Type-2 Fuzzy Logic Controller Research Articles

Obstacles Avoidance for Mobile Robot Using Type-2 Fuzzy Logic Controller

Intelligent mobile robots need to deal with different kinds of uncertainties in order to perform their tasks, such as tracking predefined paths and avoiding static and dynamic obstacles until reaching their destination. In this research, a Robotino® from Festo Company was used to reach a predefined target in different scenarios, autonomously, in a static and dynamic environment. A Type-2 fuzzy logic controller was used to guide and help Robotino® reach its predefined destination safely. The Robotino® collects data from the environment. The rules of the Type-2 fuzzy logic controller were built from human experience. They controlled the Robotino® movement, guiding it toward its goal by controlling its linear and angular velocities, preventing it from colliding obstacles at the same time, as well. The Takagi–Sugeno–Kang (TSK) algorithm was implemented. Real-time and simulation experimental results showed the capability and effectiveness of the proposed controller, especially in dealing with uncertainty problems.

Design a Robust Powering 5G Telecommunication System Based-Sliding Mode Control

Compared to the previous communication networks, 5G networks provide safe and ultra-reliable connections and enable much more reliable communication with shorter response times, higher data speeds, and a much larger number of simultaneous users. With the growth of the utilization of 5G technology in several applications, the challenges of these power networks will also increase. The power plant in the 5G systems includes several converters feeding dc loads. Most of the loads in the 5G power plant behave as a constant power load (CPL). Due to the negative impedance specifications of CPLs, the stability of such a power electronic converter is threatened when feeding the nonideal CPLs. In response to this challenge, eliminating the destructive effect of these CPLs in the 5G systems is a necessity. To stabilize the voltage of the dc–dc converter feeding CPLs in the 5G systems, this article presents a model-free nonsingular terminal sliding-mode control (MFNTSMC) with a single input interval type-2 fuzzy logic control (SIT2-FLC) to solve the instability problem of the power electronic converters feeding CPLs in the 5G systems. The hardware-in-the-loop tests are accomplished to verify the accuracy of the MFNTSMC based SIT2-FLC suggested controller against the instant changes of the CPL power.

Super-opposition spiral dynamic-based fuzzy control for an inverted pendulum system

This paper presents a hybrid spiral dynamic algorithm with a super-opposition spiral dynamic algorithm (SOSDA) strategy. An improvement on the spiral dynamic algorithm (SDA), this method uses a concept centered on opposition-based learning, which is used to evaluate the fitness of agents at the opposite location to the current solution. The SDA is a simple-structured and deterministic type of algorithm, which also performs competitively in terms of solution accuracy. However, its deterministic characteristic means the SDA suffers premature convergence caused by the unbalanced diversification and intensification during its search procedure. Thus, the algorithm fails to achieve highly accurate solutions. It is proposed that adopting super-opposition into the SDA would enable the deterministic and random techniques to complement one another. The SOSDA was tested on four benchmark functions and compared to the original SDA. To analyze the result statistically, the Friedman and Wilcoxon tests were conducted. Furthermore, the SOSDA was applied to optimize the parameters of an interval type-2 fuzzy logic control (IT2FLC) for an inverted pendulum (IP). The statistical results produced by the SOSDA for both benchmark functions and the IP show that the proposed algorithm significantly outperformed the SDA. The SOSDA-based IT2FLC scheme also produced better IP responses than the SDA-based IT2FLC.

Design and Simulation of Modified Type-2 Fuzzy Logic Controller for Power System

This Article exhibits the structure of a Modified Type-2 (MT2) Fuzzy Logic (FL) Controller (MT2FLC), direction line programming of development, also performance optimization for different power systems. The implementation of the MT2FLC for control of a power system. New participation capacities were considered in adjusting a domain for an Interval Type-2 (IT2) Fuzzy Logic (FL) System (IT2FLS). Another structure in graphic user interface (GUI) mimicked four controllers: an optimal PID controller, FLC, a Type-1(TIFLC), an Interval Type-2 ((IT2FLC), and the MIT2FLC. Their yields were analysed, different periods of the structure procedure for the fuzzy framework, from beginning depiction to conclusive execution, can be gotten from the altered tool compartment (whose capacity to create complex frameworks and adaptability in broadening the accessible usefulness into working with adjusted type2 fuzzy administrators, phonetic factors, IT2 participation capacities, and defuzzification strategies, just as in assessing the MIT2FLC are its best characteristics). Case study for this work, all the optimization controllers implemented for a Brushless DC (BLDC) Motor with MATLAB Ver.2012a was utilized in the recreation and plan of the entirety of the procedure GUIs. Satisfactory results are obtaining which improve the implementation of the using of MIT2FLC controller as practical solution of power system.

Investigation of type 1 and type 2 fuzzy logic controllers performance: application of speed control of BLDC motor

In this paper, a simulation study enhanced to model that the speed control of brushless direct current (BLDC) motors used in electric vehicles with intelligent control methods. The simulation study was prepared in Matlab/Simulink environment. The first control method is Type-1 fuzzy logic control (T1FLC), and the second control method is the Intermittent Type-2 fuzzy logic control (IT2FLC) model. Membership functions for different membership numbers have been created for both types of FLC models. These are 3×3, 5×5, 7×7. Control methods are prepared in Matlab/M-file environment. The model is defined as the input variable of the error, which is the difference between the reference speed and the motor speed, and the output variable of the Pulse Width Modulation (PWM) signal applied to the motor. The simulation study maintains the speed of the BLDC motor up to the reference speed with T1FLC and IT2FLC controllers, depending on the reference speed and applied load values. Depending on the number of different memberships, the effects of controller performances on the control of motor speed have been observed. The graphs and findings of the experiment are shown in the results and discussion section.

Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-Control for Intelligent Vehicles

Intelligent vehicles can effectively improve traffic congestion and road traffic safety. Adaptive cruise following-control (ACFC) is a vital part of intelligent vehicles. In this paper, a new hierarchical vehicle-following control strategy is presented by synthesizing the variable time headway model, type-2 fuzzy control, feedforward + fuzzy proportion integration (PI) feedback (F+FPIF) control, and inverse longitudinal dynamics model of vehicles. Firstly, a traditional variable time headway model is improved considering the acceleration of the lead car. Secondly, an interval type-2 fuzzy logic controller (lT2 FLC) is designed for the upper structure of the ACFC system to simulate the driver's operating habits. To reduce the nonlinear influence and improve the tracking accuracy for the desired acceleration, the control strategy of F+FPIF is given for the lower control structure. Thirdly, the lower control method proposed in this paper is compared with the fuzzy PI control and the traditional method (no lower controller for tracking desired acceleration) separately. Meanwhile, the proportion integration differentiation (PID), linear quadratic regulator (LQR), subsection function control (SFC) and type-l fuzzy logic control (Tl FLC) are respectively compared with the IT2 FLC in control performance under different scenes. Finally, the simulation results show the effectiveness of IT2 FLC for the upper structure and F+FPIF control for the lower structure.

Interval Type-2 Fuzzy Application for Diet Journaling

In this article, an improved system is constructed using interval type-2 fuzzy sets (IT2FS) and a fuzzy logic controller (FLC) with non-singleton inputs. The primary purpose is to better model nutritional input uncertainty which is propagated through the Type-2 FLC. To this end, methods are proposed to (1) model nutrient uncertainty in food items, (2) extend the nutritional information of a food item using an IT2FS representation for each nutrient incorporating the uncertainty in the extension process, (3) accumulate uncertainties for IT2FS inputs using fuzzy arithmetic, and (4) build IT2FS antecedents for FLC rules based on dietary reference intakes (DRIs). These methods are then used to implement a web application for diet journaling that includes a client-side Type-2 non-singleton Interval Type-2 FLC. The produced application is then compared with the previous work and shown to be more suitable. This is the first known work on diet journaling that attempts to model uncertainty for all anticipated measurement error.

Seismic collapse probability and life cycle cost assessment of isolated structures subjected to pounding with smart hybrid isolation system using a modified fuzzy based controller

The possibility of pounding on isolated structures with surrounding moat walls is one of the concerns in the design of isolation systems, especially in pulse-type near-field earthquakes. This paper puts forward the seismic probability assessment of structures equipped with passive and smart hybrid isolation systems by considering pounding possibilities. This investigation is performed on isolated structures equipped with a high damping rubber bearing (HDRB) considering stiff moat walls around the structure. In the Hybrid isolation system, magnetorheological dampers (MR) are considered an adaptive dissipation energy device along with isolators using an optimized novel interval Type-2 fuzzy logic controller with adaptive red-zone function (IT2FS + RZF) to reduce pounding possibilities. The fragility curves of the building for various cases are determined using incremental dynamic analysis (IDA), and possible damage costs are evaluated by using exceedance probability in each damage level. This study concludes that the collapse probability of the isolated structures with restrains at the code-based distance is over the acceptable limit of ASCE 7–22. The smart additional damping system with the proposed controller reduces the possible damage cost of the building by about 64 % compared to the uncontrolled system and puts the collapse probability of the structure in the acceptable range.

Interval type-2 fuzzy logic controller based grid-tied solar PV inverter with active and reactive power control

Solar photovoltaic industry is continuously thriving to improve the performance of power management systems with technology advancement. In the power system, balancing active and reactive power injection and absorption plays a pivotal role in maintaining Grid regulation and power factor and thus improving the efficiency and power handling capability. The Grid-tied solar PV inverters can be the cost-effective solution for reactive power compensation. Solar photovoltaic (SPV) system with the Grid-tied inverter is to produce active and reactive power in various ecological conditions. The Grid-tied PV inverter injects active and reactive power into the grid according to the desired value by the Grid requirements. The Proposed system uses decoupled P-Q theory with Interval type-2 Fuzzy Logic Controller (IT2-FLC). Also, the proposed controller minimizes the Total Harmonic Distortion (THD) and produces regulated output power compared to the other control techniques. The proposed IT2-FLC is compared with a Type-1 Fuzzy Logic Controller (T1-FLC) and conventional mathematical PI controller for performance analysis using simulation. A 75 kW Grid-tied inverter is designed in simulation to prove the effectiveness of the system through Matlab/Simulink. A smaller rating prototype model is developed to verify the simulation results.

Optimization of Interval Type-2 Fuzzy Logic Controller Using Real-Coded Quantum Clonal Selection Algorithm

In recent years, quantum computing has gained immense popularity with the production of real quantum computers. Researchers have developed quantum-inspired evolutionary algorithms (QIEAs) to solve combinatorial optimization problems and have obtained successful results. As a special case of QIEAs, real-coded quantum evolutionary algorithm (RCQEA) is used in the optimization of high-dimensional complex problems. In this study, a novel mechanism of the quantum rotation gate (QRG) that is used to determine the rotation angle of the qubit in the RCQEA is introduced and implemented to accelerate the evolutionary process and increase the possibility of finding the optimal solution. Moreover, the skeleton of RCQEA is modified by using a clonal selection mechanism, and the real-coded quantum clonal selection algorithm (RCQCSA) is developed. Our proposed QRG accelerates the convergence speed of the algorithm. The main purpose of this study is to present a more effective algorithm that inspires quantum computing principles for optimizing the interval type-2 fuzzy logic controller (IT2FLC) membership functions (MFs). In this study, four different comparisons are made with these two different algorithms that have the original version of QRG and our proposed QRG. Optimized IT2FLC provides stabilization of the inverted pendulum system. The results show that the RCQCSA having our proposed QRG outperforms RCQEA in stabilizing the inverted pendulum system by optimizing the IT2FLC parameters.

Driving support by type-2 fuzzy logic control model

Advanced models of Artificial Intelligence enable systems of IoT to work with great flexibility to the needs of users. In this article we present our developed IoT system for driving support by the use of type-2 fuzzy logic control module. We have developed the IoT system to collect the data about driving conditions and evaluate them adjusting to the needs of user. Applied module of fuzzy logic of the second type was used in analysis of accelerometers signals to flexibly adjust to uncertainty of evaluation of driving expectations of each driver. Our developed system was tested in different cars by driving on various roads and results show excellent efficiency.

Type‐2 Fuzzy Logic controller‐based stator current Model reference adaptive system speed observer for a hybrid electric vehicle to improve transient response during limp home mode

SummaryIn this paper, a stator current‐based model reference adaptive system (SCMRAS) for indirect vector control of induction motor fed to hybrid electric vehicle (HEV) for improving the transient response during limp home period has been proposed. In the proposed SCMRAS, the measured stator currents are employed in voltage model to eliminate the integrator in reference model. The stator currents are estimated and are compared with actual current components to estimate the rotor speed. Further, to improve the performance of SCMRAS during limp home period, the PI controller in the adaptation mechanism is replaced with type 2 fuzzy logic controller (T2FLC). The prototype model of the proposed SCMRAS using dSPACE DS 1104 R&D controller board has been developed for implementing speed sensorless indirect vector control of induction motor drive. The performance of SCMRAS and proposed SCMRAS using T2FLC estimators during limp home period is compared.

A comprehensive risk assessment view on interval type-2 fuzzy controller for a time-dependent HazMat routing problem

Hazardous material transportation is an integral part of industries that pose significant risks. Hazardous material transportation risk of is proportional to the volume of materials transferred, the length of the link, and the population density, which varies over time. By modeling the effects of time on population density, this paper presents a multi-objective time-dependent hazardous materials routing problem for efficiently managing hazardous material transportation. The proposed model's objectives include the reduction of transportation risks and travel costs. Given the uncertainty associated with hazardous materials routing, an interval type-2 fuzzy logic controller is used to estimate risk; its inputs include population density, vehicle load, link length, and time of day. A type-2 fuzzy multi-objective evolutionary algorithm based on decomposition is used to optimize the proposed model and an adaptive large neighborhood search to enhance the local neighbor search mechanism. Additionally, by utilizing the nadir reference point, the distribution of Pareto front approximation is improved. Furthermore, a new metric is introduced to evaluate the dispersion of Pareto front approximation. The proposed method is compared to three other state-of-the-art evolutionary algorithms using eleven different performance metrics for validation. Then, using the multiple-criteria decision-making approach, the meta-heuristics algorithms are evaluated. The obtained results demonstrate the proposed solution method algorithm's competitiveness and superiority over the other three evolutionary algorithms.

A Type-2 Fuzzy Logic Approach for Forecasting of Effluent Quality Parameters of Wastewater Treatment

In this investigation, we have studied and designed a type-2 fuzzy logic controller (IT2FLC) for the wastewater treatment plant at Haldia, India. To avoid modeling complex physical, chemical, and biological treatment processes of wastewater, this present work represents an ensemble of fuzzy models as surrogates for the wastewater treatment plant (WWTP). Using measured influent water quality data, each fuzzy model predicts water quality after the process of water treatment parameters. The pH, biological oxygen demand (BOD), total suspended particles (TSS), chemical oxygen demand (COD), and temperature are taken into account as input variables. Finally, the sensitivity of the IT2FLC model is evaluated by several statistical parameters like RMSE, MAE, MAPE, and most importantly R 2 value. For the current model, the values of the three parameters are almost 0, whereas the value of the R 2 is almost close to 1, which signifies that the IT2FLC model is accurate and more efficient in predicting response compared to other conventional methods reported in various literatures.

Error driven Fuzzy Logic Controller (FLC) for Spherical Mobile Robot: Simulation & Experimental Performance Analysis

Spherical mobile robot is a ball-shaped mobile robot that capable to move from one place to another to perform the desired tasks. The challenge for spherical mobile robot falls within control algorithm aspects, where it is nonlinear and highly under-actuated increases the difficulties to control its motion. In this paper, the potentiality of intelligent controller is explored to control the motion spherical mobile robot. An error driven Fuzzy Logic controller (FLC) which consist of modular of two Proportional-Derivative (PD) type FLC and a Proportional-Integral (PI) type FLC were designed and tested to control the position of the spherical mobile robot, angle of the rotation of the pendulum and the angular velocity of the spherical mobile robot, respectively. The performance of the controller was then optimized by tuning the input and output gain of the controller with Particle Swarm optimization (PSO) method. The performance of the robot without the controller and with optimized controller were compared and analysed. As a result, the performance of the system in the form of rise time and settling time of the robot to control its position managed to be reduced by 86% and 89% respectively with optimized PD-type. For PD-type FLC to control the rotation of the pendulum, it was capable to eliminate the overshoot and reduce it settling time by 35.5%. While the optimized PI-type FLC has managed to eliminate the steady state error and achieved it desired velocity setpoint in 1.2s. Lastly, all the designed controllers were successfully tested on the prototype of the spherical mobile robot to study the performance of the spherical mobile robot. However, the overall performance of the implemented designed controller has shown a minimum effectiveness. The controller managed to achieve the setpoint value but with low stability which result from the less accuracy of the hardware sensors.

FLC-ROS: A generic and configurable ROS package for developing fuzzy logic controllers1

Fuzzy logic controllers can handle complex systems by incorporating expert’s knowledge in the absence of formal mathematical models. Further, fuzzy logic controllers can effectively capture and accommodate uncertainties that are inherent in real-world controlled systems. On the other hand, Robot Operating System (ROS) has been widely used for many robotic applications due to its modular structure and efficient message-passing mechanisms for the integration of system’s components. For this reason, Robot Operating System is an ideal tool for developing software stacks for robotic applications. This paper develops a generic and configurable Robot Operating System package for the implementation of fuzzy logic controllers, particularly type-1 and interval type-2, which are based on either Mamdani or Takagi-Sugeno-Kang fuzzy inference mechanisms. This is achieved by employing a systematic object-oriented approach using the Unified Model Language (UML) to implement the fuzzy inference system as a single class that is composed of fuzzifier, inference, and defuzzifier classes. The deployment of the developed Robot Operating System package is demonstrated by implementing an interval type-2 fuzzy logic control of an Unmanned Aerial Vehicle (UAV).

A metaheuristic approach for interval type-2 fuzzy fractional order fault-tolerant controller for a class of uncertain nonlinear system*

A new optimum interval type-2 fuzzy fractional-order controller for a class of nonlinear systems with incipient actuator and system component faults is introduced in this study. The faults of the actuator and system component (leak) are taken into account using an additive model. The Interval Type-2 Fuzzy Sets (IT2FS) is used to design an optimal fuzzy fractional order controller, and two different nature inspired metaheuristic algorithms, Follower Pollination Algorithm (FPA) and Genetic Algorithm (GA), are used to optimize the parameters of the fuzzy PID controller and Interval Type-2 Fuzzy Tilt-Integral-Derivative Controller (IT2FTID) for nonlinear system. The suggested control approach consists of two parts: an Interval Type-2 Fuzzy Logic Controller (IT2FLC) controller and a fractional order TID controller. Additionally, the two inputs of the IT2FLC are also calibrated using two fine tuning parameters and , respectively. The stability of the proposed controller is presented with some conditions. In addition to unknown dynamics, some unknown process disturbances, such as rapid changes in the control variable, are taken into account to check the efficacy of the proposed control scheme. Two nonlinear conical two-tank level systems are used in the simulation as a case study. The performance of the suggested approach is also compared to that of a widely recognized Interval Type-2 Fuzzy Proportional-Integral-Derivative (IT2FPID) Controller. Finally, the proposed control scheme's fault-tolerant behaviour is demonstrated using fault-recovery time results and statistical Z-tests for both controllers, and the proposed IT2FTID controller's effectiveness is demonstrated when compared to IT2FPID and existing passive fault tolerant controllers in recent literature.

Observer-based type-2 fuzzy approach for robust control and energy management strategy of hybrid energy storage systems

In this paper, an observer-based type-2 fuzzy method is proposed for control and energy management strategy (EMS) of the hybrid energy storage system (HESS) which can be composed of the fuel cell (FC), battery (BA), and supercapacitor (SC). The objective and main contribution of the suggested strategy is to provide: 1) Appropriate tracking performance of power sources by an observer-based control method in the presence of noise and signal ripples. 2) An observer-based composite adaptive type-2 fuzzy (OCAT2F) to approximate the voltage of power sources. 3) A dynamical model of DC-bus to guarantee the stability of closed-loop system. 4) An intelligent EMS. To have a high-power supply, the proposed EMS includes two parts; a type-2 fuzzy logic control rule table (T2FLCRT), and an observer-based robust adaptive fuzzy type-2 fuzzy (ORAT2F). Furthermore, stability analyses of the closed-loop system are provided by the input-output linearization (I-OL) approach and based on the Lyapunov theorem. The simulation results of the proposed control scheme under MATLAB/Simulink indicate that the suggested strategy can provide a suitable control performance, and stability of the whole system is achieved.

Optimal Interval Type-2 Fuzzy Logic Control Based Closed-Loop Regulation of Mean Arterial Blood Pressure Using the Controlled Drug Administration

In pandemic times, the remote and automatic control/observation of physiological variables of the patients is the prime necessity for healthcare technologies. The closed-loop regulation of mean arterial pressure is essential for critically ill patients or post-surgery recovery patients. The physiological variables have serious issues such as parameter variations and uncertainties. In this paper, an interval type-2 fuzzy logic controller (IT2FLC) with footprints of uncertainty in the membership functions is presented in a two-layered design (TL-IT2FLC) that can efficiently deal with uncertainties and parameter variations. In this design, a pre-compensator IT2FLC is used, which deals with the uncertainties and parameter variations before the primary IT2FLC approach. The robustness analysis of the proposed method is investigated for external noise, and results are compared with traditional PID and T1-FLC techniques.

An Automatic Wind Turbine Braking System on PLTH Bayu Baru through a Fuzzy Logic Controller

PLTH Bayu Baru is one of the hybrid power plants (HPP) located in Baru beach, Pandansimo, Bantul, Yogyakarta, Indonesia. It generates electrical energy from two sources, wind and solar energy. However, a problem is encountered regarding wind turbine mechanics due to using a manual switch for braking during periods of excessive wind speed. This study proposes an automatic wind turbine braking system through a utilized fuzzy logic controller (FLC) for the PLTH Bayu Baru application. The Mamdani type FLC without complex mathematical models is applied to the Arduino Uno development board to realize the proposed systems. The error (Error_V) and delta error (dError_V) values from the generator voltage sensor become the input of the proposed systems, while the pulse width modulation (PWM) becomes the output for controlling the on/off period of the MOSFET as switching devices. The proposed systems have been tested on a micro-scale wind turbine with PMSG 12V/400W type. From the testing results, the proposed system successfully braked automatically at the point wherein the generator voltage exceeds the setpoint value. Also, the proposed system keeps the generator voltage less than 13.8V, so the problem caused by excessive speed can be resolved.