Study on the Extent of the Impact of Data Set Type on the Performance of ANFIS for Controlling the Speed of DC Motor

This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for tracking SEDC motor speed in order to optimize the parameters of the transient speed response by finding out the perfect training data provider for the ANFIS. The controller was adjusted using PI, PD and PIPD to generate data sets to configure the ANFIS rules. The performance of the ANFIS controllers using these the different data sets was investigated. The efficiencies of the three controllers were compared to each other, where the PI, PD, and PIPD configurations were replaced by ANFIS to enhance the dynamic action of the controller. The performance of the proposed configurations was tested under different operating situations. Matlab's Simulink toolbox was used to implement the designed controllers. The resultant responses proved that the ANFIS based on the PIPD dataset performed better than the ANFIS based on the PI and PD data sets. Moreover, the suggested controller showed a rapid dynamic response and delivered better performance under various operating conditions

Literature Review of PID Controller based on Various Soft Computing Techniques

This paper profound the various soft computing techniques like fuzzy logic, genetic algorithm, ant colony optimization, particle swarm optimization used in controlling the parameters of PID Controller. Its widespread use and universal acceptability is allocated to its elementary operating algorithm, the relative ease with the controller effects can be adjusted, the broad range of applications where it has truly developed excellent control performances, and the familiarity with which it is deduced among researchers. In spite of its wide spread use, one of its short-comings is that there is no efficient tuning method for PID controller. Given this background, the main objective of this is to develop a tuning methodology that would be universally applicable to a range of well-liked process that occurs in the process control industry

: Hibridni samopodešavajući fuzzy PID regulator za upravljanje brzinom bezkolektorskog istosmjernog motora

The objective of the proposed work is to investigate the performance of hybrid self tuned fuzzy proportional integral derivative (STFPID) controller for brushless DC (BLDC) motor drive. The proposed hybrid STFPID controller includes a proportional integral derivative (PID) controller at steady state, a PID type self tuned fuzzy logic (FL) controller (STFLC) at transient state thereby combining the merits of both the controllers. The switching function incorporated in the controller ensures desired control response at various operating conditions by appropriately switching between PID and STFPID based on speed error. A detailed simulation study and performance comparison with other control approaches is performed to highlight the merits of the proposed work. The simulation results indicate that the proposed controller is robust with fast tracking capability and less steady state error. The experimental results are provided to validate the simulation study.Cilj ovog rada je istražiti performance hibridnog samopodešavajućeg regulatora za bezkolektorski istosmjerni motor. Predloženi hibridni samopodešavajući fuzzy regulator uključuje PID regulator u stacionarnom stanju i samopodešavajući fuzzy PID regulator (STFLC) za vrijeme trajanja prijelazne pojave kombinirajući prednosti oba regulatora. Funkcija prekapčanja regulatora omogućava upravljanje u različitim uvjetima odgovarajućim odabirom između PID i samopodešavajućeg fuzzy PID regulatora na temelju brzine pogreške. Provedena je detaljna simulacijska analiza i usporedba performanci s ostalim metodama upravljanja kako bi se istaknule prednosti predloženog rada. Iz simulacijskih rezultata je vidljivo je robusno svojstvo predloženenog regulatora te smanjena pogreška u stacionarnom stanju. Sustav pravljanja testiran je i eksperimentalno kao potvrda simulacijskih rezultata

Fuzzy-PID in BLDC Motor Speed Control Using MATLAB/Simulink

Brushless DC motors (BLDC) are one of the most widely used types of DC motors, both in the industrial and automotive fields. BLDC motor was chosen because it has many advantages over other types of electric motors. However, in its application in the market, most of the control systems used in BLDC motors still use conventional controls. This conventional method is easy and simple to apply but has many weaknesses, one example is that if the system state changes, then the parameters of the PID must also be changed so that static and dynamic performance will decrease, causing slow response and frequent oscillations. In this study, the design and simulation of a speed control system for BLDC motors using the Fuzzy-PID method were carried out. The research method is performed through simulation with Matlab / Simulink. The simulation is carried out by providing a speed setpoint input of 650 rpm and used 2 methods, namely Fuzzy-PID Logic and Pi conventional method which was carried out for 1 second. The test results show that the Fuzzy-PID control can provide better and more stable performance than the conventional PI control. The use of Fuzzy-PID control can reduce speed fluctuation and torque stability so that the BLDC motor can operate more efficiently and reliably

Rancang Bangun Sistem Kontrol Kecepatan Motor BLDC Menggunakan ANFIS Untuk Aplikasi Sepeda Motor Listrik

Sistem kontrol kecepatan motor BLDC menggunakan ANFIS telah didesain dan diimplementasikan. Algoritma ANFIS mampu mengkontrol kecepatan motor BLDC sesuai dengan nilai refrensi yang diinginkan. Rata-rata error steady state yang dicapai dengan menggunakan ANFIS adalah sebesar 0,1 % dengan rise time sebesar 2,7437 s untuk kecepatan referensi sebesar 4000 rpm. Proses pembelajaran ANFIS menggunakan metode hybrid PSO dan RLSE dengan supervisi dari Fuzzy-PID. PSO dan RLSE dapat mentraining data ANFIS multi output dengan sangat baik. Data training terbaik dicapai saat nilai λ=1 dengan error RMSE sebesar 0,05364. Waktu eksekusi algoritma ANFIS pada mikrokontroler adalah sebesar 96 us. ====================================================================================================== BLDC motor speed control system using ANFIS has been designed and implemented. ANFIS algorithm is able to control the speed of the BLDC motor according to the desired reference value. The average of steady state error achieved using ANFIS is 0.1% and the rise time is 2.7437 s when the reference speed is 4000 rpm. ANFIS learning process uses hybrid PSO and RLSE methods supervised by Fuzzy-PID. PSO and RLSE can train the ANFIS multi-output data very well. The best training data is achieved when the value of λ = 1 with RMSE error of 0.05364. The execution time of ANFIS algorithm on microcontroller is 96 us

Dual fuzzy logic PID controller based regulating of dc motor speed control with optimization using Harmony Search algorithm

This paper discusses the implementation of a Proportional-Integral-Derivative (PID) controller for regulating the speed of a closed loop four quadrant chopper fed DC motor. The PID controller is combined with a Dual Fuzzy Logic Controller to form a DFPID controller for enhancing the performance of speed control of the DC motor. The DFLC is optimized using a metaheuristic algorithm known as Harmony Search Algorithm (HSA). The major aim of this research is to gain an effective control over the speed of the motor in the closed loop environment. For achieving this, the parameters for the DFPID are selected through time domain analysis which aims to satisfy the requisites such as settling time and peak overshoot. Initially, the fuzzy logic controller in the DFPID controls the coefficients of the PID achievement gain an effective control over the system error and rate of error change. Further, the DFPID is improved by the HAS for obtaining a precise correction. The solutions obtained by tuning the DFPID controller are evaluated from simulation analysis conducted on a MATLAB/SIMULINK platform. The closed loop performance is analyzed in both time and frequency domain analysis and the performance of DFPID is optimized using the HSA algorithm to obtain precise value of the control process. As observed from the Simulation analysis, the DFPID-HSA generates optimized control signals to the DC motor for controlling the speed. The performance of the intended speed control approach is analyzed in terms of different evaluation metrics such as motor speed, torque and armature current. Experimental outcomes show that the proposed approach achieves better control performance and faster speed of DC motor compared to conventional PID controllers and SMC controller

An intelligent power management system for unmanned earial vehicle propulsion applications

Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed with a unidirectional converter and a bidirectional converter to integrate the fuel cell system and the battery into the propulsion motor drive. The main objective of the power management system is to obtain the controlled fuel cell current profile as a performance variable. The relationship between the fuel cell current and the fuel cell air supplying system compressor power is investigated and a referenced model is developed to obtain the optimum compressor power as a function of the fuel cell current. An adaptive controller is introduced to optimize the fuel cell air supplying system performances based on the referenced model. The adaptive neuro-fuzzy inference system based controller dynamically adapts the actual compressor operating power into the optimum value defined in the reference model. The online learning and training capabilities of the adaptive controller identify the nonlinear variations of the fuel cell current and generate a control signal for the compressor motor voltage to optimize the fuel cell air supplying system performances. The hybrid electric power system and the power management system were developed in real time environment and practical tests were conducted to validate the simulation results

An application of modified adaptive bats sonar algorithm (MABSA) on fuzzy logic controller for dc motor accuracy

Controllers are mostly used to improve the control system performance. The works related to controllers attract researchers since the controller can be applied to solve many industrial problems involving speed and position. Fuzzy logic controller (FLC) gains popularity since it is widely used in industrial application. However, the FLC structure is still lacking in terms of the accuracy and time response. Although there are optimization technique used to obtain both accuracy and time response, it is still lacking. Therefore, this research presents works on the FLC system which is the fuzzy inference system that will be optimized by the modified adaptive bats sonar algorithm (MABSA) for the DC servo motor position control. The MABSA will be optimized with the range of the membership input in the FLC. The research aims are to achieve accuracy while minimizing the time response of the DC servo motor. This is done by designing the FLC using the Matlab toolbox. After the FLC is designed completely, the Simulink block diagram for the DC servo motor and FLC are built to see the performance of the controller. The range of the membership function for inputs and outputs will be optimized by the MABSA to get the best positional values. The performance of the developed FLC with the optimized MABSA is verified through the simulation and robustness tests with the system that did not use the FLC and also the system without MABSA. It was demonstrated from the study that the proposed FLC with optimization of MABSA algorithm was able to yield an improvement of 3.8% with respect to the rise time in comparison to other control schemes evaluated. When compared with PSO algorithm, proposed FLC optimized by MABSA showed improvement by 12.5% in rise time and 10% in settling time. PSO-FLC also give 0.6% steady state error compared to the MABSA-FLC. In conclusion, the results validate the better performance in terms of rise time and settling time of the developed FLC that has been optimized by the MABSA

Design and development of intelligent actuator control methodologies for morphing wing in wind tunnel

In order to protect our environment by reducing the aviation carbon emissions and making the airline operations more fuel efficient, internationally, various collaborations were established between the academia and aeronautical industries around the world. Following the successful research and development efforts of the CRIAQ 7.1 project, the CRIAQ MDO 505 project was launched with a goal of maximizing the potential of electric aircraft. In the MDO 505, novel morphing wing actuators based on brushless DC motors are used. These actuators are placed chord-wise on two actuation lines. The demonstrator wing, included ribs, spars and a flexible skin, that is composed of glass fiber. The 2D and 3D models of the wing were developed in XFOIL and Fluent. These wing models can be programmed to morph the wing at various flight conditions composed of various Mach numbers, angles of attack and Reynolds number by allowing the computation of various optimized airfoils. The wing was tested in the wind tunnel at the IAR NRC Ottawa. In this thesis actuators are mounted with LVDT sensors to measure the linear displacement. The flexible skin is embedded with the pressure sensors to sense the location of the laminar-to-turbulent transition point. This thesis presents both linear and nonlinear modelling of the novel morphing actuator. Both classical and modern Artificial Intelligence (AI) techniques for the design of the actuator control system are presented. Actuator control design and validation in the wind tunnel is presented through three journal articles; The first article presents the controller design and wind tunnel testing of the novel morphing actuator for the wing tip of a real aircraft wing. The new morphing actuators are made up of BLDC motors coupled with a gear system, which converts the rotational motion into linear motion. Mathematical modelling is carried out in order to obtain a transfer function based on differential equations. In order to control the morphing wing it was concluded that a combined position, speed and current control of the actuator needs to be designed. This controller is designed using the Internal Model Control (IMC) method for the linear model of the actuator. Finally, the bench testing of the actuator is carried out and is further followed by its wind testing. The infra red thermography and kulite sensors data revealed that on average on all flight cases, the laminar to turbulent transition point was delayed close to the trailing edge of the wing. The second journal article presents the application of Particle Swarm Optimization (PSO) to the control design of the novel morphing actuator. Recently PSO algorithm has gained reputation in the family of evolutionary algorithms in solving non-convex problems. Although it does not guarantee convergence, however, by running it several times and by varying the initialization conditions the desired results were obtained. Following the successful computation of controller design, the PSO was validated using successful bench testing. Finally, the wind tunnel testing was performed based on the designed controller, and the Infra red testing and kulite sensor measurements results revealed the expected extension of laminar flows over the morphing wing. The third and final article presents the design of fuzzy logic controller. The BLDC motor is coupled with the gear which converts the rotary motion into linear motion, this phenomenon is used to push and pull the flexible morphing skin. The BLDC motor itself and its interaction with the gear and morphing skin, which is exposed to the aerodynamic loads, makes it a complex nonlinear system. It was therefore decided to design a fuzzy controller, which can control the actuator in an appropriate way. Three fuzzy controllers were designed each of these controllers was designed for current, speed and position control of the morphing actuator. Simulation results revealed that the designed controller can successfully control the actuator. Finally, the designed controller was tested in the wind tunnel; the results obtained through the wind tunnel test were compared, and further validated with the infra red and kulite sensors measurements which revealed improvement in the delay of transition point location over the morphed wing