OpenMCT: an open-source DC motor control educational kit

This research aims to develop and realize a 2WD car robot that uses an Arduino UNO and is controlled by a wireless PS2 joystick. The method used in this research is experimental, which includes system design, hardware assembly, programming, and performance testing. The designed system integrates the joystick as input, Arduino for data processing, and the L298N motor driver to control DC motors. The test results showed that the robot could effectively carry out instructions from the joystick, such as moving forward, backward, turning, and stopping. The system's response time is relatively fast, approximately 1 second, allowing for real-time control. The wireless connection proved stable at distances between 1 and 5 meters, but its performance decreased at longer distances and was disconnected at around 10 meters. Based on these results, it can be concluded that the system has functioned according to the intended design, although it still. although it still has limitations in communication range. Therefore, it is recommended to use a communication module with a wider range and to add sensors to improve the robot's capabilities. This research is expected to serve as a reference in the development of microcontroller-based robotic systems.

Abstract The performance of a solar power system depends mainly on how effectively the panel receives sunlight during different times of the day. In most practical installations, solar panels are fixed in a single position, which limits their ability to capture maximum energy as the sun moves. To address this issue, a dual axis solar tracking system with IoT-based monitoring is developed in this work. The system is designed to automatically adjust the position of the panel in both horizontal and vertical directions based on the availability of sunlight. In this setup, Light Dependent Resistors (LDRs) are used to sense the intensity of light from different directions. The sensed values are processed by an Arduino Nano controller, which then controls DC motors through an L293D driver to rotate the panel accordingly. An ESP8266 module is used to transmit the output voltage to the Blynk mobile application, allowing the user to monitor the system and control connected loads remotely. From the testing results, it was observed that the system is able to track the sunlight effectively and maintain better alignment compared to fixed panels. The overall design is simple, low-cost, and suitable for small-scale applications where improved solar energy utilization is required. Keywords: Dual axis solar tracking, Arduino Nano, LDR sensor, ESP8266 Wi-Fi module, Blynk application, solar energy utilization, IoT monitoring, DC motor control.

Abstract This paper presents the design and analysis of a Grey Wolf Optimization (GWO)-based PI controller for a solar-fed Brushless DC (BLDC) motor with Incremental Conductance (INC) MPPT technique. Conventional MPPT methods such as Perturb and Observe (P&O) and PSO suffer from steady-state oscillations, slower tracking, and limited optimization performance. The proposed system integrates the INC MPPT technique with GWO-tuned PI controller for enhanced performance. The solar PV array is connected to a DC-DC boost converter and three-phase inverter feeding the BLDC motor. The INC MPPT method ensures accurate and fast maximum power point tracking with reduced oscillations under varying environmental conditions, while the GWO algorithm optimally tunes the PI controller parameters by minimizing the Integral of Absolute Error (IAE). MATLAB/Simulink simulation demonstrates 99.4% MPPT tracking efficiency, 1.0% steady-state speed error, 35ms settling time, and 38% reduction in IAE compared to conventional PSO-tuned controllers, validating the approach for solar-powered electric drives in water pumping and EV applications. Keywords: BLDC Motor, Solar PV, Grey Wolf Optimization, INC MPPT, PI Controller, Speed Control, Renewable Energy Drive

Performance and robustness analysis of maiden atom search optimizer driven tri-parametric PID-like fractional controller for speed control of DC motor

This paper addresses the speed control problem of a DC motor in the presence of nonlinearities, disturbances, and unmodeled dynamics by proposing a neural backstepping control scheme based on a Recurrent High-Order Neural Network (RHONN). The proposed RHONN serves as an online approximator to compensate for uncertain nonlinear dynamics in a PD-based backstepping controller, enabling the system to handle disturbances, modeling errors, and unmodeled dynamics. Instead of relying on the traditional Extended Kalman Filter (EKF) for RHONN weight adaptation, the neural parameters are updated online using a Super-Twisting Algorithm (STA). As a result, the proposed STA-based learning law provides a simpler and robust covariance-free adaptation mechanism with practical finite-time convergence properties, making it suitable for real-time embedded implementations. The proposed method was evaluated through numerical simulations and implemented on an embedded microcontroller to assess its real-time performance. Simulation results show reductions between 0.04% and 2.04% in steady-state and integral error metrics compared with a tuned PD controller, and improvements up to 25.66% and 23.82% over LQR and MPC in the IMSE index. Experimental results demonstrate good tracking performance, robustness under varying load conditions, and low computational requirements, confirming the practical feasibility.

The present study focuses on the development and evaluation of a solar-powered grass cutter as an eco-friendly alternative to conventional fuel-based machines. The system utilizes a 40 W solar panel, battery storage, DC motor, and charge controller to operate the cutting mechanism efficiently. The design emphasizes reduced fuel consumption, low maintenance cost, and minimized environmental impact. Experimental analysis was conducted by measuring temperature, voltage, and solar intensity at different time intervals to assess performance. Results indicated that the system performs effectively under varying sunlight conditions, with optimal output observed during peak solar hours. The developed prototype is suitable for agricultural fields, lawns, and institutional areas, offering a sustainable solution for grass cutting. Overall, the solar-powered grass cutter demonstrates significant potential in promoting renewable energy utilization and reducing dependence on fossil fuels.

Design and intelligent tuning of a proportional–integral–derivative controller for an armature-controlled DC motor

Introduction. This study proposes a finite-time robust control law for position tracking of a DC motor under conditions of model uncertainty and external disturbances. The motor operates through a pulse-width modulation (PWM) unit and an H-bridge power circuit, aiming to achieve finite-time position tracking while minimizing the effects of model uncertainties and external disturbances. Problem. The main challenge lies in achieving accurate and rapid position and speed regulation for the DC motor while maintaining high performance, despite model inaccuracies and external disturbances. The goal of this paper is to design a robust finite-time position tracking control law for a DC motor based on the differential geometric approach, ensuring high tracking accuracy and control efficiency in the presence of disturbances and parameter uncertainties. Scientific novelty. The integration of finite-time control based on a virtual system, diffeomorphism transformation, and disturbance compensation introduces an innovative solution for DC motor position tracking under incomplete modeling and external perturbations. Methodology. The study employs the differential geometric method to construct a virtual system with finite-time characteristics and uses Lyapunov theory to prove global stability in the presence of uncertainties and disturbances. A finite-time virtual system is proposed after analyzing the incomplete dynamic model of the DC motor. Results. To validate the proposed approach, MATLAB simulations were conducted and compared with a conventional sliding mode controller. The results demonstrate improved settling time and robustness of the proposed method in DC motor position tracking. The findings confirm that the proposed controller provides intuitive and precise control, accurate position tracking, and enhanced performance regulation. It also exhibits strong robustness against model uncertainties and external disturbances. The practical value of the proposed method is considerable, as it offers a reliable and efficient position control scheme for DC motors using PWM. The method ensures precise position control and robust performance under varying conditions and external interferences, making it well-suited for real-world DC motor control applications. References 23, tables 1, figures 12.

Levy Flight-Augmented Artificial Circulatory System Algorithm for optimal PID tuning in DC motor control

Sensorless Control of Brushed DC Motors: A Cost and Space Efficient Approach Using Sliding Mode and State Space Observers

This article addresses the data-driven asynchronous dynamic event-triggered <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> tracking control problem for singularly perturbed systems (SPSs) with unknown slow dynamics and unknown bounded disturbances. First of all, considering the two-time-scale characteristic of SPSs, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> tracking control problem for the slow subsystem and an asymptotic stability problem for the fast subsystem are formulated via time-scale decomposition. Secondly, an asynchronous dynamic event-triggered scheme (ETS) based on dual-rate sampling is proposed to reduce the communication burden. Then, a data-based parameterized model of the augmented system is given, which consists of the reference system and the slow subsystem with unknown dynamics. Further, by combining the data-based parameterized model and employing the full-block S-procedure, the data-based co-design method of tracking controller and event-triggered matrix is developed. The overall stability analysis of the full system under the composite controller is given. Finally, the proposed scheme is verified by a Chua’s circuit and a networked DC motor control system.

The research is focused on developing an algorithm of choice and use of different types of PID controllers, and the tuning of its settings to simulate the word and control the speed and position of a DC motor (direct current motor).After analyzing the characteristics of the DC motor and speed and position sensors of the system (typically an encoder) the researchers were able to ensure safe operating conditions and provide the experimenter with data about the limits of the motor, its capabilities and optimal working conditions. As equipment the QNET DCMCT DC motor control trainer was used. A step response test was conducted during the research to evaluate system stability based on its reaction to a step input signal. The Ziegler-Nichols method is recommended for controlling the position of a DC motor. This two-step tuning method involves performing a test to quantitatively assess the system’s behavior in terms of how quickly and to what extent the process variable changes with alterations in the control input. The results of these tests are used in empirical formulas to determine appropriate controller settings for desired performance. The method involves determining the critical period Tu (ultimate period) and the critical gain Pu (Ku) (ultimate gain). The information and measurement complex with PID controller temporarily disables its algorithm, replacing it with an ON/OFF relay, causing the process variable to oscillate. These obtained values describe the process behavior to guide PID tuning for the desired closed-loop performance. The method involves determining the critical period Tu (ultimate period) and the critical gain Pu (Ku) (ultimate gain).

This paper presents an innovative and accessible hands-free wheelchair control system designed for individuals with severe motor impairments, particularly tetraplegic users. Unlike traditional joystick-based systems, which are often unsuitable for users with quadriplegia, our system relies on intuitive head-motion detection to enhance autonomy and ease of use. The system consists of a wearable motion-sensing cap equipped with an MPU-6050 sensor which is a 6-axis Inertial Measurement Unit (IMU) to capture head gestures, processed by an ATmega328 microcontroller (a low-power 8-bit AVR microcontroller widely used in embedded systems) integrated on an Arduino Nano development board. Wireless commands are transmitted via a Bluetooth module (HC-05) to the wheelchair's control unit consisting of an Arduino Uno microcontroller and BTS7960 motor drivers - high-power H-bridge modules that enable bidirectional control of DC motors. The operational flow, including signal processing, gesture interpretation, and wireless transmission, is structured following a detailed flowchart-based design. Experimental results indicate a high response rate and directional accuracy of over 90 % using a 45° head tilt. The optimal safe speed was determined to be 1.87 km/h with a Pulse Width Modulation (PWM) value of 180. Rather than designing a mechanical chassis from scratch, a commercially available electric wheelchair was modified by removing its joystick interface, allowing seamless integration of the head-controlled system. These findings validate the system's usability and precision under real-world conditions. By eliminating manual input and emphasizing simplicity, the proposed solution holds strong potential as a scalable and low-cost mobility aid, especially in low-resource environments.

4-D chaotic system synchronization for secure wireless control of DC motor using fuzzy brain-inspired neural network controller and disturbance observer

In today's world, the rise in global warming is driving a shift towards electric mobility. The progress in battery technology and power electronic devices has facilitated the transition of vehicles from being powered by traditional internal combustion engines to electric motors. The types of motors utilized for propulsion include DC motors, three-phase induction motors, permanent magnet synchronous motors (PMSM), and brushless DC motors (BLDC). Among them, the BLDC motor, when paired with a suitable control algorithm, proves to be the most suitable option for electric vehicle applications. The existing control algorithms for BLD motors are quite complex. Therefore, this study presents the development of an innovative and simple digital control algorithm based on a combinational logic circuit to drive the BLDC motor under motoring and regenerative braking mode. The proposed control algorithm and its effectiveness are validated by simulating it using Xilinx &amp;amp; Proteus software and experimenting with the concept in hardware by utilizing a PIC microcontroller. The proposed control algorithm forms a cost-effective alternative for BLDC motor speed control.

Abstract To address the problems of difficult parameter adjustment, large speed fluctuations, slow response, and low accuracy in traditional motor control algorithms under complex conditions, this study applies the immune prediction algorithm to DC motor control. This algorithm includes operations such as initialization and affinity calculation. It builds a mathematical model based on the DC motor armature and mechanical equations, uses the ITAE index as the objective function, and optimizes parameters through real number encoding, clone variation, etc. The MATLAB/Simulink simulation shows that after optimization, the overshoot of the motor step response is 32.6%, the adjustment time is 1.2 seconds, and the steady-state error is ±0.8 rad/s. The performance is superior to conventional and fuzzy PID. In actual tests, the control performance at the end of the mechanical arm is good, but it requires high processor performance for multi-link control of complex equipment, and there is still room for optimization. This provides a new idea for motor modeling and improves the model accuracy and adaptability.

The classical controller design methods, often lead to sub-optimal performance, especially when implemented for plants exhibiting complex dynamics like integrals, non-minimum phase zeros, time-delays, etc.; and the controllers synthesised using classical methods can result in poor time domain characteristics, and limited robustness. Thus, it is essential to formulate the controller synthesis methods that improve stability, dynamic performance, and robustness. Proposed design explores the synthesis of optimal and robust controllers by posing the controller synthesis as a multi-objective optimization problem; wherein objectives of peak sensitivity, minimization of integral square error and control effort, along with phase margin penalty and delay margins are considered while formulating the objective function; followed by solving it by multi-objective genetic algorithm. Following the synthesis, a set of Pareto-optimal solutions is generated; to identify the ideal controller from these solutions, K-Means clustering is applied along with the determination of the utopia point for controller selection. The work is implemented for four systems like (a) integrating system, (b) position control of DC motor, (c) non-minimum phase hydropower system and (d) coupled tank systems. The proposed controller demonstrates significant quantitative improvement of performance metrics across all systems when compared to conventional methods. Additionally, Monte Carlo simulations for the robustness analysis are included to establish the superiority of the proposed method over the conventional.

This paper presents a discrete delta-domain fractional-order PID (FOPID) controller, tuned using dynamic Particle Swarm Optimization (dPSO), for precise DC motor speed control. The proposed approach directly discretizes the FOPID controller in the delta domain, ensuring improved numerical stability and continuous-time-like performance even at fast sampling rates. The controller is optimized for time-domain criteria and implemented on an Atmega328P microcontroller, with comprehensive validation via both simulation and hardware-in-the-loop (HIL) experiments. Compared with the conventional z-domain FOPID and PID controllers, the delta-domain design achieved up to 60% reduction in overshoot and 40% improvement in settling time, while maintaining steady-state error below 1%. These results confirm the practical viability of advanced digital fractional-order control for real-time industrial applications.

Design and optimization of PID controller for Arduino Uno-based DC motor with encoder feedback and signal conditioning using LM324 op-amp. The method used is Ziegler–Nichols (ZN) to obtain the initial parameters Kp, Ki, and Kd from the measurement of ultimate gain (Ku) and oscillation period (Pu). The plant model uses a digital implementation of a DC motor including anti-windup, first-order filtered derivatives, and PWM voltage limiting. The test data in the form of step response, disturbance rejection, and sensitivity to noise are analyzed using rise time, overshoot, settling time, IAE, and ISE indicators. The analysis results of Ku≈11.3 and Pu≈0.3 s produce Kp≈6.80, Ki≈45.31, Kd≈0.25, with fast response and acceptable overshoot; fine-tuning reduces oscillation and accelerates steady-state time. The conclusion is that the LM324 encoder improves feedback accuracy, while ZN is effective as a starting point for tuning for stable and robust performance. Highlights: Accurate tuning using Ziegler–Nichols method provides effective initial PID parameters. LM324 signal conditioning enhances encoder feedback precision. Optimized response achieves fast rise time with minimal overshoot and stable steady-state. Keywords: PID, Ziegler–Nichols, motor DC, encoder, LM324