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Speed regulation is one of the significant characteristics to be adopted in the field of brushless DC motor drive for effective and accurate speed and position control operations. In this paper, stability analysis and performance characteristics of brushless direct current motor are studied and implemented with a new deep learning neural network—fu...
Citations
... 13 Analysis, design and optimization of a permanent magnet synchronous motor (PMSM) for an electric vehicle (EV) is reported in Ref.14 In this paper, three parameters of permanent magnet (PM) structure, air-gap length and stator core geometry are optimized in order to validate the rated power and speed range of the drive PMSM. Deep learning architecture for a BLDC motor is reported in Ref. 15 where the authors utilized a multi-layer perceptron network in the design phase to validate the stability of the system. In this design, a unified multi-swarm particle optimization is introduced for tuning the parameters of a deep perceptron neural network to grasp optimal solutions. ...
... where E max is the peak back EMF at the rated speed, k p is the pitch factor, k d is the distribution factor, k s the skew factor and ω m is the rotational speed. 27 The wire cross-sectional area could be calculated according to (15), in which I ph is the phase current and J is the current density. ...
This paper presents the development of an optimization and modeling method for the objective functions of output power, efficiency and weight of an outer rotor permanent magnet brushless DC (BLDC) motor based on radial basis function (RBF) approximation technique. The proposed RBF‐based Pareto optimization method requires less knowledge about electric/magnetic formulas and can replace conventional optimizations based on these equations with higher accuracy. To apply the proposed optimization method, the initial design should be developed using such equations. Therefore, RBFs are used to model and predict engine behavior. To optimize the objective functions, we used a genetic algorithm optimization technique with nonlinear electric and magnetic constraints to find the Pareto front set. The design obtained by the proposed radial basis function Pareto optimization (RBFPO) method was finally verified by Ansoft Maxwell. The results of optimal design using the RBFPO method have higher output power and efficiency. Also, in addition to the advantage of a favorable accuracy, RBF‐based models are significantly faster than models available in simulation tools.
... For instance, to enhance the closed-loop performance of linear and non-linear process models controlled by fuzzy PID controller, Mitra et al. in [13] designed a rule-based set point weighting mechanism. Similarly, the authors in [14] proposed a novel approach that hybridizes deep neural network and fuzzy logic for optimal tuning of PID in order to analyze the stability and study the performing attributes of a brushless DC (BLDC) motor speed controller. However, in ANN, the training process and convergence length become significant, and the effective development of membership functions is dependent on designer skill, data analysis and model tuning in FL controlled system [15]. ...
... Fig. 5 illustrates the comparison of error magnitude of proposed controller and its baseline (HHO) for the best fitness (ITSE-ZLG) values with 50 iterations. Similarly, it can be inferred from the figure that the proposed controller exhibits best fitness values after three (3) iterations whereas the baseline (HHO) achieve convergence starting from the fourteenth (14) iteration. ...
... The optimal parameters of the proposed and other considered controllers obtained using ITSE and ZLG objective function are presented in Table IV. The equivalent transfer function with these parameters for the proposed LHHO-PID controlled DC motor system is as stated in Eq. (14). ...
Proportional Integral and Derivative (PID) controllers are widely employed to normalize the response of numerous DC motor-powered systems, such as electric locomotives, traction systems, etc. Specifically, this category of controller is ubiquitous due to their simplicity, ease of control, and low-cost. However, their design parameters need to be carefully tuned to enhance control performance. A novel LHHO-PID controller with a compound objective function that combine Integral of Time multiplied Squared Error (ITSE) with Zwee-Lee Gaing’s (ZLG) time-domain performance criterion is presented in this current study to enhance the control performance of a DC motor system. The system's performance was assessed by analyzing its response across various dimensions, including frequency responses, convergence profile and time. The results demonstrated that the developed PID-based controller exhibited a commendable performance in regulating the DC motor speed as compared to other existing PID-based controllers in the domain. The time response analysis showed an improvement of 7.2% compared to the best performing controller in rise time, a settling time of 0.1204 seconds, zero overshoot and negligible steady state error. These findings indicate that the proposed LHHO-PID controller has the capacity to regulate the speed of a DC motor efficiently in a variety of industrial applications.
... In the industrial sector, the use of direct current (DC) motors is of crucial importance due to their wide range of applications [35]. These motors are highly valued for their efficiency and versatility. ...
Automated industrial processes require a controller to obtain an output signal similar to the reference indicated by the user. There are controllers such as PIDs, which are efficient if the system does not change its initial conditions. However, if this is not the case, the controller must be retuned, affecting production times. In this work, an adaptive PID controller is developed for a DC motor speed plant using an artificial intelligence algorithm based on reinforcement learning. This algorithm uses an actor–critic agent, where its objective is to optimize the actor’s policy and train a critic for rewards. This will generate the appropriate gains without the need to know the system. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) was used, with a network composed of 300 neurons for the agent’s learning. Finally, the performance of the obtained controller is compared with a classical control one using a cost function.
... Several optimization strategies have been developed to optimize the coefficients of PID based controllers. The authors in [20,21] optimized PID controllers using neural networks. Neural networks can either be trained online or offline and are characterized by their slow response and high computational complexity. ...
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 effective control over the speed of the motor
in the closed loop environment. To achieve 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 controllers
... The SANN has a simple structure, faster learning and improved approximation capabilities. Due to these advantages, it has been applied to control many systems, such as intelligent sensors, DC motors and fixed-wing UAVs [24], [29], [30]. ...
This research focuses on developing a proportional integral derivative controller based on a single artificial neural network (PID-SANN). The proposed control strategy drives the direct current (DC-DC) boost converter output voltage to follow the desired reference value. This controller calculates the PID gains via a learning algorithm based on an artificial single-neuron network, which overcomes the computational complexity of PID gains using analytical methods and automatically adjusts the controller parameters. The developed PID-SANN method offers the boost converter the appropriate duty ratio, which permits controlling the output voltage value despite fluctuations in the resistive load or input voltage. The obtained results confirm that the developed method can successfully surmount the constraints of conventional PID controllers and direct the output voltage of the considered DC-DC converter to follow the required value precisely. This is an open access article under the CC BY-SA license.
... In addition, artificial neural network technology has been applied to industrial applications related to electric motors, for example, estimating the power consumption of brushless DC motors for unmanned aerial vehicles and electric vehicles [16]; identifying faults in motor rotor and stator components [17]; and using multilayer artificial neural networks to optimize the performance of brushless DC motors [18] and predict the service life of motors [19]. Learning architecture based on multi-layerperceptron-topology neural networks has also been extensively studied in estimating the position angle of rotating components (such as axis sensors) [20] and the position of BLDC [21] or speed-control algorithms [22]. The above control system models all introduce complex structures that increase the computational burden and greatly affect the dynamic responsiveness of motor control, making it difficult to achieve ideal control results. ...
In order to reduce the complexity of the brushless DC motor (BLDC)-control-system algorithm while improving the estimation performance of the rotor phase position and the speed of the sensorless motor, a neural network (ANN) control algorithm based on multi-layer perceptron (MLP) topology is proposed. The phase voltage of the motor is conditioned to obtain the phase-voltage signal with a high signal-to-noise ratio, which is used as the input eigenvalue of the multi-layer-perceptron-topology neural network algorithm. The encoder signal is used as the training test data of the MLP-ANN. The first layer of the perceptual neural network estimates the position according to the voltage characteristics with incremental time characteristics. The second layer of the perceptual neural network estimates the speed according to the collected time characteristics and the characteristics of rotor position error. The algorithm after learning and training is digitally discretized and integrated into the motor control system. Experimental tests were carried out under no-load, speed step and load mutation conditions. The experimental results show that the algorithm can accurately estimate the rotor position and speed. The absolute error of the rotor position is within 0.02 rad, and the absolute error of the rotor speed is within 4 rpm. The control system with strong robustness has good dynamic and static characteristics.
... Numerous research efforts have focused on developing motor control systems that can respond quickly, accurately, and with minimal overshoot. DC servo motors [23], DC motor control [24], [25], [26], [27], and BLDC motor [28], [29], [30], [31] are commonly used in these systems. The most popular control method in these systems is the PID control system [32], [33], [34], [35], [36], [37]. ...
This study aims to develop an expert system implementation of P controller and fuzzy logic controller to address issues related to improper control input estimation, which can arise from incorrect gain values or unsuitable rule-based designs. The research focuses on improving the control input adaptation by using an expert system to resolve the adjustment issues of the P controller and fuzzy logic controller. The methodology involves designing an expert system that captures error signals within the system and adjusts the gain to enhance the control input estimation from the main controller. In this study, the P controller and fuzzy logic controller were regulated, and the system was tested using step input signals with small values and larger than the saturation limit defined in the design. The PID controller used CHR tuning to least overshoot, determining the system's gain. The tests were conducted using different step input values and saturation limits, providing a comprehensive analysis of the controller's performance. The results demonstrated that the adaptive fuzzy logic controller performed well in terms of %OS and settling time values in system control, followed by the fuzzy logic controller, adaptive P controller, and P controller. The adaptive P controller showed similar control capabilities during input saturation, as long as it did not exceed 100% of the designed rule base. The study emphasizes the importance of incorporating expert systems into control input estimation in the main controller to enhance the system efficiency compared to the original system, and further improvements can be achieved if the main processing system already possesses adequate control ability. This research contributes to the development of more intelligent control systems by integrating expert systems with P controllers and fuzzy logic controllers, addressing the limitations of traditional control systems and improving their overall performance.
... Numerous research efforts have focused on developing motor control systems that can respond quickly, accurately, and with minimal overshoot. DC servo motors [23], DC motor control [24], [25], [26], [27], and BLDC motor [28], [29], [30], [31] are commonly used in these systems. The most popular control method in these systems is the PID control system [32], [33], [34], [35], [36], [37]. ...
This study aims to develop an expert system implementation of P controller and fuzzy logic controller to address issues related to improper control input estimation, which can arise from incorrect gain values or unsuitable rule-based designs. The research focuses on improving the control input adaptation by using an expert system to resolve the adjustment issues of the P controller and fuzzy logic controller. The methodology involves designing an expert system that captures error signals within the system and adjusts the gain to enhance the control input estimation from the main controller. In this study, the P controller and fuzzy logic controller were regulated, and the system was tested using step input signals with small values and larger than the saturation limit defined in the design. The PID controller used CHR tuning to least overshoot, determining the system's gain. The tests were conducted using different step input values and saturation limits, providing a comprehensive analysis of the controller's performance. The results demonstrated that the adaptive fuzzy logic controller performed well in terms of %OS and settling time values in system control, followed by the fuzzy logic controller, adaptive P controller, and P controller. The adaptive P controller showed similar control capabilities during input saturation, as long as it did not exceed 100% of the designed rule base. The study emphasizes the importance of incorporating expert systems into control input estimation in the main controller to enhance the system efficiency compared to the original system, and further improvements can be achieved if the main processing system already possesses adequate control ability. This research contributes to the development of more intelligent control systems by integrating expert systems with P controllers and fuzzy logic controllers, addressing the limitations of traditional control systems and improving their overall performance.
... The minimization of ripples is the most important work in BLDC motors. BLDC motors are preferred in many applications because they have many advantages such as easy speed control, long service life, high efficiency, and high-power density [27][28][29][30][31]. Aviation, the automotive industry, robotics, medical devices, and ...
The increase in fossil fuel consumption used in conventional vehicles has adversely affected the amount of carbon emissions in the atmosphere. Due to this negativity, many problems such as global warming, noise pollution and cost have emerged. In order to find solutions to these problems, many studies have been carried out to increase the energy storage capacity of Electric Vehicles (EV) since 1835. EVs produced as a result of these studies work more efficiently than traditional tools. However, the driving range problem and charging time are the biggest disadvantages of these vehicles. These disadvantages are a major obstacle for EVs to replace traditional tools. In this study, an experimental study was conducted on flywheel-battery in-vehicle topologies, which are recommended to be used to increase the range in EV and hybrid electric vehicles. In the application, two flywheels with the same rotor radius and different masses were used. Energy was produced from the generator through these flywheels. This energy was employed to charge the batteries. The stored energy and power amounts were investigated depending on the variation of the moment of inertia of both flywheels at the maximum and minimum levels. As a result of this examination, it has been determined which of the flywheels with the same rotor radius but different masses is more suitable for electric vehicles.
... These controllers have become popular in the field of mobile robotics due to their ease of design and testing [40]. In the development of motor systems for mobile robots, PID controllers have been used in a variety of motor types, including DC servo motors [41], DC motors [42], [43], brushless DC motors [44], [45], [46], and modified versions such as those used in electric wheelchairs [47], [48], [49], [50], [51], [52], [53] and balance vehicles with PID control [54], [55], [56]. In some cases, PID controllers have been combined with fuzzy control methods, such as [57], [58], [59], [60], to improve the performance of mobile robots with mecanum wheels and omnidirectional wheels [61], [62], [63], [64]. ...
This research introduces a dual design proportional-integral-derivative (PID) controller architecture process that aims to improve system performance by reducing overshoot and conserving electrical energy. A dual design PID controller uses real-time error and one-time step delay to adjust the confidence weights of the controller, leading to improved performance in reducing overshoot and saving electrical energy. To evaluate the effectiveness of a dual design PID controller, experiments were conducted to compare it with the PID controller using least overshoot tuning by Chien-Hrones-Reswick (CHR) technique. The results showed that a dual design PID controller was more effective at reducing overshoot and saving electrical energy. A case study was also conducted as part of this research, and it demonstrated that the system performed better when using a dual design PID controller. Overshoot and electrical energy consumption are common issues in systems that can impact performance, and a dual design PID controller architecture process provides a solution to these issues by reducing overshoot and saving electrical energy. A dual design PID controller offers a new technique for addressing these issues and improving system performance. In summary, this research presents a new technique for addressing overshoot and electrical energy consumption in systems through the use of a dual design PID controller. A dual design PID controller architecture process was found to be an effective solution for reducing overshoot and saving electrical energy in systems, as demonstrated by the experiments and case study conducted as part of this research. A dual design PID controller presents a promising solution for improving system performance by addressing the issues of overshoot and electrical energy consumption.
