Search Results (183)

This paper employs the Direct Synthesis approach to present an analytical methodology for designing fractional-order controllers, aiming to balance simplicity and robustness for practical industrial implementation. Although significant progress has been made in developing fractional-order PID controllers, the advancement of Direct Synthesis controllers has been comparatively slower. This study underscores the importance of further research on these controllers and the need for innovative approaches to enhance parameter adjustment. The proposed methodology is based on the fractional “second-order” transfer function and the solution of two equations derived from four key specifications: overshoot, settling time, and the frequency and magnitude of disturbance rejection. Additionally, the fractional order should be chosen as close as possible to 1, ensuring practical implementation and minimizing the system’s sensitivity to parameter variations. The resulting controller demonstrates strong robustness against plant parameter variations, input noise, and disturbances while achieving shorter settling times and lower overshoot. It outperforms fractional-order PID and ID controllers optimized numerically and surpasses integer-order phase lead-lag compensators designed analytically. The validation process involved Monte Carlo simulations and Kruskal–Wallis statistical analysis on a complex system characterized by closely spaced poles and significant parametric variations. Furthermore, the proposed controller effectively reduces the integral of the control signal (control effort), enhancing energy efficiency. Full article

A variable fractional-order (VFO) fuzzy sliding mode controller is designed to control the speed of a permanent magnet synchronous motor (PMSM). First, a VFO sliding mode surface is established. Then, a VFO fuzzy sliding mode controller is designed, capable of suppressing the effects of parameter uncertainties and disturbances to achieve precise PMSM speed control. The global stability and finite time convergence of the controlled system state are demonstrated using Lyapunov stability theory. The numerical and experimental results validate the effectiveness of the controller, showing better immunity to disturbances and a smaller overshoot compared to PID and fixed-order fuzzy sliding mode controllers. Full article

This study demonstrates that the Grünwald–Letnikov fractional proportional–integral–derivative (GPID) controller outperforms traditional PID controllers in adaptive cruise control systems, while conventional PID controllers struggle with nonlinearities, dynamic uncertainties, and stability, the GPID enhances robustness and provides more precise control across various driving conditions. Simulation results show that the GPID improves the accuracy, reducing errors better than the PID controller. Additionally, the GPID maintains a more consistent speed and reaches the target speed faster, demonstrating superior speed control. The GPID’s performance across different fractional orders highlights its adaptability to changing road conditions, which is crucial for ensuring safety and comfort. By leveraging fractional calculus, the GPID also improves acceleration and deceleration profiles. These findings emphasize the GPID’s potential to revolutionize adaptive cruise control, significantly enhancing driving performance and comfort. Numerical results obtained in α=0.99 from the GPID controller have shown better accuracy and speed consistency, adapting to road conditions for improved safety and comfort. The GPID also demonstrated faster stabilization of speed at 60 km/h with smaller errors and reduced the error to 0.59 km/h at 50 s compared to 0.78 km/h for the PID. Full article

As high-performance drives, asynchronous motor (AM) drives find extensive use in electric cars, elevators, and machine tools. For these applications, AM drives with direct torque control (DTC) are typically chosen over AM drives with field-oriented control because of their simplicity and quick torque control. Direct torque control of AM drives is frequently achieved using proportional–integral–derivative (PID) controllers. With variable set points and AM parameter ambiguity, these controllers perform poorly. New controllers called fractional-order controllers (FOCs) offer notable improvements over traditional PID controllers due to their enhanced flexibility, robustness, and fine control. In order to provide fast torque performance, this research suggests an AM drive that is regulated by direct torque control theory; nevertheless, the inverter control is optimized for fast response. On the other hand, by employing an optimized fractional-order PI (FOPI) controller, the AM drive speed response is enhanced. The particle swarm optimization (PSO) algorithm is used to optimize the FOPI’s parameters. The MATLAB/Simulink platform was used to model every part of the AM drive with the optimized control system. Three distinct controllers—optimized FOPI, standard PI, and optimized PI—were used to compare the performances of the introduced drive. According to the simulation results, the optimum response in terms of torque and speed was offered by the optimized FOPI controller. The average improvement in the settling time is about 84.4%, and that in the steady-state error is almost killed for all disturbances using the proposed optimized FOPI controller. Furthermore, under parameter uncertainties, the AM’s performance using the suggested optimized FOPI was examined. The outcomes of the simulation demonstrated how resilient the optimized FOPI controller was to changes in the parameters. Full article

In recent decades, there have been many studies on Hopf bifurcation and population stability with time delay. However, the stability and Hopf bifurcation of fractional-order population systems with time delay are lower. In this paper, we discuss the dynamic behavior of a fractional-order three-population model with pregnancy delay using Laplace transform of fractional differential equations, stability and bifurcation theory, and MATLAB software. The specific conditions of local asymptotic stability and Hopf bifurcation for fractional-order time-delay systems are determined. A fractional-order proportional–integral–derivative (PID) controller is applied to the three-population food chain system for the first time. The convergent speed and vibration amplitude of the system can be changed by PID control. For example, after fixing the values of the integral control gain ki and the differential control gain kd, the amplitude of the system decreases and the convergence speed changes as the proportional control gain kp decreases. The effectiveness of the PID control strategy in complex ecosystem is proved. The numerical simulation results are in good agreement with the theoretical analysis. The research in this paper has potential application values concerning the management of complex population systems. The bifurcation theory of fractional-order time-delay systems is also enriched. Full article

This study addresses the load frequency control (LFC) within a multiarea power system characterized by diverse generation sources across three distinct power system areas. area 1 comprises thermal, geothermal, and electric vehicle (EV) generation with superconducting magnetic energy storage (SMES) support; area 2 encompasses thermal and EV generation; and area 3 includes hydro, gas, and EV generation. The objective is to minimize the area control error (ACE) under various scenarios, including parameter variations and random load changes, using different control strategies: proportional-integral-derivative (PID), two-degree-of-freedom PID (PID-2DF), fractional-order PID (FOPID), fractional-order integral (FOPID-FOI), and fractional-order integral and derivative (FOPID-FOID) controllers. The result analysis under various conditions (normal, random, and parameter variations) evidences the superior performance of the FOPID-FOID control scheme over the others in terms of time-domain specifications like oscillations and settling time. The FOPID-FOID control scheme provides advantages like adaptability/flexibility to system parameter changes and better response time for the current power system. This research is novel because it shows that the FOPID-FOID is an excellent control scheme that can integrate these diverse/renewable sources with modern systems. Full article

In this paper, fractional calculus is used to develop a generalized fractional dynamic model of an electrohydraulic system composed of a servo valve and a hydraulic cylinder, where a fractional position controller PIγDμ is proposed for minimizing the performance index according to the integral of the time-weighted absolute error (ITAE). First, the general mathematical equations of the cylinder and servo valve are used to obtain the transfer functions in fractional order by applying Caputo’s definition and a Laplace transform. Then, through a block diagram of the closed-loop system without a controller, the fractional model is validated by comparing its performance concerning the integer-order electrohydraulic system model reported in the literature. Subsequently, a fractional PID controller is designed to control the cylinder position. This controller is included in the closed-loop system to determine the fractional exponents of the transfer functions of the servo valve, cylinder, and control, as well as to tune the controller gains, by using the ITAE objective function, with a comparison of the following: (1) the electrohydraulic system model in integer order and the controller in fractional order; (2) the electrohydraulic system model in fractional order and the controller in integer order; and (3) both the system model and the controller in fractional order. For each of the above alternatives, numerical simulations were carried out using MATLAB^®/Simulink^® R2023b and adding white noise as a perturbation. The results show that strategy (3), where electrohydraulic system and controller model are given in fractional order, develops the best performance because it generates the minimum value of ITAE. Full article

Heat pump-based renewable energy and waste heat recycling have become a mainstay of sustainable heating. Still, configuring an effective control system for these purposes remains a worthwhile research topic. In this study, a Smith-predictor-based fractional-order PID cascade control system was fitted into an actual clean heating renovation project and an advanced fireworks algorithm was used to tune the structural parameters of the controllers adaptively. Specifically, three improvements in the fireworks algorithm, including the Cauchy mutation strategy, the adaptive explosion radius, and the elite random selection strategy, contributed to the effectiveness of the tuning process. Simulation and field investigation results demonstrated that the fitted control system counters the adverse effects of time lag, reduces overshoot, and shortens the settling time. Further, benefiting from a delicate balance between heating demand and supply, the heating system with upgraded management increases the average exergetic efficiency by 11.4% and decreases the complaint rate by 76.5%. It is worth noting that the advanced fireworks algorithm mitigates the adverse effect of capacity lag and simultaneously accelerates the optimizing and converging processes, exhibiting its comprehensive competitiveness among this study’s three intelligent optimization algorithms. Meanwhile, the forecast and regulation of the return water temperature of the heating system are independent of each other. In the future, an investigation into the implications of such independence on the control strategy and overall efficiency of the heating system, as well as how an integral predictive control structure might address this limitation, will be worthwhile. Full article

This paper proposes a fractional order integral-derivative plus second-order derivative with low-pass filters and a tilt controller called I^λDND²N²-T to improve the control performance of an automatic voltage regulator (AVR). In this study, equilibrium optimisation (EO), multiverse optimisation (MVO), and particle swarm optimisation (PSO) algorithms are used to optimise the parameters of the proposed controller and statistical tests are performed with the data obtained from the application of these three algorithms to the AVR problem. Afterwards, the performance of the I^λDND²N²-T controller is demonstrated by comparing the transient responses with the results obtained in recently published papers. In addition, extra disturbances such as frequency deviation, load variation, and short circuit faults in the generator are applied to the AVR system. The proposed controller has outperformed the compared controller against these disturbances. Finally, a robustness test is performed in terms of deterioration in the system parameters. The results show that the I^λDND²N²-T controller outperforms the compared controllers under all conditions and exhibits a robust effect on the perturbed system parameters. Full article

The hovering flight of a quadrotor Unmanned Aerial Vehicle (UAV) refers to maintaining the aircraft in a fixed position in the air, without lateral, vertical, or rotational movements, using only the vehicle’s control systems to maintain proper balance in all spatial dimensions. Algorithms and control systems have been developed to continuously adjust motor speeds to counteract deviations from the desired position and achieve effective hovering flight. This paper proposes a set of PID controllers with an integral effect based on the Riemann–Liouville fractional-order approach to improve the hovering flight of a quadrotor UAV. This research innovates by introducing a set of fractional-order PID controllers for UAV hover stability, which offer better adaptability to non-linear dynamics and robustness than traditional PID controllers. Also presented is the development of new performance metrics (MSE, BQC-LR), which allow for more comprehensive control system evaluations. A thorough comparative analysis with conventional control methods demonstrates the superior performance of fractional-order control in real-world simulations. The numerical simulation results show the effectiveness of the proposed Fractional Integral Action PID Controller in the control of UAV hovering flight, while comparative analyses against a classical controller emphasize the benefits of the fractional-order approach in terms of control accuracy. Full article

This paper introduces a robust system designed to effectively manage and enhance the electrical output of a Wind Energy Conversion System (WECS) using a Cascaded Doubly Fed Induction Generator (CDFIG) connected to a power grid. The solution that was investigated is the use of a CDFIG that is based on a variable-speed wind power conversion chain. It comprises the electrical and mechanical connection of two DFIGs through their rotors. The originality of this paper lies in the innovative application of a fuzzy logic controller (FLC) in combination with a CDFIG for a WECS. To demonstrate that this novel configuration enhances control precision and performance in WECSs, we conducted a comparison of three different controllers: a proportional–integral (PI) controller, a fractional PID (FPID) controller, and a fuzzy logic controller (FLC). The results highlight the potential of the proposed system in optimizing power generation and improving overall system stability. It turns out that, according to the first results, the FLC performed optimally in terms of tracking and rejecting disturbances. In terms of peak overshoot for power and torque, the findings indicate that the proposed FLC-based technique (3.8639% and 6.9401%) outperforms that of the FOPID (11.2458% and 10.9654%) and PI controllers (11.4219% and 11.0712%), respectively. These results demonstrate the superior performance of the FLC in reducing overshoot, providing better control stability for both power and torque. In terms of rise time, the findings show that all controllers perform similarly for both power and torque. However, the FLC demonstrates superior performance with a rise time of 0.0016 s for both power and torque, compared to the FOPID (1.9999 s and 1.9999 s) and PI (0.0250 s and 0.0247 s) controllers. This highlights the FLC’s enhanced responsiveness in controlling power and torque. In terms of settling time, all three controllers have almost the same performance of 1.9999. An examination of total harmonic distortion (THD) was also employed to validate the superiority of the FLC. In terms of power quality, the findings prove that a WECS based on an FLC (0.93%) has a smaller total harmonic distortion (THD) compared to that of the FOPID (1.21%) and PI (1.51%) controllers. This system solves the problem by removing the requirement for sliding ring–brush contact. Through the utilization of the MATLAB/Simulink environment, the effectiveness of this control and energy management approach was evaluated, thereby demonstrating its capacity to fulfill the objectives that were set. Full article

This research introduces a fractional high-order sliding mode control (FHOSMC) method that utilises an inverse integral fractional order, 0<β<1, as the high order on the FHOSMC reaching law, exhibiting a novel contribution in the related field of study. The application of the proposed approach into a bioreactor system via diffeomorphism operations demonstrates a notable improvement in the management of the bioreactor dynamics versus classic controllers. The numerical findings highlight an improved precision in tracking reference signals and an enhanced plant stability compared to proportional–integral–derivative (PID) controller implementations within challenging disturbance scenarios. The FHOSMC effectively maintains the biomass concentration at desired levels, reducing the wear of the system as well as implementation expenses. Furthermore, the theoretical analysis of the convergence within time indicates substantial potential for further enhancements. Subsequent studies might focus on extending this control approach to bioreactor systems that integrate sensor technologies and the formulation of adaptive algorithms for real-time adjustments of β-type fractional-orders. Full article

Quadcopter drones have become increasingly popular because of their versatility and usefulness in various applications, such as surveillance, delivery, and search and rescue operations. Weather conditions and obstacles can undoubtedly pose challenges for drone flights, sometimes causing the loss of one or two propellers. This is a significant challenge as the loss of one or more propellers leads to a sudden loss of control, potentially resulting in a crash, which must be addressed through advanced control strategies. Therefore, this article develops and implements a fractional-order control algorithm to enhance quadrotor drones’ safety and resilience during propeller failure scenarios. The research encompasses the complexities of quadrotor dynamics, fractional-order control theory, and existing methodologies for ensuring safe drone landings. The study emphasizes case validation on experimental results, where four distinct cases were tested using PID and Fractional-order PID (FOPID) controllers. These cases involve various simulated failure conditions to assess the performance and adaptability of the developed control algorithms. The results show the proposed FOPID control’s superior robustness and adaptability compared to traditional PID controllers. These offer significant advancements in navigating dynamic environments and managing disruptive elements introduced during propeller failure simulations in drone control technology. Full article

The tuning of fractional-order proportional-integral-derivative (FOPID) controllers for automatic voltage regulator (AVR) systems presents a complex challenge, necessitating the solution of real-order integral and differential equations. This study introduces the Dumbo Octopus Algorithm (DOA), a novel metaheuristic inspired by machine learning with animal behaviors, as an innovative approach to address this issue. For the first time, the DOA is invented and employed to optimize FOPID parameters, and its performance is rigorously evaluated against 11 existing metaheuristic algorithms using 23 classical benchmark functions and CEC2019 test sets. The evaluation includes a comprehensive quantitative analysis and qualitative analysis. Statistical significance was assessed using the Friedman’s test, highlighting the superior performance of the DOA compared to competing algorithms. To further validate its effectiveness, the DOA was applied to the FOPID parameter tuning of an AVR system, demonstrating exceptional performance in practical engineering applications. The results indicate that the DOA outperforms other algorithms in terms of convergence accuracy, robustness, and practical problem-solving capability. This establishes the DOA as a superior and promising solution for complex optimization problems, offering significant advancements in the tuning of FOPID for AVR systems. Full article

This article studies the transformation and assembly process of the Volkswagen (VW) Crafter from conventional to hybrid vehicle of the department of vehicles engineering, University of Debrecen, and uses a computer-aided simulation (CAS) to design the vehicle based on the real measurement data (hardware-in-the-loop, HIL method) obtained from an online CAN bus data measurement platform using MATLAB/Simulink/Simscape and LabVIEW software. The conventional vehicle powered by a 6-speed manual transmission and a 4-stroke, 2.0 Turbocharged Direct Injection Common Rail (TDI CR) Diesel engine and the transformed hybrid electrified powertrain are designed to compare performance. A novel methodology is introduced using Netcan plus 110 devices for the CAN bus analysis of the vehicle’s hybrid version. The acquired raw CAN data is analyzed using LabVIEW and decoded with the help of the database (DBC) file into physical values. A classical proportional integral derivative (PID) controller is utilized in the hybrid powertrain system to manage the vehicle consumption and CO₂ emissions. However, the intricate nonlinearities and other external environments could make its performance unsatisfactory. This study develops the energy management strategies (EMSs) on the basis of enhanced proportional integral derivative-based genetic algorithm (GA-PID), and compares with proportional integral-based particle swarm optimization (PSO-PI) and fractional order proportional integral derivative (FOPID) controllers, regulating the vehicle speed, allocating optimal torque and speed to the motor and engine and reducing the fuel and energy consumption and the CO₂ emissions. The integral time absolute error (ITAE) is proposed as a fitness function for the optimization. The GA-PID demonstrates superior performance, achieving energy efficiency of 90%, extending the battery pack range from 128.75 km to 185.3281 km and reducing the emissions to 74.79 gCO₂/km. It outperforms the PSO-PI and FOPID strategies by consuming less battery and motor energy and achieving higher system efficiency. Full article