DC Motor Control Research Articles

PID control algorithm based on multistrategy enhanced dung beetle optimizer and back propagation neural network for DC motor control.

Traditional Proportional-Integral-Derivative (PID) control systems often encounter challenges related to nonlinearity and time-variability. Original dung beetle optimizer (DBO) offers fast convergence and strong local exploitation capabilities. However, they are limited by poor exploration capabilities, imbalance between exploration and exploitation phases, and insufficient precision in global search. This paper proposes a novel adaptive PID control algorithm based on enhanced dung beetle optimizer (EDBO) and back propagation neural network (BPNN). Firstly, the diversity of exploration is increased by incorporating a merit-oriented mechanism into the rolling behavior. Then, a sine learning factor is introduced to balance the global exploration and local exploitation capabilities. Additionally, a dynamic spiral search strategy and adaptive [Formula: see text]-distribution disturbance are presented to enhance search precision and global search capability. The BPNN is employed to fine-tune both PID and network parameters, leveraging its powerful generalization and learning ability to model nonlinear system dynamics. In the simplified motor experiments, the proposed controller achieved the lowest overshoot (0.5%) and the shortest response time (0.012s), with a settling time of 0.02s and a steady-state error of just 0.0010. In another set of experiments, the proposed controller recorded an overshoot and response time of 0.7% and 0.0010s, across five DC motor tests. These results demonstrate the proposed adaptive PID control algorithm has superior performance in optimizing control system parameters, as well as improving system robustness and stability.

Improved PID control of DC motor

Abstract. In this paper, the circuit optimization problem of precise control of DC motor through PID interface is studied. The main purpose of this research is to strategically coordinate resistance and capacitance elements in circuit design to explore and improve the speed of signal transmission and the accuracy of response. Additionally, by optimizing the interaction between these components, this paper attempts to achieve improved performance and efficiency in the motor control system, that is, changing different K_p and K_d to achieve the best fit parameters for the most system. The experimental approach employed in this study involves a collaboration between Tinkercad and Falstad software platforms to simulate and analyze the effects of various circuit configurations as well as coefficients. The effects of K_p and K_d on the system are obtained through systematic experiments and analysis. Meanwhile, it aim to verify the proposed optimization technique and evaluate its effectiveness in practical applications. The simulation results of Tinkercad and Falstad agree well with the expected theoretical results.

Analyzing the performance of metaheuristic algorithms in speed control of brushless DC motor: Implementation and statistical comparison.

A brushless DC (BLDC) motor is likewise called an electrically commutated motor; because of its long help life, high productivity, smaller size, and higher power output, it has numerous modern applications. These motors require precise rotor orientation for longevity, as they utilize a magnet at the shaft end, detected by sensors to maintain speed control for stability. In modern apparatuses, the corresponding, primary, and subsidiary (proportional-integral) regulator is broadly utilized in controlling the speed of modern machines; however, an ideal and effective controlling strategy is constantly invited. BLDC motor is a complex system having nonlinearity in its dynamic responses which makes primary controllers in efficient. Therefore, this paper implements metaheuristic optimization techniques such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Accelerated Particle Swarm Optimization (APSO), Levy Flight Trajectory-Based Whale Optimization Algorithm (LFWOA); moreover, a chaotic map and weight factor are also being applied to modify LFWOA (i.e., CMLFWOA) for optimizing the PI controller to control the speed of BLDC motor. Model of the brushless DC motor using a sensorless control strategy incorporated metaheuristic algorithms is simulated on MATLAB (Matrix Laboratory)/Simulink. The Integral Square Error (ISE) criteria is used to determine the efficiency of the algorithms-based controller. In the latter part of this article after implementing these mentioned techniques a comparative analysis of their results is presented through statistical tests using SPSS (Statistical Package for Social Sciences) software. The results of statistical and analytical tests show the significant supremacy of WOA on others.

Design Development and Performance Evaluation of Solar Operated Pigeon Pea (Cajanus cajan L. Millsp.) Pruner

Pigeon pea (Cajanus cajan L. Millsp.) is a vital grain legume known for its contribution to food security and soil fertility. However, pruning, which enhances plant architecture and yield, remains a significant challenge in its cultivation, especially in large-scale operations. Manual pruning is labor-intensive, time-consuming, and costly, which has led to the need for mechanized alternatives, a solar-operated pruner was developed at IGKV, Raipur, to reduce labour, time, and costs. This study focuses on the design, development, and performance evaluation of a solar-operated pruner tailored specifically for pigeon pea. The pruner integrates key components such as a cutting blade, DC motor, solar panel, battery, and charge controller. A 12 V DC motor, generating 10.53 N of force and operating at 8500 RPM, ensures efficient pruning. The motor is powered by a 7.2 Ah battery, which can be charged in 4 hours using an AC adaptor or 8 hours via a 10 W solar panel. The battery allows for 4-6 hours of continuous operation. The pruner's lightweight PVC frame makes it portable and user-friendly, while the protective blade guard ensures operator safety. Performance evaluation of the pruner indicated a field efficiency of 81% and pruning efficiency of 76%. The actual field capacity was measured at 0.097 ha/h, with a walking speed of 1.2 km/h. Losses due to half-cut stems were calculated at 9.04%. The pruner demonstrated its ability to reduce labor costs, improve pruning efficiency, and promote sustainable agriculture through its solar-powered operation. This study highlights the economic and environmental benefits of adopting solar-operated pruners for pigeon pea cultivation.

Design and Application of Parameter Self-tuning Regulator for DC Motor based on Neural Network

This study introduces a cutting-edge approach to regulating DC motors, featuring a unique combination of Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks. This innovative system capitalizes on the adaptive learning capabilities of ANNs to dynamically fine-tune the control parameters of DC motors. This adaptability ensures optimal motor performance across diverse operational conditions, addressing the challenges posed by fluctuating loads and varying speed requirements. The integration of LSTM networks into this framework adds a layer of predictive functionality, allowing the system to anticipate future motor states. Such foresight enables the regulator to make proactive adjustments, significantly enhancing its responsiveness to changes in operational demands. The dual application of ANN’s adaptive control mechanisms and LSTM’s predictive capabilities is particularly effective in overcoming the non-linearity and variability that are typical challenges in DC motor control. This synergy ensures that the motor operates efficiently, stably, and with a quick response time, even under varying and unpredictable conditions. The practical application of this advanced regulator in real-world scenarios has shown marked improvements in motor performance. These enhancements are evident in the increased efficiency, stability, and responsiveness of the motors, making them more suitable for a wide range of industrial applications. This study marks a notable progression in the field of DC motor control technology. By integrating advanced machine learning techniques, it offers a solution that is not only more efficient and reliable but also adaptable to the evolving demands of industrial environments. The innovative combination of ANN and LSTM networks in this regulator design paves the way for smarter, more responsive, and efficient motor control systems, potentially transforming how motors are managed in various industrial applications.

Voltage stability control strategy for DC microgrid based on adaptive virtual DC motor

The large-scale integration of distributed energy sources and power electronic devices results in the DC microgrid exhibiting significant low inertia and weak damping characteristics. This, in turn, leads to inevitable fluctuations in the DC bus voltage, which endanger the stable operation of the DC system. Energy storage devices can provide equivalent inertia. To enhance the inertia and response speed of the DC bus interface converter, this paper proposes a power allocation parameter adaptive virtual DC motor control strategy based on a hybrid energy storage unit. The strategy introduces power allocation control to regulate the energy storage converter on the basis of virtual inertia parameter adaptive control, thereby enabling the energy storage converter to simulate the inertia and damping characteristics of a DC generator. The small-signal stability of the system is analyzed by establishing a small-signal model of the photovoltaic energy storage system and utilizing the impedance ratio criterion. Finally, the proposed control strategy is validated through simulation. The results demonstrate that the strategy effectively mitigates the fluctuations in bus voltage under varying photovoltaic power and sudden load changes, ensures the power distribution in the hybrid energy storage system, and enhances the dynamic response of the system.

A novel approach to design and analyze fractional order PID controller for speed control of brushless DC motor

A novel approach to design and analyze fractional order PID controller for speed control of brushless DC motor

Achieving Robust and Optimal Speed Control of DC Motor through Sliding Mode Control Tuned by Genetic and Particle Swarm Optimization Algorithms

Achieving Robust and Optimal Speed Control of DC Motor through Sliding Mode Control Tuned by Genetic and Particle Swarm Optimization Algorithms

Nonlinear Dynamics Induced by Coil Heat in the PMDC Motor and Control

In this paper, the interesting dynamics of chaos induced by the effect of the variation of internal average heat during operation in the DC motor control by the full bridge drive are analyzed. By using simple powerful tools of analyzing nonlinear dynamical systems like phase portraits, time traces and frequency spectrum in the MATLAB-SIMULINK environment, we showed that under certain conditions, the PMDC motor develops different behaviors so as periodic limited, and chaotic attractors, when the motor drive different form of external load torque and the windings resistance variation. This paper presents the first studies on the variation of the average heat of the motor and the amplitude of the triangular load torque to produce the strange phenomena like chaos as far as our knowledge go. A chaos control of the unstable regime is proposed to stabilize the PM DC motor in a desire regime. This contribution is very important in industry because some unexplained dynamical behaviors of the DC motor driven by a full bridge now can be avoided.

A robust optimization strategy for brushless DC motor repetitive controller based on H-infinity

The control of Brushless DC (BLDC) motor has many challenges. A large number of harmonics are generated during motor operation, while it is susceptible to external disturbances such as noise. The large torque ripple is also a constraint to the large-scale popularity of BLDC motors in some high-end industries. Taking the BLDC motor control system as the research object, this paper proposes a control method of H-infinity repetitive control. First, the state space expression is derived from the mathematical model of the BLDC motor. Then, the parameters of the low-pass filter in the internal mode link are obtained for the design of the internal mode link in the repetitive control. In addition, the H-infinity repetitive control system for the BLDC motor is constructed and converted into the standard form of the H-infinity system. Finally, bringing the generalized controlled object in the standard form of the H-infinity into MATLAB to derive the compliant compensator. The simulation results show that the proposed method can not only suppress harmonics but also improve the robustness of the BLDC motor control system.

Novel modified Chernobyl disaster optimizer for controlling DC motor

This article presents the modified Chernobyl disaster optimizer (CDO) method for DC motor control to find the optimal proportional integral derivative (PID) settings. DC motors are widely used machinery. DC motors are also simple to use. The detonation of the Chernobyl nuclear reactor core served as the inspiration for the idea and guiding principles of the CDO. CDO has limitations in the stability of exploration and exploitation areas. This research aims to obtain a new balance of exploration and exploitation. This study suggests incorporating the levy flight and chaotic algorithm (CA) techniques to enhance the CDO method. This study was conducted with the MATLAB/Simulink software. A comparative technique, which included the marine predator algorithm (MPA), golden jackal optimization (GJO), and CDO, was utilized to determine the performance of the MCDO method. According to the study’s findings, the MCDO method’s overshoot value outperformed all other approaches.

Performance Comparison and Analysis of Four- and Six-Switch Inverter Controls of BLDC Motor Employing Deep Learning

Robust and effective control of brushless dc (BLDC) motors is paramount in modern-day motion control. The BLDC motor is known for its high speed, high torque, small size, low noise, and equally low maintenance requirements compared to the brushed DC motor. Nowadays, it can be found in the areas of robotics, aerospace, military, and industrial machines, among others. In this paper, two inverter topologies, the threephase six-switch driver circuit (TPSSDC) and the three-phase four-switch driver circuit (TPFSDC), are used to drive and control the speed of the BLDC motor. For the control technique, however, a fitting neural network from the deep learning (DL) toolbox is employed to train and improve the speed performance of the motor. Both TPSSDC and TPFSDC are simulated and tested in MATLAB Simulink, and the resultant output is analyzed graphically and analytically. Graphical observation shows that the TPSSDC control approach is superior in terms of reference tracking and has less ripple, and better rise and settling times when compared to the TPFSDC control approach, however, the TPFDC is considered for its low cost and simple circuitry. Numerically, the TPSSDC also outperforms at 1000 rpm with a 1.685 ms rise time and a 1.420 ms settling time compared to the TPFSDC, which rises at 6.526 ms and settles at 5.237 ms. At 3000 rpm motor speed, the TPSSDC is better, having a rise time of 5.815 ms and settling time of 4.048 ms, when compared to the TPFSDC, which rises at 11.277 ms and settles at 10.067 ms.

Generalized uniform optimization for robust adaptive Buck converter with uncertain perturbations

This paper presents an event-triggered impulse (ETI) control mechanism with generalized uniform optimization, endowing robust adaptive for various uncertain Buck converter perturbations. An impulse compensates for the state fluctuation error, and the event triggering mechanism (ETM) is introduced into the control process of the stochastic model of the Buck converter. The mean square stability theory of linear matrix inequality (LMI) stochastic systems is provided, wherein the cost function incorporates uncertain perturbation-induced state feedback impulses and adaptive system tuning. Unlike traditional event-triggered control with manually determined triggering times, ETI is activated only when events conform to the optimal design, significantly enhancing error-tracking accuracy and adaptive capability. Moreover, the parameters of the optimal controller remain independent of the statistical characteristics of uncertain noise, ensuring globally optimal operational control and model generality. Namely, the proposed method demonstrates versatility by extending its application to DC motor control, addressing various perturbations Buck converters encounter. Finally, the designed controller can closely track the required angular velocity trajectory across sudden changes in system uncertain perturbation.

Design and Optimization of an Electric Go-Kart

This research paper contains an in-depth study of the design and optimization processes for an electric go-kart prototype focused on at addressing the demand for sustainable transportation alternatives. The essential target of the undertaking is to design a protected and useful go-kart with an inflexible casing and very much-mounted powertrain while complying with contest rules. Through a multidisciplinary approach, the group means comprehending the complexities of vehicle design and creating a model reasonable for customer deals. Key center regions incorporate upgrading driver solace and security, expanding vehicle execution, and advancing mobility. The paper subtleties the design methodology, division into sub-groups, and joining of trend-setting innovations like Brushless DC (BLDC) motors, motor controllers, and battery. Also, it talks about the difficulties faced during the venture, including overheating the motor regulator, and proposes solutions to defeat these difficulties. The exploration also features electric vehicles' meaning in moderating environmental change and accomplishing energy autonomy, highlighting the desperation of progressing to reasonable transportation options. DOI: https://doi.org/10.52783/tjjpt.v45.i03.7472

Analysis of the PID Controller Parameters for A Surface Mobility Platform Mobile Robot

Surface mobility control is a fundamental challenge in robotics, and its applications range from autonomous vehicles to industrial automation. The main aim of the paper is to analyze the PID controller parameters for the speed control of a surface mobility platform mobile robot. MATLAB simulation toolbox and mathematical model of the DC motor are used to find the optimal PID controller parameters to complete the DC motor speed control system. For the SMP motion control system, the mobile robot kinematic model is supported as a differential drive system. In this paper, the PID controller parameters for the DC motor control system are analyzed with simulation results using various KP, KI, KD gains. The experimental results of the SMP mobile robot with optimal PID controller parameters are analyzed by driving on the rough, slope and even terrains. According to simulation results and experimental tests, the proposed system effectively controls the speed of the SMP mobile robot, demonstrating satisfactory performance in settling time, rise time, and system overshoot. Among PI and PID controllers, the designed PID controller can quickly control to reach the desired DC motor speed. Using the optimal PID coefficient parameters, all motors reached their desired speed in 0.12 seconds during simulations. In the experimental tests on various terrain (even, slope and rough), all motors achieved their desired speed simultaneously within 25 seconds.Keywords: Differential drive, PID controller, motion trajectory, real-time speed control. skid steering, SMP mobile robot, surface mobility control

Stability Analysis of Controlled DC Motor

Controlled dc motors are nonlinear systems, thatshow a nonlinear action in their operation including, sub-harmonics and chaos when they work outside their designspecifications. This nonlinearity forces the motor changingits normal operation to a random-like behaviour; In thispaper, the nonlinear dynamics of DC motors areinvestigated. It is shown that the concept of the Poincarémap approach and the monodromy matrix method can besuccessfully applied to determine the stability of DC motors

Controlling parameters proportional integral derivative of DC motor using a gradient-based optimizer

In this paper, a gradient-based optimizer (GBO) algorithm is presented to optimize the parameters of a proportional integral derivative (PID) controller in DC motor control. The GBO algorithm which mathematically models and mimics is inspired by the gradient-based Newton method. It was developed to address various optimization issues. To determine the performance of the proposed method, a comparison method with the ant colony optimization (ACO) method. It was compared using the integral of time multiplied absolute error (ITAE). They are most popularly used in the literature. From the test results, the proposed method is promising and has better effectiveness. The proposed method, namely GBO-PID, shows the best performance.

New PID parameter tuning based on improved dung beetle optimization algorithm

AbstractIn this paper, a proportional‐integral‐derivative (PID) controller parameter optimization method based on the improved dung beetle optimization (IDBO) algorithm is proposed, which improves the balance between the global exploration and local exploitation capabilities of the dung beetle optimization (DBO) and significantly enhances the convergence speed and optimization accuracy. Initially, the dung beetle population is initialized using piecewise linear chaotic map (PWLCM) chaotic mapping in order to increase its variety and the DBO algorithm's capacity for global exploration. Furthermore, adaptive weighting in the DBO algorithm is now balanced between the capabilities of local exploitation and global exploration with the addition of adaptive weights. After that, in order to improve the DBO algorithm's capacity for local exploitation, a triangle wandering strategy is included during the dung beetle reproductive phase. Finally, using both Lévy flying wandering and greedy strategy (GS) together make it easier to take advantage of opportunities in both local and global areas. The outcomes of the traditional benchmark function test demonstrate a significant improvement in both convergence speed and optimization accuracy when the particle swarm optimization (PSO), DBO, grey wolf optimization (GWO), and sparrow search algorithm (SSA) algorithms are compared. The performance index function incorporates an overshooting penalty term to prevent the overshooting phenomenon in the control system. Simulation experiments are carried out for the DC motor control system, and the time domain performance, frequency domain performance, and robustness performance of the closed‐loop control system with ZN‐PID, Lambda‐PID, PSO‐PID, and IDBO‐PID rectified PID controller parameters are comparatively analyzed, which verifies the validity and practicability of the IDBO algorithm.

Optimization of BLDC-based electric vehicles: Vehicle dynamics modelling through dual-motor approach and designing a novel augmented TLBO algorithm for PID control

The work primarily focuses on increasing the efficiency of EV drive in electric two-wheeler by working on several aspects, such as modulating the vehicle’s design, optimizing the control strategy, and increasing the speed range using a dual-motor approach. The dynamics of electric two-wheeler have been discussed with a mathematical vehicle model and further tuning of several aspects. Besides, this paper also introduces a novel Augmented Teaching and Learning based Optimization (ATLBO) technique designed exclusively to control BLDC motors for the electric two-wheeler vehicle. Besides, the designed technique has been implemented for the widely used commercial e-bike of Hero Company. Most two-wheeler electric motors are mounted on the rear side of the wheel, which has a limited speed range. Therefore, an analysis has been performed to increase the vehicle’s speed range using a dual motor, from 45 km hr−1 to 62 km hr−1, proving to be a viable alternative to a single motor generally used in an electric bike. ATLBO technique has been designed against a conventional TLBO to optimize the proportional-integral-derivative (PID) controller for the speed control of a linear brushless DC (BLDC) motor. The proposed method has several advantages, including ease of implementation, a consistent convergence characteristic, and high computational efficiency. Furthermore, the literature has validated the merits of the presented novel control technique. The only disadvantage of using a dual motor is the initial cost, but the overall cost is moderated in the long-term usage for its augmented performance parameters. The performance parameters of the above technique are analyzed against other optimization techniques like conventional Teaching and Learning based optimization (TLBO), Particle Swarm Optimization (PSO). MATLAB/Simulink models the brushless DC motor and implements ATLBO, TLBO, and PSO algorithms. It has been found that the response obtained from ATLBO is comparatively much faster than other optimization techniques, which supports the motor for quick acceleration as well as more efficient in improving the step response characteristics such as rise time, settling time, and steady-state error in the speed control of a linear BLDC motor.

Control the Speed of Separately Excited Dc Motor Using Sliding Mode Controller

This study looks into how a Sliding Mode Controller (SMC) can be used to control a Separately Excited DC Motor's (SEDCM) speed. The SMC is utilized to improve the system's resilience to unknowns and disruptions, guaranteeing accurate control under changing operational circumstances. Extensive simulations and experimental settings confirm the suggested control technique, which shows improved performance in terms of stability, disturbance rejection, and speed tracking. The results further the development of DC motor control techniques, especially in situations where robustness and precision are crucial.