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The paper presents a tuning methodology for the parameters of adaptive current and speed controllers in a permanent- magnet brushless DC (BLDC) motor drive system. The parameters of both inner-loop and outer-loop PI controllers, which vary with the operating conditions of the system, are adapted in order to maintain deadbeat response for current an...
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... Maintaining the necessary output in the entirety of the old techniques includes suitable adjustments to the controller parameters. As have seen in the Figure 3 show the schematic diagram with traditional BLDCM controller [25], [26]. Two controls have been utilized: the first one in the internal loop (to regulate current) and the second for the external loop (for speed control) through modifying voltage throughout the DC bus. ...
The constant innovative advancements of brushless DC motors (BLDCMs) have discovered a wide scope of utilizations. For instance, underground electric vehicles, drones, and submerged bikes have just confirmed elite BLDCMs. Be that as it may, their appropriation requires control frameworks to screen torque, speed and other execution attributes. This paper presents numerous brilliant controllers structure and order line programming to assemble, alter and mimic intelligent to control BLDCMs. Recently planned graphical user interface (GUI) structure for Multi controllers: conventional proportional integral derivative (PID) controller, intelligent controller based fuzzy techniques: type 1, type 2, and modified type 2 fuzzy logic controller (T1FLC, IT2FLC, and MIT2FLC respectively). Different phases of the problematic framework configuration process, from the underlying depiction to the last usage, can be acquired from the altered tool compartment. MATLAB Ver.2019b was utilized to mimic and plan all procedure GUI. The agreeable aftereffects of MIT2FLC have been approved also; the examination gives the subtleties of the GUI program through estimations, shapes, flowcharts and code, giving understanding into the presumptions of common-sense boundaries that may emerge in such a minimal effort mentor for BLDCM. At last, the outcomes acquired through a few reproduction tests affirm the legitimacy of the showed math model and designing of intelligent controllers. Keywords: Brushless DC motor Conventional PID controller Fuzzy logic controller Graphical user interface Multi intelligent controller This is an open access article under the CC BY-SA license.
... "(vk) and (xk) is the velocity and position of the particle, respectively, best and best are the optimum solution position of the individual particle and swarm particles, respectively, is the weighting factor, 1 and 2 are the learning "factors, and Rand is the uniform distribution random variable over. The flowchart of the PSO algorithm is shown in Fig. 4 [26]. The first particle is produced randomly and the best function value can be found by iteration searching in the PSO algorithm. ...
Because induction motors are used in most industries, IM control is more essential, Optimization is used approaches are becoming more common for improving Three - Phase induction motor (TIM). In addition, the Volt/Hz (V/f) control is utilized to minimize the harmonics level of other control and modulation approaches. This study is about tuning the PI controller parameters for utilization in TIM. To optimize the speed response performance of the TIM, the Particle Swarm Optimization (PSO) algorithm is used to adjust each parameter of the PI speed controller. Kp and Ki of the PI speed controller parameters are optimized for TIM operation with V/f Control by designing an appropriate PSO algorithm. The PI speed controller's performance on the TIM is measured by measuring changes in speed and torque under-speed response events. In PSO, the PI controller performs well in terms of overshoot, settling time, and steady-state error”.
... ANFIS has been utilised in a range of applications to control the speed of permanent magnet excitation transverse flux linear motors and BLDC motors [33][34]. Adjusting the dead-beat gain, Awadallah et al. (2009) used ANFIS to develop a proportional integral-based speed controller for BLDC motors. However, the controller had the issue of being limited to certain operating conditions [35]. ...
... Adjusting the dead-beat gain, Awadallah et al. (2009) used ANFIS to develop a proportional integral-based speed controller for BLDC motors. However, the controller had the issue of being limited to certain operating conditions [35]. ANFIS controllers were also used to manage the speed response of BLDC motors [36][37]. ...
Motors stand out as critical components in every element of modern life that requires motion. Control systems that govern the operation of these devices in industrial or commercial applications become crucial as a result. Improved performance results in increased efficiency, which in the long term translates to significant cost and energy savings and improved output quality. Numerous control systems have been developed specifically for this purpose over the last few decades in response to a persistent requirement for performance improvements. With that in mind, this paper is aimed at classifying and analysing a variety of different types of motor controllers in relation to previous work done with them. The shift from conventional to more intelligent adaptive controllers is highlighted along with their respective principles. Conventional controllers are those designed for linear systems without taking into account the possibility of parametric variation occurrence. Adaptive controllers have the ability to automatically learn and adapt with the state of the plant. Using transient attributes of closed loop control systems, their performance impact and feasibility are studied in detail. Subsequent conclusions are also drawn out to provide clear and concise reference.
... Malik and Tewari (2021) developed a performability model to decide about maintenance priorities for power generation system of a typical thermal power plant. Awadallah et al. (2009) claimed that the methodology used by ANFIS is more popular due to low cost and generates robust results similar to statistical models. Tabesh et al. (2009) used neutral artificial networks and fuzzy logic to assess water conduit failure frequency and operational reliability levels. ...
The purpose of this paper is to predict the online performance of the refrigeration system (a typical process industry) of a dairy plant based on failure rate of subsystems as input parameters by using an adaptive neuro-fuzzy information system (ANFIS). The mathematical modelling of the system is performed to develop Chapman Kolmogorov first order differential equations. These equations are solved recursively to get the performance under steady state conditions with combinations of various failure and repair rate parameters. Then, adaptive neuro-fuzzy inference system (ANFIS) is applied to predict the performance of the system by using failure rates of subsystems as input parameters. The result shows that the prediction of online values closely matches the experimental values. The findings of the paper helped the plant personnel to monitor the online performance of the refrigeration system of a dairy plant. Keywords: process industry; modelling; Markov birth-death process; ANFIS; adaptive neuro-fuzzy inference system; Chapman-Kolmogorov differential equations. Reference to this paper should be made as follows: Jain, V. and Aggarwal, A.K. (2022) 'Mathematical modelling and performance prediction for refrigeration system of a dairy plant using ANFIS', Int.. He received his B.E. (Mech.) with honours from Jamia Millia Islamia (a central university), New Delhi, India. He received his MTech. (Production) degree from GNDEC, Ludhiana, Punjab. His area of interest are metal forming, Springback of sheet metal components with expertise in Reliability Engineering, maintenance optimisation. He has more than 20 years of experience in teaching and industry.
... where determine the centre and represent the fuzzy set width. The training cost is determined using the number of training parameters, thus, since Gaussian membership function has only two adjustable parameters it is the most frequently used membership function in the literature [35]. In this study, Gaussian membership function is adopted for partitioning the input space because of its features such as fewer tuneable parameters and smooth representation of the domain. ...
This paper presents a control-oriented neuro-fuzzy model of brazed-plate evaporators for use in organic Rankine cycle (ORC) engines for waste heat recovery from exhaust-gas streams of diesel engines, amongst other applications. Careful modelling of the evaporator is both crucial to assess the dynamic performance of the ORC system and challenging due to the high nonlinearity of its governing equations. The proposed adaptive neuro-fuzzy inference system (ANFIS) model consists of two separate neuro-fuzzy sub-models for predicting the evaporator output temperature and evaporating pressure. Experimental data are collected from a 1-kWe ORC prototype to train, and verify the accuracy of the ANFIS model, which benefits from the feed-forward output calculation and backpropagation capability of the neural network, while keeping the interpretability of fuzzy systems. The effect of training the models using gradient-descent least-square estimate (GDLSE) and particle swarm optimisation (PSO) techniques is investigated, and the performance of both techniques are compared in terms of RMSEs and correlation coefficients. The simulation results indicate strong learning ability and high generalisation performance for both. Training the ANFIS models using the PSO algorithm improved the obtained test data RMSE values by 29% for the evaporator outlet temperature and by 18% for the evaporator outlet pressure. The accuracy and speed of the model illustrate its potential for real-time control purposes.
... Many researchers paid attention to the DC and the AC electric machines [2]. Many of them studied the DC-motor drives and their control [3][4][5][6][7][8]. Latha [8] studied the design of a model reference adaptive speed control of [7] paid attention to the design of the speed controller of converter fed DC-motor drive using model order reduction technique. ...
In electric drive control systems, the main goal is to maintain the driving motor speed to meet the mechanism’s requirements. In some practical industrial applications the mechanically-coupled load to the motor shaft has a varying mass during the system operation. Therefore, the change of mass changes the value of the moment of inertia of the system. The moment of inertia impacts the system operation, particularly the transient performance. Therefore, the variation of moment of inertia on the motor shaft during its operation creates additional challenges to accomplish a high-quality speed control. The main purpose of the current work is to study the impact of the variation of moment of inertia on the performance of both AC and DC electric drive control systems and to make a comparison between them. A mathematical analysis and simulations of the control system models had been presented; one time with three-phase induction motor and another time with DC motor, both with variable moment of inertia. A simulation of both systems had been accomplished using the Simulink software in MATLAB. The simulation results of operation of these systems have been analysed in order to get useful conclusions and recommendations for the electric drive control system designer.
... Recently, various computational intelligence techniques such as particle swarm optimisation (PSO), bacteria foraging optimisation (BFO), Differential Evolution algorithm (DE) and ant colony optimisation (ACO) are used for many power system problems with remarkable success (Khorramabadi and Bakhshai, 2015;Awadallah et al., 2009;Soliman et al., 2009;Parida and Rout, 2018;Bayoumi and Salem, 2012). The PSO method being computationally efficient, simple, and robust is commonly used to improve the microgrid transient response (Kennedy and Eberhart, 1995;Bayoumi and Soliman, 2007). ...
Islanded microgrids' dynamics are greatly affected by the controllers' gains and power-sharing parameters. This paper considers an islanded microgrid containing two distributed generation (DG) units. Each DG has its pulse width modulated (PWM) inverter for three phases. Each inverter has two control loops in cascade. The inner loop is a current loop and the outer loop is a voltage loop. The controller of each loop is designed to provide good dynamic performance in terms of minimal percentage overshoot and overall system stability. The particle swarm optimisation (PSO) is used to design the current and voltage controllers to achieve the desired performance. The proposed controllers are compared with the conventional technique (Ziegler and Nichols), which shows the excellence of the proposed technique. Different test scenarios as voltage and current tracking and load parameters variation are used to examine the proposed design.
... Now-a-days, numerous algorithms have been established to design the controller especially swarms behaviors like fireflies, ants, bats, birds, pigeons, bees. The GA [2], BAT algorithm [3], cuckoo search algorithm [4], particle swarm optimization [5], flower pollination algorithm [5] and GOA [6] are used for BLDCM speed control. This work interested in Harris hawks based controller design. ...
... Now-a-days, numerous algorithms have been established to design the controller especially swarms behaviors like fireflies, ants, bats, birds, pigeons, bees. The GA [2], BAT algorithm [3], cuckoo search algorithm [4], particle swarm optimization [5], flower pollination algorithm [5] and GOA [6] are used for BLDCM speed control. This work interested in Harris hawks based controller design. ...
... The closed loop system response without controller as shown in Fig.2. The PI and PID controller is mostly used in industry and PI controller is mostly prepared for speed control operation [5]. The system with controller structure is shown in Fig. 3 and the tuning rule is given in Table 2. ...
The electrical vehicle is the upcoming technology in automobile industry due to pollution free and free cost of availability via renewable energy sources utilization. The electric motor is employed in vehicle especially BLDC motor with electronic control. The control part is the foremost participation and the internet technology is hired to perceive and control the vehicle. The motor speed control and softness are the primary factor during the load fluctuations. Many controllers were developed in past eras since Proportional Integral Control (PI). At present, the researcher are interested to apply nature based like Genetic Algorithm(GA), swarm intelligent and many more, controller design rather than traditional controller which provide a good predicted outcome. This work promises the PI controller design in traditional way and also debated with Grasshopper Optimization Algorithm (GOA), Harris Hawks Optimization Algorithm (HHO). The motor parameters are monitored and controlled in the globe with the establishment of Internet of Things (IoT). The controller parameter of PI is Proportional gain (kp), integral gain (kI) which was found from PI tuning, GOA, HHO. The HHO offers minimum jerk at initial condition and smoothness in steady state operations. This model is simulated in MATLAB and validated in hardware configuration.
... In the past decade, artificial intelligence techniques such as neural networks, fuzzy-neural networks, and wavelet neural networks control have been utilized to control the speed of the BLDC motor [7][8][9][10]. Since BLDC motor is a multivariable and nonlinear system, it is complex to obtain high performance by applying classical PID control. ...
... The output of WNN is described by Eqs. (7) and (8). ...
In the recent years, researchers have sophisticated the synthesis of neural networks depending on the wavelet functions to build the wavelet neural networks (WNNs), where the wavelet function is utilized in the hidden layer as a sigmoid function instead of conventional sigmoid function that is utilized in artificial neural network. The WNN inherits the features of the wavelet function and the neural network (NN), such as self-learning, self-adapting, time-frequency location, robustness, and nonlinearity. Besides, the wavelet function theory guarantees that the WNN can simulate the nonlinear system precisely and rapidly. In this chapter, the WNN is used with PID controller to make a developed controller named WNN-PID controller. This controller will be utilized to control the speed of Brushless DC (BLDC) motor to get preferable performance than the traditional controller techniques. Besides, the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of the WNN-PID controller. The modification for this method of the WNN such as the recurrent wavelet neural network (RWNN) was included in this chapter. Simulation results for all the above methods are given and compared.
... Other than this, it is seen that PSO is also applied for structure and parameter optimization of ANFIS. Awadallah et al. (2009) used PSO to train ANFIS for current and speed controllers in a permanentmagnet brushless DC (BLDC) motor drive system. Pousinho et al. (2010) employed PSO to adjust the parameters of the membership functions of ANFIS for short-term wind power prediction. ...
In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.
