Type-2 Fuzzy Logic Controller Research Articles

Analysis of interval type-2 fuzzy controller based interleaved sepic PFC converter for PQ enhancement in power solutions

The Power solutions have become indispensable for all the devices in recent years with an appropriate power conversion circuitries and control methods to ensure good dynamic response, improved stability, reliability and efficiency. The main intent of this article is to impart the designing of interval type-2 fuzzy logic controller (IT2FLC) based interleaved Sepic power factor correction (PFC) converter. This work also involves the careful design of the robust controller with enhanced precision and good power quality (PQ) performance at the AC mains. In addition, the development of IT2FLC based power solution improves the overall power conversion with stabilized output in the perspective of its quick rise time, less overshoot and fast settling time in comparison to other traditional controllers. Further, the uncertainties and issues associated with the conventional proportional integral (PI) and fuzzy logic controllers (FLCs) are handled effectively by the proposed IT2FLC controller. Moreover, this preferred converter is modeled with an internal parasitics and its performances are evaluated and compared with other conventional Zeigler Nicholas (ZN) tuned PI controller and FLC by dint of MATLAB/Simulink platform. Finally, the experimental test bench set up of 250 W, 48 V power circuitry is devised and the test outcomes confirm the excellent transient behavior and PQ performances of the modeled power solution.

Opposition-based manta ray foraging algorithm for global optimization and its application to optimize nonlinear type-2 fuzzy logic control

Interval Type-2 Fuzzy Logic Control (IT2FLC) possesses a high control ability in a way that it can optimally handle the presence of uncertainty in a system dynamic. However, the design of such a control scheme is a challenging task due to its complex structure and nonlinear behavior. A Manta Ray Foraging Optimization (MRFO) is a promising algorithm that can be applied to optimize the control design. However, MRFO still suffers the local optima problem due to unbalance exploration-exploitation of the MRFO agents and hence limiting the performance of the desired control. In this paper, Standard, Quasi, Super, and Quasi-Reflected opposition strategies are integrated into the MRFO structure. Each strategy enhances the exploration-exploitation capability and offers different approaches of varying agent’s step size relative to the algorithm’s iteration. The proposed opposition-based MRFO (OMRFO) algorithms are applied to optimize the IT2FLC control design for a laboratory-scaled inverted pendulum system. Moreover, as the algorithms are also promising strategies to other problems, they are applied to solve 50D of 30 IEEE CEC14 benchmark functions representing problems with different features. Performance analysis of the algorithms is statistically conducted using Wilcoxon sign rank and Friedman tests. The result shows that the performance of MRFO and Quasi-Reflected-OMRFO are equal, while all other OMRFO variants show a significant improvement and better rank over the MRFO. The Super and Quasi OMRFO-IT2FLC schemes acquired the best responses for the cart and pendulum, respectively.

Enhanced Control of Polymer Electrolyte Membrane Fuel Cell Fed Basic Series Positive Super Voltage Lift Luo Converter Using Interval Type-2 Fuzzy Logic

An eco-friendly and renewable energy source, Polymer Electrolyte Membrane (PEM) fuel cells offer various advantages such as stability and efficiency. The present study designs a basic series positive super voltage lift Luo (PSVLL) converter which, to produce a PEM fuel cell with a low output voltage, yields a low input current ripple and a high voltage conversion ratio. Additionally, it proposes an interval type-2 fuzzy logic (IT2FL) controller to maintain the output voltage of the proposed converter at a specified value. The model, which consists of a fuel cell and a basic series PSVLL converter, was designed in Matlab/Simulink simulation environment. The enhanced control of the proposed converter output voltage was performed using an IT2FL controller. The simulation results demonstrated that using the proposed controller, the PEM fuel cell fed basic series PSVLL converter reached the specified boundary current and voltage variation ratios under nominal operating conditions and at different reference voltage ranges within a short duration.

Development of a Position Control System for Wheeled Humanoid Robot Movement Using the Swerve Drive Method Based on Fuzzy Logic Type-2

A humanoid robot is capable of mimicking human movements, which poses a challenge for researchers. This has led some to utilise wheels to facilitate its motion. However, achieving smooth and accurate movements at desired positions remains a challenge, necessitating the development of an optimal control system and movement method. In this study, solutions to address these challenges include the use of type-2 fuzzy logic controller (FLC) and the swerve drive method. During the steering rotation movement testing, type-1 FLC exhibits the fastest response time of 0.8 seconds, but oscillations occur, reaching up to 117 degrees to achieve the set point of 90 degrees. Additionally, type-1 FLC cannot reach the set point of -90 degrees. On the contrary, type-2 FLC aligns successfully with both set points of 90 and -90 degrees. In coordinate movement testing, type-1 FLC still shows an error between 1 cm and 2 cm compared to type-2 FLC, particularly with 3 and 5 members, which are equal to the given set point. The results of the tests indicate that type-2 FLC is reliable, showing a small steady-state error, stability, and no overshoot, despite its longer response time and processing duration compared to type-1 FLC.

An adaptive virtual inertia control design for energy storage devices using interval type-2 fuzzy logic and fractional order PI controller

This research paper introduces a novel methodology, referred to as the Optimal Self- Tuning Interval Type-2 Fuzzy-Fractional Order Proportional Integral (OSTIT2F-FOPI) controller for inverter-based energy storage system (ESS) to regulate the input and output power of ESSs, aimed at enhancing the frequency control of microgrids (MGs) with varying levels of renewable energy sources (RESs) integration. The proposed method entails the integration of a self-tuner interval type-2 fuzzy logic controller (IT2FLC) and FOPI controller within a virtual inertia (VI) control loop. Within the OSTIT2F-FOPI control framework, the IT2FLC block tunes the parameters of the FOPI controller in an online method to deal with problems associated with fixed gains of the FOPI controller. Consequently, the VI control unit can emulate varying degrees of inertia and damping based on the integrated levels of RESs or load variations. The self-tuning capability of the proposed method enables real-time performance improvement of the system, resulting in enhanced frequency stabilization and improved control efficiency, particularly in low-inertia MGs. Moreover, a different control methodology, referred to as EO-FOPI-based VI, is developed to compare and assess the results achieved by the suggested OSTIT2F-FOPI control framework. This alternative method incorporates Equilibrium Optimization (EO) to adjust VI and FOPI parameters and provide a comparative analysis of the obtained outcomes.

An innovative fuzzy logic based controller for solid oxide fuel cells

The solid oxide fuel cell (SOFC) stands out as a vital component in sustainable distributed generation systems, thanks to its reliability and adaptable power generation capabilities. This adaptability allows the SOFC to efficiently balance grid power by adjusting its power output. Nevertheless, maintaining the desired fuel usage rate and achieving rapid load tracking pose considerable hardships in SOFC control. To deal with these difficulties, this study introduces a novel approach, the Cascaded Double Input Interval Type-2 Fuzzy Logic Controller (C-DIT2-FLC), designed for SOFC control. In this innovative model, the Improved Salp Swarm Algorithm (ISSA) is applied to determine the crucial parameters of Proportional Integral-Proportional Derivative (PI-PD) cascade controllers. The C-DIT2-FLC dynamically adjusts the controller gains, which are optimized using ISSA, to enhance the effectiveness of SOFC operations. The research results substantiate the superior efficacy of the C-DIT2-FLC over conventional controllers, thereby providing enhanced control and performance capabilities for SOFC systems.

Interval type-2 fuzzy sliding mode control for a cable-driven parallel robot with elastic cables using metaheuristic optimization methods

In this paper, a supervisory interval type-2 fuzzy adaptive sliding mode control scheme is addressed for cable robots. In the proposed control scheme, intelligent methods are combined with conventional sliding mode control to achieve optimal adjustment of control parameters. This approach ensures accurate tracking performance, despite the structural constraints of cable robots. These constraints include the production of purely tensile forces and the effects due to inherent elasticity, which will make the design of the controller challenging. For this purpose, the internal force concept is utilized to ensure the tension style of cables. Additionally, considering a compensator part in the proposed controller and applying the singular perturbation theorem, the vibration effects of elastic cables are handled, and stability is proved through the second Lyapunov method. Therefore, the desired performance of the control system will be guaranteed for the movements that stimulate the vibration modes of cables. An interval type-2 fuzzy logic controller is proposed to adjust the control gain, effectively reducing the chattering level. Moreover, a supervisory interval type-2 fuzzy logic control system is introduced to regulate the gains within the sliding surface. The Grasshopper Optimization Algorithm is employed to select the optimal parameters for the membership functions of the fuzzy system. The results of the conducted simulations show that the desired tracking performance is provided, despite different uncertain parameters and the structural constraints of the considered cable robot.

A Comparison of Type-1 and Type-2 Fuzzy Logic Controller for Full Bridge Boost Converter on DC Microgrid System

A Comparison of Type-1 and Type-2 Fuzzy Logic Controller for Full Bridge Boost Converter on DC Microgrid System

Automated blood glucose regulation for nonlinear model of type-1 diabetic patient under uncertainties: GWOCS type-2 fuzzy approach.

Regulating blood glucose level (BGL) for type-1 diabetic patient (T1DP) accurately is very important issue, an uncontrolled BGL outside the standard safe range between 70 and 180mg/dl results in dire consequences for health and can significantly increase the chance of death. So the purpose of this study is to design an optimized controller that infuses appropriate amounts of exogenous insulin into the blood stream of T1DP proportional to the amount of obtained glucose from food. The nonlinear extended Bergman minimal model is used to present glucose-insulin physiological system, an interval type-2 fuzzy logic controller (IT2FLC) is utilized to infuse the proper amount of exogenous insulin. Superiority of IT2FLC in minimizing the effect of uncertainties in the system depends primarily on the best choice of footprint of uncertainty (FOU) of IT2FLC. So a comparison includes four different optimization methods for tuning FOU including hybrid grey wolf optimizer-cuckoo search (GWOCS) and fuzzy logic controller (FLC) method is constructed to select the bestcontroller approach. The effectiveness of the proposed controller was evaluated under six different scenarios of T1DP using Matlab/Simulink platform. A 24-h scenario close to real for 100 virtual T1DPs subjected to parametric uncertainty, uncertain meal disturbance and random initial condition showed that IT2FLC accurately regulate BGL for all T1DPs within the standard safe range. The results indicated that IT2FLC using GWOCS can prevent side effect of treatment with blood-sugar-lowering medication. Also stability analysis for the system indicated that the system operates within the stability region of nonlinear system.

Power Quality Analysis of Interleaved Cuk Configuration-Based Interval Type-2 Fuzzy Logic Controller for Battery Charging in Electric Vehicles

Power Quality Analysis of Interleaved Cuk Configuration-Based Interval Type-2 Fuzzy Logic Controller for Battery Charging in Electric Vehicles

Solar Assisted Relift Luo Converter with UPQC for Voltage Unbalance Mitigation Using DDSRF Theory

The upsurge seen in integration of sensitive non-linear loads and Photovoltaic (PV) system based power generation has strengthened the demand for enhanced Power Quality (PQ) in distributed power system. Thereby, a Unified Power Quality Conditioner (UPQC), which is a custom power device is proposed in this work for improving the PQ of the overall network. The load side PQ issues is mitigated using the shunt compensator, while the source side PQ issues is mitigated using the series compensator of the UPQC. The active power to the load is provided from the PV system through a shunt compensator. The voltage level of the output obtained from the PV is improved with the aid of Re-lift Luo converter, which is a DC-DC converter of high efficiency and voltage gain. Moreover, in order to provide a stabilized voltage supply to the UPQC, an Adaptive Proportional Integral (PI) controller is selected and its gains are adjusted with the application of Fuzzy Logic Controller (FLC). The Decoupled Double Synchronous Reference Frame (DDSRF) theory is applied to derive the reference voltage and reference current for balancing source voltage variations and load current harmonics, respectively. The series and shunt compensators are controlled using Cascaded Type-2 FLC (CT2FLC). The effectiveness of the proposed PV-UPQC configuration in eliminating load current harmonics and source voltage fluctuations is evaluated on the basis of MATLAB simulations.

Model reference adaptive system speed estimator based on type-1 and type-2 fuzzy logic sensorless control of electrical vehicle with electrical differential

Introduction. In this paper, a new approach for estimating the speed of in-wheel electric vehicles with two independent rear drives is presented. Currently, the variable-speed induction motor replaces the DC motor drive in a wide range of applications, including electric vehicles where quick dynamic response is required. This is now possible as a result of significant improvements in the dynamic performance of electrical drives brought about by technological advancements and development in the fields of power commutation devices, digital signal processing, and, more recently, intelligent control systems. The system’s reliability and robustness are improved, and the cost, size, and upkeep requirements of the induction motor drive are reduced through control strategies without a speed sensor. Successful uses of the induction motor without a sensor have been made for medium- and high-speed operations. However, low speed instability and instability under various charge perturbation conditions continue to be serious issues in this field of study and have not yet been effectively resolved. Some application such as traction drives and cranes are required to maintain the desired level of torque down to low speed levels with uncertain load torque disturbance conditions. Speed and torque control is more important particularly in motor-in-wheel traction drive train configuration where vehicle wheel rim is directly connected to the motor shaft to control the speed and torque. Novelty of the proposed work is to improve the dynamic performance of conventional controller used of model reference adaptive system speed observer using both type-1 and type-2 fuzzy logic controllers. Purpose. In proposed scheme, the performance of the engine is being controlled, fuzzy logic controller is controlling the estimate rotor speed, and results are then compared using type-1 and type-2. Method. For a two-wheeled motorized electric vehicle, a high-performance sensorless wheel motor drive based on both type-2 and type-1 fuzzy logic controllers of the model reference adaptive control system is developed. Results. Proved that, using fuzzy logic type-2 controller the sensorless speed control of the electrical differential of electric vehicle EV observer, much better results are achieved. Practical value. The main possibility of realizing reliable and efficient electric propulsion systems based on intelligent observers (type-2 fuzzy logic) is demonstrated. The research methodology has been designed to facilitate the future experimental implementation on a digital signal processor.

Type-2 fuzzy-logic based control of photovoltaic-hydrogen production systems

Green hydrogen will play an important role for sustainable development, it can be considered as a promising solution for energy transition. In this paper, direct and indirect-coupled configuration of hydrogen production system consisted of Photovoltaic (PV) source and electrolyzer stack are investigated. A study on power loss and efficiency in PV-electrolyzer system is conducted for both cases. Optimal matching conditions in directly coupled hydrogen system can be achieved by modifying the configuration of series/parallel cells in electrolyzer stack or PV generator. However, the energy transfer can be improved by incorporating a buck converter between PV string and electrolyzer stack. In this article, the type-2 fuzzy inference system is proposed to control the operating point of PV string by adjusting the duty cycle of control signal of buck converter. A comparison with classical MPPT methods is carried out in order to verify the efficiency of suggested technique. It has been found that the indirect-coupled hydrogen system can achieve higher energy transfer rate by ensuring continuous extracting of maximum power of PV source under various operating conditions. The obtained results, under Matlab/Simulink environment, demonstrated a satisfactory performance of type-2 fuzzy logic controller in both transient and permanent regimes. We obtain a minimal power loss value (less than 8.1%) in directly-coupled hydrogen production system, in addition a satisfactory value of efficiency (more than 93%) has been obtained.

Modified Flower Pollination Optimization Based Design of Interval Type-2 Fuzzy PID Controller for Rotary Inverted Pendulum System

The Type 2 Fuzzy Logic System (T2FLS) is an enhanced form of the classical Fuzzy Logic System (FLS). The T2FLS based control technics demonstrated a lot of improvements for the past few decades. This is based on the advantage of its membership function (MF). Many experimental studies indicated the superiority of Type 2 Fuzzy Logic Controller (T2FLC) over the ordinary Type 1 Fuzzy Logic Controller (T1FLC), particularly in the event of non-linearities and complex uncertainties. However, the organized design method of T2FLCs is still an interesting problem in the control engineering community. This is due to the difficulties in computing the parameters associated it. A novel application of the Modified Flower Pollination (MFP) optimization algorithm in the design of T2FL is presented. The optimized Cascade Interval Type 2 Fuzzy PID Controller (IT2FPIDC) structure is proposed in this study. The best values of the parameters of the antecedent MFs and the PID gains of IT2FPIDC are found using the MFP algorithm. The MFP optimization technique was used because of its lower computational effort and high convergence speed, in view of the higher number of variables to be optimized in cascaded IT2FPIDC. The MFP-based Type-1 Fuzzy Proportional Integral Derivative Controller (T1FPIDC) is compared with the proposed MFP-based cascade-optimized IT2FPIDC. The rotary inverted pendulum (RIP) which is a non-minimum phase, non-linear, and unstable system is employed as a benchmark for testing the proposed controller. Balance and trajectory-tracking controls of the RIP are considered. Furthermore, the disturbance rejection ability of the proposed controller is analysed. The presented control methos is evaluated on the RIP manufactured by Quanser over many simulations and real-world experiments. The performance characteristics considered are steady state error (Ess), settling time (ts), maximum overshoot (Mp) and rise time (tr). The improvement of the effectiveness and robustness proposed controller in the presence of load disturbance, noise effects and parameter variation is shown.

Design and development of brain emotional learning based adaptive membership functions for type-2 fuzzy systems

This paper presents the design and development of Brain Emotional Learning based adaptive Type-2 Fuzzy Systems for control of dynamical systems. The BEL controller belongs to the class of bio inspired controllers, as its architecture is based on limbic system of human brain and is capable of providing solutions for complex real time problems. In this work, dynamics of Brain Emotional Learning are used for the adaptation of membership functions in the design of Type-2 Fuzzy Logic Controllers. The stability of the overall system is analysed through Lyapunov Yakubovich’s criteria. The proposed approach is validated on the benchmark system such as inverted pendulum, CSTR and Ship heading control through simulation and in real-time environment using OPAL RT OP5600.

PSO tuned interval type-2 fuzzy logic for load frequency control of two-area multi-source interconnected power system

Nowadays, most of modern power systems integrate concentrated renewable energy resources power plants like solar and wind parks in addition to central conventional plants. The output power from these concentrated renewable energy resources varies continuously according to weather conditions like solar irradiance value or wind speed and direction, the variation for their output power may be in mega watts. In this work, Robust secondary load frequency controller (LFC) based on one of artificial intelligent technique which called interval type-2 fuzzy logic controller (IT2FLC) has been proposed for two-area multi-source interconnected power system with central solar park power plants in each area while considering non-linearities in the power system. IT2FLC has accommodated vagueness, distortions and imprecision for the power system input signals which caused by weather fluctuations and system non-linearities. In addition to LFC, another controller based also on IT2FLC has been proposed to control the output power from the central solar parks in each area of generation during cloudy periods instead of maximum power point tracking method (MPPT) in order to enhance the stability for the power system during disturbance periods. In order to enhance the performance of the proposed LFC, particle swarm optimization technique (PSO) has been utilized to optimize the proposed LFC gains to minimize the steady state error, over/under shooting value, settling time and system oscillation for the investigated power system frequency. The performance and the superiority of the proposed PSO tuned IT2FLC is evaluated and compared with another LFC based on PSO tuned cascaded PID controller while applying severe demand load and solar irradiance changes. the simulation has been carried out using matlab/simulink program.

A simple robust mechanism of PV-supported dynamic voltage restorer using interval type-2 fuzzy logic controller

Power electronic devices and variable speed drives solve a power quality issue. To increase the power quality, the distribution side must be compensated by concurrently infusing actual and reactive power. A cost-effective method of preventing voltage sag and swell in power electronic loads is the use of a dynamic voltage restorer. To enhance the power quality for end users, a DC-link component will be combined with the DVR. Ratings for DC-Link elements and inverters are more challenging when constructing a DVR. To simplify things, the Distributed Energy Source (DES) is combined with the DC-Link and the Inverter. The PV-integrated DVR is under the supervision of the Interval type-2 Fuzzy Logic Controller using Synchronous Reference Frame Theory. Reactive power is injected and absorbed under defective situations using a variety of injection techniques with various controllers. The suggested controller enhances power quality and provides exact results under different fault scenarios. Matlab is used to compare the proposed IT2-FLC to a type-1 fuzzy-adjusted PI controller and a traditional mathematical PI controller. The results of the simulations showed that the suggested methodology provided a better outcome on the Distribution side. To validate the simulation results, a scaled-down rating prototype model is created.

Design of a novel mixed interval type-2 fuzzy logic controller for 2-DOF robot manipulator with payload

This paper investigates a novel coupling-based mixed interval type-2 fuzzy logic controller (MIT2FLC) for trajectory tracking problems of highly nonlinear and complex robot manipulator plants. The major impediment for these types of plants is coupling between the robotic links at the time of operation. In addition, the performances of these plants are adversely affected by parameter uncertainties, random noise and external disturbances. Therefore, the MIT2FLC approach with an additional degree of freedom in the membership functions is proposed to efficiently deal with the problem of uncertainty and provide robust performance. The Grey wolf optimization (GWO) is implemented to calculate the optimal parameters of the proposed controller. For checking the performance, the MIT2FLC approach is compared with its type-1 fuzzy counterparts, namely mixed type-1 fuzzy logic controller (MT1FLC), type-1 fuzzy logic controller (T1FLC) and PID controllers. Robustness analysis of the proposed controller is investigated for external disturbances, varying system parameters, and random noise.

DT-CWT and type-2 fuzzy-HSAPF for harmonic compensation in distribution system

Extensive utilization of power electronics in different applications injects harmonics into degrading the power quality. Harmonics can be eliminated in distribution systems using low-cost passive filters, but, they become bulky and complex in design with the increase in harmonic order. Hence, active power filters (APFs) are proposed as a voltage source converter (VSC) for providing better compensation, but active filtering is costly. To minimize such demerits, this article represents a hybrid shunt active power filter (HSAPF). For reference current calculation, dual-tree complex wavelet transform (DT-CWT) is used here and conventional proportional–integral (CPI), type-1 and type-2 fuzzy logic controllers (T1FLC and T2FLC) are employed for parameter optimization of HSAPF, to enhance the harmonic compensation ability and power factor. The case studies are performed in both simulations as well as real-time experimental setup using dSPACE, and it is analyzed that the proposed DT-CWT and type-2 fuzzy-based HSAPF provide better compensation performance in comparison to other controllers under different operating conditions.

Cross-Modal Integration and Transfer Learning Using Fuzzy Logic Techniques for Intelligent Upper Limb Prosthesis

The integration and interaction of proprioception and exteroception in the human multisensory network facilitate high-level cognitive functionalities, such as cross-modal integration, recognition, and imagination for accurate evaluation and comprehensive understanding of the multimodal world. In this article, we propose a novel cross-modal integration framework for the upper limb prosthesis based on type-2 fuzzy logic system (FLS), which can facilitate the high-level cognitive and dexterous manipulation of the prosthesis by combing human's surface electromyography (sEMG) with the computer vision. First, a transfer learning approach is proposed to improve the decoding of human's intent and enhance the effectiveness of skill transition. Then the sEMG signals and image information are integrated to jointly determine the grasp posture of the bionic hand based on fuzzy decision strategy. Fusing multisensory data and using cross-modal integration, the system is capable of crossmodally recognizing multimodal information. In order to realize the prosthesis automatically reaching the target position under the guidance of computer vision, an interval type-2 fuzzy logic controller considering uncertain dynamic parameters and disturbance is designed. Experiments are performed in some typical 3C assembly scenarios, and results show that our proposed strategy can obviously improve the accuracy of grasp posture selection and trajectory tracking effect, which brings more potential job opportunities with hope to amputees, and provides a promising approach toward robotic sensing and perception.