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This paper introduces a novel multi-stage FOPD(1 + PI) controller for DC motor speed control, optimized using the Pelican Optimization Algorithm (POA). Traditional PID controllers often fall short in handling the complex dynamics of DC motors, leading to suboptimal performance. Our proposed controller integrates fractional-order proportional-derivative (FOPD) and proportional-integral (PI) control actions, optimized via POA to achieve superior control performance. The effectiveness of the proposed controller is validated through rigorous simulations and experimental evaluations. Comparative analysis is conducted against conventional PID and fractional-order PID (FOPID) controllers, fine-tuned using metaheuristic algorithms such as atom search optimization (ASO), stochastic fractal search (SFS), grey wolf optimization (GWO), and sine-cosine algorithm (SCA). Quantitative results demonstrate that the FOPD(1 + PI) controller optimized by POA significantly enhances the dynamic response and stability of the DC motor. Key performance metrics show a reduction in rise time by 28%, settling time by 35%, and overshoot by 22%, while the steady-state error is minimized to 0.3%. The comparative analysis highlights the superior performance, faster response time, high accuracy, and robustness of the proposed controller in various operating conditions, consistently outperforming the PID and FOPID controllers optimized by other metaheuristic algorithms. In conclusion, the POA-optimized multi-stage FOPD(1 + PI) controller presents a significant advancement in DC motor speed control, offering a robust and efficient solution with substantial improvements in performance metrics. This innovative approach has the potential to enhance the efficiency and reliability of DC motor applications in industrial and automotive sectors.

Two learning-based methods were employed in this study to control DC motor speeds. The first method is one of the data-driven control strategies. It identifies the system and learns the motor characteristics by evaluating the input-output data of each motor. In contrast to traditional methods, which require distinct models for each motor, the second method uses a single dataset, offering a generalized solution across many DC motor characteristics. Imitation learning (IL) was used to develop this model. IL is a type of machine learning where an expert system is imitated to learn a given task. The expert system was designed using fuzzy logic control principles, and an observation-action dataset was generated. This dataset presents the optimum response of DC motors to voltage changes and disturbances. An adaptive neuro-fuzzy inference system (ANFIS) was used as a supervised learning method. Unlike simulation-based studies, this study analyzes controllers in real-world scenarios, making it extremely relevant to industrial applications. Real-time experiments with three motors of different characteristics revealed that the IL-based controller significantly outperformed the data-driven method in terms of robustness, stability, and transient response metrics.

The increasing adoption of high-performance DC motor control in embedded systems has driven the development of cost-effective solutions that extend beyond traditional software-based optimization techniques. This work presents a refined hardware-centric approach implementing real-time particle swarm optimization (PSO) directly executed on STM32 microcontroller for DC motor speed control, departing from conventional simulation-based parameter-tuning methods. Novel hardware-optimized composition of an interval type-2 fuzzy logic controller (FLC) and a PID controller is developed, designed for resource-constrained embedded systems and accounting for processing delays, memory limitations, and real-time execution constraints typically overlooked in non-experimental studies. The hardware-in-the-loop implementation enables real-time parameter optimization while managing actual system uncertainties in controlling DC micro-motors. Comprehensive experimental validation against conventional PI, PID, and PIDF controllers, all optimized using the same embedded PSO methodology, reveals that the proposed FT2-PID controller achieves superior performance with 28.3% and 56.7% faster settling times compared to PIDF and PI controllers, respectively, with significantly lower overshoot at higher reference speeds. The proposed hardware-oriented methodology bridges the critical gap between theoretical controller design and practical embedded implementation, providing detailed analysis of hardware–software co-design trade-offs through experimental testing that uncovers constraints of the low-cost microcontroller platform.

This article describes the methodology of speed control by understanding control method of DC motor, definitely, armature and field resistances with additional to armature voltage control methods. The speed of DC motor is controlled PI controller as donor in this work. Using Matlab simulation and practical measurements, Terco DC motor speed control is achieved in this work. The results that obtained from Matlab simulation circuit is appeared approximately similar that obtained by practical connection.

This article proposes a nonlinear control strategy to address the voltage instability issue caused by the boost converter with an uncertain constant power load (CPL). This strategy combines a passivity-based controller (PBC) with a virtual DC motor controller (VDCM). Initially, a PBC is designed for the boost converter, which enhances the robustness of the converter with CPL perturbations in the DC bus voltage. To overcome the limitations of PBC, including steady-state errors resulting from variations in load or input voltage, the VDCM is incorporated, simulating the characteristics of a DC motor. This addition improves the system’s inertia and damping, making it more stable and significantly enhancing its dynamic performance. The efficacy and stability analysis of the proposed control strategy is validated through both simulation and experimentation.

This paper mathematically explains how state derivative space (SDS) system form with state derivative related feedback can supplement standard state space system with state related feedback in control designs. Practically, inverse optimal control is attractive because it can construct a stable closed-loop system while optimal control may not have exact solution. Unlike the previous algorithms which mainly applied state feedback, in this paper inverse optimal control are carried out utilizing state derivative alone in SDS system. The effectiveness of proposed algorithms are verified by design examples of DC motor tracking control without tachometer and very challenging control problem of singular system with impulse mode. Feedback of direct measurement of state derivatives without integrations can simplify implementation and reduce cost. In addition, the proposed design methods in SDS system with state derivative feedback are analogous to those in state space system with state feedback. Furthermore, with state derivative feedback control in SDS system, wider range of problems such as singular system control can be handled effectively. These are main advantages of carrying out control designs in SDS system.

DC motors are widely used in mechatronic systems; however, their performance degrades significantly in the presence of nonlinear mechanical loads, parameter variations and sensing uncertainties. This paper proposes three control strategies (i.e., PID, optimal, and hybrid controllers) for discrete-time DC motor systems to overcome the disturbances caused by nonlinear mechanical loads and parameter variations. Optimal control of nonlinear discrete-time systems is formally characterized by the Hamilton–Jacobi–Bellman (HJB) equation, whose analytical solution is generally intractable. To address this challenge, a learning-based optimal control strategy based on the Heuristic Dynamic Programming (HDP) framework is developed to approximate the HJB equation, supported by a formal convergence proof. For that purpose, Neural Networks (NNs) are employed to approximate both the cost function and the optimal control policy, enabling near-optimal performance with manageable computational complexity. Although the resulting optimal control achieves fast convergence, it may introduce overshoot and steady-state offset under nonlinear disturbances. To address this limitation, a hybrid control framework is proposed, where nonlinear optimal corrections are integrated with the robustness and adaptability of Proportional–Integral–Derivative (PID) control through error-dependent gating and gain-scheduling mechanisms. A structured evaluation framework is conducted, including nominal analysis, motor-parameter stress testing across nine nonlinear scenarios, controller-design sensitivity analysis, and stochastic measurement-noise assessment under filtered sensing conditions. Results demonstrate that the hybrid controller preserves transient speeds within 5–10% of the optimal controller while effectively eliminating overshoot and steady-state offset under nominal conditions. The hybrid design reduces the accumulated tracking error by more than 95% compared to the optimal controller, while incurring only negligible additional control effort. Under aggressive supply-sag disturbances, the hybrid controller significantly limits peak deviation and reduces accumulated tracking error by over 90%, while maintaining comparable control cost. Overall, the hybrid framework provides a convergence-proven and practically deployable control solution that combines near-optimal convergence speed with robust, overshoot-free performance for intelligent motion-control and robotics applications.

This paper presents a systematic approach for designing an electric motor drive system (EMDS) for a permanent magnet DC motor to achieve precise speed regulation using a classical PID controller. Smooth voltage trajectory planning based on Bézier curves is employed to mitigate high voltage and current peaks during step speed transitions, improving dynamic performance, reducing electrical stress, and making the control system physically realizable. A comparative evaluation of inverting buck–boost, positive buck–boost, and quadratic DC–DC converters is conducted using the same motor and controller, enabling the identification of the most suitable controller–converter pairing. Experimental results demonstrate that, with an appropriate converter topology and voltage trajectory, peak voltages and currents are significantly reduced, resulting in a smoother control action and reliable speed regulation without the need for complex control schemes.

Some coastal cities are sometimes exposed to floods, mainly caused by strong winds or earthquakes on the seafloor. This causes the water waves heading to the coastal cities to rise quickly, possibly destroying civilization. The proposed study will introduce an intelligent rubber balloon that automatically acts as a water repellent to absorb the momentum of water hammers from the sea. The proposed system has been energized by wind power energy. This enables the control of the bus voltage of the DC link. Sequential balloons could be arranged in such a matter to form a repel flood wall. Wind turbine generators could be used for charging the storage batteries. These batteries energize the smart control system and DC motors coupled with air pumps. These pumps are used to inflate the sequential air balloons. The theoretical models of the proposed system components have been simulated by MATLAB environment. Three DC motors are connected based on the master-salve mechanism, and the third is considered in standby mode. These motors are controlled by a model reference adaptive controller. The tracking speed between reference and measured speeds has been accomplished. Control of switching ON-OFF balloons using fuzzy logic control and classical control has been compared. After using several control scenarios for air pressure in balloons, it is observed that the best response is obtained using fuzzy logic control since it reduces the setting time and faster time response compared to the classical PID controller. Also, it was noticed that time response improved when using a PID controller instead of proportional or PD control scenarios, and the system dynamic response became acceptable.