller. By means of combining FLC theory and expert knowledge, we adopted an improved FLC algorithm for the cleaning system, which is mounted in our self-designed combine harvester. Through this scheme, we have realized the objective of migrating the FLC algorithm from a MATLAB environment to the controller. The results of the experiment show that our method is reliable.

The Control Schemes of Vehicle Steer by Wire System by using Fuzzy Logic and PID Controller

Recently, the Vehicle Steer-By-Wire (VSBW) system has improved significantly in terms of vehicle dynamics, comfort, safety, maneuverability and stability. VSBW eliminates the mechanical linkage between steering wheel and the front wheels which caused the practical problems for vehicle steering control. This study presents investigations into the development of PID and fuzzy logic control schemes for steering wheel returnability, directional control and wheel synchronization of a VSBW system. For PID control scheme, PD controller is used to control the returnability of steering wheel while PID controller is used to control the angle of front wheel. The Ziegler Nichols method is used for tuning the PID parameters. The fuzzy logic controller is used to control the returnability of steering wheel. The implementation environment is developed within LabView Software for comparison of performance for both control schemes. Implementation results of the response of the VSBW system with the PID and fuzzy logic controller are presented in time domains. The performances of control schemes are examined in terms of steering wheel returnability, directional control and wheel synchronization.

Cascade-Free Fuzzy Finite-Control-Set Model Predictive Control for Nested Neutral Point-Clamped Converters With Low Switching Frequency

In this brief, a cascade-free fuzzy finite-control-set model predictive control (FCS-MPC) is proposed for nested neutral point-clamped converters with low switching frequency (SF). The main objective of the proposed method is to achieve a low SF operation. First, a new cost function is formulated to reduce the SF, and a fuzzy logic control (FLC) technique is employed to choose the weighting factors dynamically. As such, the FLC scheme has a good potential to avoid the constant of weighting factor for the conventional FCS-MPC strategy, especially in the presence of dynamic operation condition. Second, in order to enhance the dynamic response and improve the computationally efficient under low SF, a dynamic reference design with a simplified cascade-free strategy is embedded into the predictive controller. The novelty of this brief lies not only in combining the proposed cost function with the FLC technique for online determining the appropriate values of the weighting factors, but also in a compatible reference design that directly allows formulating the optimal control problem using a cascade-free structure. Finally, simulation results are presented for assessing the effectiveness of the proposed control strategy.

A robust SMES controller strategy for mitigating power and voltage fluctuations of grid-connected hybrid PV–wind generation systems

Recently, hybrid generation systems (HGSs) are considered to be the optimal solution for supplying remote areas with the required electrical power. HGSs contain two or more renewable energy sources (RESs), such as wind power generators (WPGs), photovoltaic (PV) generation systems and the energy of the tides. Due to the intermittent nature of RESs, power and voltage fluctuations have appeared accordingly at the connection point with the utility power grid. Hence, energy storage technologies can play an effective role in mitigating power and voltage fluctuations caused by hybrid PV–wind generation systems. This paper proposes a robust control strategy for superconducting magnetic energy storage (SMES) combined with HGS. Fuzzy logic control (FLC) is used to control the chopper circuit of SMES in order to charge/discharge the active power from/to HGS. The proposed FLC strategy considers the fluctuations due to PV and wind power generation systems and the random load power variation. SMES with the proposed FLC method can effectively mitigate the fluctuation of active and reactive powers transfer between HGS and grid, and it can also regulate the voltage profile of the common connection point of SMES with HGS. Moreover, the system line power loss is clearly reduced by using the SMES system. The complete power system including loads is built in MATLAB/Simulink platform. The simulation results indicate that the proposed FLC–SMES can smooth both power and voltage fluctuations due to uncertain climate changes of PV and WPG systems.

Design and Performance Analysis of a Stand-alone PV System with Hybrid Energy Storage for Rural India

The operations of domestic stand-alone Photovoltaic (PV) systems are mostly dependent on storage systems due to changing weather conditions. For electrical energy storage, batteries are widely used in stand-alone PV systems. The performance and life span of batteries depend on charging/discharging cycles. Fluctuation in weather conditions causes batteries to charge/discharge quite often, which decreases the operational life and increases the maintenance cost. This paper proposes a domestic stand-alone PV system with Hybrid Energy Storage System (HESS) that is a combination of battery and supercapacitor. A new Fuzzy Logic Control Strategy (FHCS) is implemented to control the power flow of the battery and supercapacitor. Simulation studies are performed with real data collected in Sultanpur, India to investigate the proposed system’s performance (Latitude [N] 26.29 and Longitude [E] 82.08). The results show that FHCS successfully controls the power flow of HESS components to increase the system efficiency. The developed system is validated to provide an effective alternative that would enhance the battery life span and reduce the system maintenance cost. While considering the prohibitive upfront costs for rural systems, such an improvement helps to electrify more underserved communities.

An Adaptive Fuzzy Logic Control Strategy for Performance Enhancement of a Grid-Connected PMSG-Based Wind Turbine

Wind power installations are rapidly increasing worldwide, leading to a huge level of permeation into electricity supply networks. Enormous efforts are spent to improve the performance of the wind turbine generator systems. This paper proposes a novel adaptive fuzzy logic control strategy for performance improvement of a grid-tied wind generator system. The variable-speed wind turbine driven permanent-magnet synchronous generator is tied to the electricity network by a full-capacity power converter. A cascaded adaptive fuzzy logic control strategy is proposed as the control methodology for the generator- and the grid-side converter/inverter. The adaptive technique depends on a continuous mixed $p$ -norm algorithm, which on-line updates the scaling factors of the fuzzy logic controllers (FLCs) at a high convergence speed. For the sake of preciseness, real wind speed data measured in the Zaafarana wind farm, Egypt, are considered in the analyses. The effectiveness of the proposed adaptive FLC is compared to that achieved using particle swarm optimization algorithm based an optimal proportional-integral controller, considering severe grid disturbances. Extensive simulation analyses, which are done using MATLAB/Simulink software, are presented to validate the efficiency of the adaptive fuzzy logic control strategy.

Sensorless adaptive rotor flux direct vector-controlled induction motor drive based on fuzzy logic control flux estimator

In this paper, we propose the application of a speed estimation strategy to a fuzzy logic control flux estimator for a sensorless adaptive rotor flux direct-vector-controlled (RFDVC) induction motor drive. The RFDVC induction motor drive was established using the stator current and rotor flux, with the stator current being obtained from the induction motor. The model reference adaptive system (MRAS) theory was utilized to develop an adaptive rotor flux estimator based on voltage-model and current-model flux estimators. The estimated rotor speed and synchronous angle position are derived from the adaptive flux estimator. The adjustment mechanism of this estimator was designed using the fuzzy logic control strategy because this scheme is simple, easy to implement, and requires no precise information about the mathematical model. The MATLAB/Simulink® toolbox was used to simulate this system, and all the control algorithms were realized using a TI 6713-and-F2812 DSP card to validate this approach. Both the simulation and experimental results (including the estimated rotor speed, electromagnetic torque, and stator flux locus) confirmed the effectiveness of the proposed system and thereby validate the proposed approach.

An intelligent control approach for resonance damping in a grid-connected inverter system

Photovoltaic power generation has transformed into the most reassuring strategy for power generation among the renewable sources in view of its intrinsic favorable circumstances. The significant snag that photovoltaic system poses like other inexhaustible sources is the power intermittency in regard to atmospheric conditions like irradiation and temperature. These photovoltaic systems are interfaced to the grid through filters via inverters to prevent harmonics getting in to the grid. Generally, inductor–capacitor–inductor filters are employed in grid-connected system, as they provide better harmonic attenuation than inductor and inductor-capacitor filters. However, they bring resonance as well as oscillations into the grid-connected system. So, in order to suppress the resonance that brings instability problems in the system, active damping control strategies are adopted. But these conventional active damping methods increase the complexity of the system as they need extra sensors. This article proposes a fuzzy logic control-based active damping method to ease the control activity. The proposed controller is contrasted with the injected grid current feedback controller to investigate the resonance damping and the stability of grid interfaced multilevel inverter with inductor–capacitor–inductor filter. When compared with the injected grid current feedback controller, the fuzzy logic control approach promises satisfactory performance based on harmonic content, resonance peak, and stability analysis. The compliance according to the industrial standard in terms of harmonics is met in this article and is duly documented within the manuscript. Simulation results verify the design strategies and the effectiveness of the proposed fuzzy logic control strategy.

Modeling and Fuzzy MPPT Controller Design for Photovoltaic Module Equipped with a Closed-Loop Cooling System

Electrical energy generated by a photovoltaic (PV) panel depends heavily on two climatic conditions: total solar irradiance and absolute temperature. If high intensity of the solar illumination contributes positively to increasing electrical power, a high degree of absolute temperature has, by contrast, a negative effect on its electrical characteristic. In this paper, the electrical efficiency provided by a conventional PV panel is enhanced using the proposed photovoltaic thermal (PVT) panel. The latter contains serpentines fed by a water tank, which allows cooling its PV cells at high temperature. Accordingly, the desired enhancement needs two main requirements: an efficient PVT panel model that accurately describes the actual PVT panel behavior and an efficient controller that correctly tracks the maximum power point tracking (MPPT). For this reason, a number of experimental test data is firstly recorded from an actual ISOFOTON I-50-PVT module under different climatic conditions. Afterward, the recorded data are fitted by the Curve Fitting Toolbox (CF-Tool), creating therefore a 2-dimensional lookup table, used in the following step. Next, the fuzzy logic control (FLC) strategy is employed to synthesize the proposed MPPT-FLC controller, which should ensure a good extraction of the maximal electrical power. To validate the effectiveness of the proposed MPPT-FLC controller based on a 2-dimensional lookup table, the obtained performance is compared, in terms of electrical power and duty cycle, to those provided by an MPPT-FLC controller for a conventional PV panel in various climatic conditions.

Control of a Two-wheeled Machine with Two-directions Handling Mechanism Using PID and PD-FLC Algorithms

This paper presents a novel five degrees of freedom (DOF) two-wheeled robotic machine (TWRM) that delivers solutions for both industrial and service robotic applications by enlarging the vehicle’s workspace and increasing its flexibility. Designing a two-wheeled robot with five degrees of freedom creates a high challenge for the control, therefore the modelling and design of such robot should be precise with a uniform distribution of mass over the robot and the actuators. By employing the Lagrangian modelling approach, the TWRM’s mathematical model is derived and simulated in Matlab/Simulink®. For stabilizing the system’s highly nonlinear model, two control approaches were developed and implemented: proportional-integral-derivative (PID) and fuzzy logic control (FLC) strategies. Considering multiple scenarios with different initial conditions, the proposed control strategies’ performance has been assessed.

A switching-based collaborative fractional order fuzzy logic controllers for robotic manipulators

In the present work, a collaborative implementation of fractional order proportional-integral-derivative control and fractional order fuzzy logic control method is investigated for the controlled tracking performance of a manipulator plant. An integral time absolute error measured between the desired and actual output trajectories, which is obtained by the optimal parameters, drives the switching mechanism between the two controllers, fractional order proportional-integral-derivative and fractional order fuzzy logic control, in order to implement the collaboration scheme. Different possible ways of switching mechanism between fractional order proportional-integral-derivative controller and fractional order fuzzy logic control scheme are explored. The resulting controller's parameters are investigated with meta-heuristic technique namely cuckoo search algorithm. The performance of the proposed collaborative scheme is compared with existing control schemes and basic proportional-integral-derivative control method for obtaining the controlled tracking performance. To generalize the use and effectiveness of designed control scheme, the simulation studies are also investigated for a first order plant and an inverted pendulum for the trajectory tracking problem. To analyse the effects of the parameter variations, disturbance rejection, and payload variations, the performance results for the proposed scheme are also presented for robotic manipulator. The proposed controller has a potential application in robotic manipulator control for different applications.

A Single Input Variable FLC for DFIG-Based WPGS in Standalone Mode

This paper proposes a novel single input variable fuzzy logic controller (FLC) strategy for a wind turbine driven doubly fed induction generator (DFIG) with a battery energy storage in autonomous mode. The proposed control scheme has multi-functionalities such as harmonic elimination, compensation of unbalanced load currents, and extraction of maximum power from the wind. The control of rotor side converter and load side converter is based on the field-oriented approach. The proposed single input variable FLC-based control has good dynamic response as compared to conventional control algorithms. The tip speed ratio-based maximum power point tracking technique is used to extract maximum power from the wind. The simulation of DFIG-based wind power generation system is carried out under various conditions, such as constant wind speed, variable wind speeds, and load currents unbalancing, using Simpower Systems toolbox of MATLAB. The DFIG stator voltages and currents are found balanced and sinusoidal by maintaining constant frequency and voltage at PCC. Finally, a prototype of DFIG-based wind energy system is developed in the laboratory to verify the performance of the proposed control scheme at variable wind speeds and linear, nonlinear loads. The total harmonic distortions of the DFIG stator voltages and currents are obtained within the limits of the IEEE 519 standard.

Numerical Investigation on Fuzzy Logic Control Energy Management Strategy of Parallel Hybrid Electric Vehicle

In order to further reduce fuel consumption, a double input and single output fuzzy logic controller has been established for the energy management of a parallel hybrid electric vehicle (HEV). In this paper, the fuel economy and battery state of charge(SOC) of vehicles has been numerically investigated under both NEDC and WLTC driving cycles. Results showed that compared with the logic threshold control strategy, the fuel consumption under the NEDC and WLTC driving cycles are reduced by 13.3% and 4.5%, respectively, when the fuzzy logic control strategy was adopted. In addition, compared with the logic threshold control strategy, the fluctuation of SOC variation of the fuzzy logic control strategy is much smaller under both driving cycle, which has a positive effect on increasing the effectiveness of the battery discharge, maintaining stability the battery operating, and extending the battery life.

Fuzzy logic control of the support of a lightweight robot during rehabilitation

The usage of robots for the rehabilitation training has the potential to relieve the therapists and to enable patients to train independently. However, the intelligent control of robots in direct interaction with humans is still subject to current research activities. This work presents an assist-as-needed control strategy for an industrial lightweight robot during upper arm rehabilitation training. To include the expert knowledge of therapists, a fuzzy logic control strategy for the stiffness of the underlying impedance controller is designed and implemented. Apart from the patient’s current status, defined by pain and exhaustion, his or her general capabilities and personal characteristics are also considered by the presented approach. In order to test the designed algorithm, an experiment with an additional weight to simulate a patient suffering paresis and an oval movement in the xz-plane is done. The results illustrate that the controller is able to adapt the robot’s behavior according to the current status of the patient and its general condition.

A novel fuzzy logic variable geometry turbocharger and exhaust gas recirculation control scheme for optimizing the performance and emissions of a diesel engine

Variable geometry turbocharger and exhaust gas recirculation valves are widely installed on diesel engines to allow optimized control of intake air mass flow and exhaust gas recirculation ratio. The positions of variable geometry turbocharger vanes and exhaust gas recirculation valve are predominantly regulated by dual-loop proportional–integral–derivative controllers to achieve predefined set-points of intake air pressure and exhaust gas recirculation mass flow. The set-points are determined by extensive mapping of the intake air pressure and exhaust gas recirculation mass flow against various engine speeds and loads concerning engine performance and emissions. However, due to the inherent nonlinearities of diesel engines and the strong interferences between variable geometry turbocharger and exhaust gas recirculation, an extensive map of gains for the P, I, and D terms of the proportional–integral–derivative controllers is required to achieve desired control performance. The present simulation study proposes a novel fuzzy logic control scheme to determine appropriate positions of variable geometry turbocharger vanes and exhaust gas recirculation valve in real-time. Once determined, the actual positions of the vanes and valve are regulated by two local proportional–integral–derivative controllers. The fuzzy logic control rules are derived based on an understanding of the interactions among the variable geometry turbocharger, exhaust gas recirculation, and diesel engine. The results obtained from an experimentally validated one-dimensional transient diesel engine model showed that the proposed fuzzy logic control scheme is capable of efficiently optimizing variable geometry turbocharger and exhaust gas recirculation positions under transient engine operating conditions in real-time. Compared to the baseline proportional–integral–derivative controllers approach, both engine’s efficiency and total turbo efficiency have been improved by the proposed fuzzy logic control scheme while NOx and soot emissions have been significantly reduced by 34% and 82%, respectively.

A Review on Fuel Cell-Based Locomotive Powering Options for Sustainable Transportation

Conventional locomotives employing diesel-based propulsion systems have raised concerns about the environment and hence sustainable development due to their emissions. To obtain an environmentally benign railway system, fuel cell-based locomotives are considered promising candidates owing to their high efficiencies as well as environmental performance. In the present study, a review of the development of fuel cell-based locomotives is conducted and their current progress is described. Further, investigations conducted on various aspects of these types of locomotives are discussed. The literature studies were found to be focused on four main areas namely (1) prototype design/analysis, (2) energy management, (3) feasibility and economic assessment and (4) environmental performance. Fuel cell-based hybrid locomotives entail the potential to reduce the environmental emissions considerably with similar investment costs as diesel fuel-based locomotives. Nearly 3318 tonnes/year of $$\hbox {CO}_{2}$$ emissions may be reduced by replacing diesel engine locomotives with fuel cell trains. Approximately 98% of $$\hbox {NO}_{\mathrm{x}}$$ emissions are estimated to be reduced with the utilization of hybrid fuel cell locomotives. Moreover, several control systems utilizing fuzzy logic control strategy have been proved to be efficient energy management aids for such locomotives. Overall locomotive efficiencies of 50.9% are achievable with the deployment of such control strategies that effectively manage the power demands between the fuel cells, batteries and supercapacitors.

Equalization of Lithium-Ion Battery Pack Based on Fuzzy Logic Control in Electric Vehicle

A nondissipative equalization scheme based on fuzzy logic control (FLC) is presented to improve the inconsistency of series-connected Lithium-ion batteries. The two-stage bidirectional equalization circuit with energy transferring inductors is designed to implement the equalization of cell to cell for battery packs, and the equalization circuit paves the way for module-based equalization of hardware. Equalization based on the state of charge (SOC) is proposed in this paper, and the Thevenin equivalent circuit model of the Lithium-ion battery, as well as the extended Kalman filter (EKF) algorithm, is employed for SOC estimation. For effective equalization, the FLC is proposed to reduce energy consumption and equalization time. The FLC strategy is constructed with a set of membership functions to describe the equalization behavior of the cell. A comparison of the proposed FLC with the mean-difference algorithm is carried out to validate the advantages of the proposed scheme. Simulation results show that the standard deviation of final SOC reduces by ${\text{18}}{\text{.5}}\%$ , and equalization time decreases by ${\text{23}}\%$ for FLC compared with the mean-difference algorithm under new European driving cycle working conditions. The energy efficiency improves by ${\text{5}}{\text{.54}}\%$ compared with the mean-difference algorithm. In addition, the two-stage bidirectional equalization circuit has good performance and improves the inconsistency.

A Fuzzy Adaptive PID Controller Design for Fuel Cell Power Plant

Solid oxide fuel cells (SOFCs) are promising electrochemical devices which translate chemical energy directly into electric energy with high efficiency and low pollution. However, the control of the output voltage of SOFCs is quite challenging because of the strong nonlinearity, limited fuel flow, and rapid variation of the load disturbance. Nowadays, proportional-integral-derivative (PID) controllers are commonly utilized in industrial control systems for their high reliability and simplicity. However, it will lead to overshoot and windup issues when used in the wide-range operation of SOFCs. This paper aims to improve the PID controller performance based on fuzzy logic by (1) identifying a linear model based on the least squares method; (2) optimizing the PID parameters based on the generated linear model; and (3) designing a fuzzy adaptive PID controller based on the optimized parameters. The simulation results of the conventional PID controller and the fuzzy adaptive PID controller are compared, demonstrating that the proposed controller can achieve satisfactory control performance for SOFCs in terms of anti-windup, overshoot reduction, and tracking acceleration. The main contribution of this paper can be summarized as: (1) this paper identifies the SOFC model and uses the identified model as a control object to optimize conventional PID controllers; (2) this paper combines a fuzzy logic control scheme and PID control scheme to design our proposed fuzzy adaptive PID controller; and (3) this paper develops an anti-windup structure based on a back-calculation method to reduce saturation time and overshoot.

Simulation research on a novel control strategy for fuel cell extended-range vehicles

Abstract Adapting to urban transportation and emission reduction in China, fuel cell extended-range commercial vehicles are advocated and studied, which have the advantages of no pollution and long continued driving mileage. According to the features of fuel cell extender and characteristics of the powertrain system of the electric commercial vehicle, the design principle of the extender control strategy is determined in this paper, in order to improve the power and economic performance. A simulation platform for fuel cell plus electric vehicles was established. By comparing and analyzing the characteristics of on-off control strategy, power following control strategy and fuzzy logic control strategy, an on-off power following control strategy is put forward and built which is used for extender controller, and a fuzzy algorithm of following control strategy is studied. By Simulating and analyzing on the platform, the results show that the power following fuzzy algorithm can improve the power performance with the 8.9s accelerating time (0–50 km/h) and better total mileage continued 286.7 km for the powertrain system of fuel cell extended-range commercial vehicles. The research in this paper provides a basis for the in-depth study of the energy management of electric vehicles.