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This paper deals with the longitudinal movement control of an airplane (pitch angle) using fractional order adaptive controllers (FOACs). It shows the improvements achieved in the plane’s behavior, in terms of the minimization of a given performance index. At the same time, less control effort is needed to accomplish the control objectives compared with the classic integer order adaptive controllers (IOACs). In this study, fractional order direct model reference adaptive control (FO-DMRAC) is implemented at the simulation level, and exhibits a better performance compared with the classic integer order (IO) version of the DMRAC (IO-DMRAC). It is also shown that the proposed control strategy for FO-DMRAC reduces the resultant system control structure down to a relative degree 2 system, for which the control implementation is simpler and avoids oscillations during the transient period. Moreover, it is interesting to note that this is the first time that an FOAC with fractional adaptive laws is applied to the longitudinal control of an airplane. A suitable model for the longitudinal movement of the airplane related to the pitch angle θ as the output variable with the lifter angle (δe) as the control variable, is first analyzed and discussed to obtain a reliable mathematical model of the plane. All of the other input variables acting on the plane are considered as perturbations. For certain operating conditions defined by the flight conditions, an FO-DMRAC is designed, simulated, and analyzed. Furthermore, a comparison with the implementation of the classical adaptive general direct control (relative degree ≥ 2) is presented. The boundedness and convergence of all of the signals are theoretically proven based on the new Lemma 3, assuring the boundedness of all internal signals ω(t).

Particle swarm optimization (PSO) is one of the most famous swarm-based optimization techniques inspired by nature. Due to its properties of flexibility and easy implementation, there is an enormous increase in the popularity of this nature-inspired technique. Particle swarm optimization (PSO) has gained prompt attention from every field of researchers. Since its origin in 1995 till now, researchers have improved the original Particle swarm optimization (PSO) in varying ways. They have derived new versions of it, such as the published theoretical studies on various parameters of PSO, proposed many variants of the algorithm and numerous other advances. In the present paper, an overview of the PSO algorithm is presented. On the one hand, the basic concepts and parameters of PSO are explained, on the other hand, various advances in relation to PSO, including its modifications, extensions, hybridization, theoretical analysis, are included.

For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of the problem space. An efficient strategy for adjusting hyperparameters can be established with the use of the greedy search and Swarm intelligence algorithms. The Random Search and Grid Search optimization techniques show promise and efficiency for this task. The small population of solutions used at the outset, and the costly goal functions used by these searches, can lead to slow convergence or execution time in some cases. In this research, we propose using the machine learning model known as Support Vector Machine and optimizing it using four distinct algorithms—the Ant Bee Colony Algorithm, the Genetic Algorithm, the Whale Optimization, and the Particle Swarm Optimization—to evaluate the computational cost of SVM after hyper-tuning. Computational complexity comparisons of these optimization algorithms were performed to determine the most effective strategies for hyperparameter tuning. It was found that the Genetic Algorithm had a lower temporal complexity than other algorithms.

Speed control of DC motors is essential for automated vehicles and four-wheel differential drive (4WD) cars, which are distinct by their high level of maneuverability. The PID controller is one of the most popular techniques for controlling speed, but tuning its parameters is challenging. This paper presents a novel hybrid algorithm, the Moth-Flame Particle Swarm Optimization (MFPSO), which combines moth-flame optimization (MFO) and particle swarm optimization (PSO) to address the slow convergence of MFO and the premature convergence of PSO. The MFPSO is deployed for real-time interactive tuning of the PID controller to control the speed of DC motors in a 4WD car. Additionally, a novel practical procedure is proposed to build a robust four-wheel differential drive and maintain the synchronization of the four DC motors. Simulation results and statistical analysis demonstrate the superior performance of the MFPSO compared with the PSO, MFO, and other hybrid variants (HMFPSO and HyMFPSO), with MFPSO ranking first in the Friedman test on CEC2020/2021 and engineering optimization benchmark problems. Practical results and the transient response analysis of the speed control revealed that MFPSO significantly outperformed the traditional Ziegler-Nichols (ZN) method, MFO, PSO, HMFPSO, and HyMFPSO algorithms. Specifically, the MFPSO algorithm reduced settling time by 34.83%, 21.20%, 20.75%, 22.97%, and 31.59%, and overshoot by 86.11%, 64.99%, 71.02%, 74.37%, and 60.58% compared to the ZN, MFO, PSO, HMFPSO, and HyMFPSO algorithms, respectively. The source code of the proposed algorithm is available at https://github.com/MohamedRedaMu/MFPSO-Algorithm .

Social networks exhibit interactions that lead to event changes in their communities. It is imperative to track community events to understand an extensive social network. Recently, several models reported that the randomness and sparsity of social networks bring significant challenges in predicting community events. Hence, the proposed work extracts both community and temporal features to predict the events effectively that a community might experience. Machine learning (ML) models are widely used in predicting such events in a social network. Many machine learning models, such as naive Bayes, random forest, logistic regression, SVM, neural networks, etc., are used to predict community events. Further, the model’s performance is enhanced using hyper-parameter tuning by selecting the appropriate parameters. Evolutionary algorithms are effective in tuning these hyper-parameters. This paper investigates the effectiveness of Cuckoo search optimization (CSO), particle swarm optimization (PSO), ant colony optimization (ACO), jellyfish search optimization (JFO), and mayfly optimization (MFO) evolutionary algorithms in tuning the hyper-parameters of four ML models to achieve higher accuracy in the results. The comparative analysis of these 20 combinations (five evolutionary algorithms and four ML models) shows that CSO improves average accuracy by 4.12% in all the machine learning models compared to PSO, ACO, JFO, and MFO. Furthermore, results confirm that CSO precisely suits the neural network model in tuning its hyper-parameters. The accuracy of the neural network model improved by 4.5% after tuning its hyper-parameters using CSO.

With the advancement in the technology of power electronics and microelectronics, the use of brushless DC motor has been increasingly expanded. The numerous benefits of brushless DC motor are one of the prime factors that has helped in the rapid expansion in the use of BLDC motor. Simple operation and maintenance, compact volume, light weight, energy conservation, high efficiency, easy regulation of speed and high reliability are some the merits that promotes the increasing use of BLDC motor. Its current application is observed in almost all the sectors of industrial control. BLDC motor drivers are most widely used in motion control applications. With the increasing necessity of designing effective control strategy, the use of electronically commuted brushless DC motors is increasing rapidly in various industrial applications. For this purpose, cheap and efficient speed regulator is needed for the motor. Proper tuning of PID can also be one of the ways of controlling BLDCM. This paper adopts intelligent controlling technique for the reduction of torque ripple, settling time and overshoot through the use of MATLAB and SIMULINK.

The presence of toxic gas emissions from conventional vehicles is worrisome globally. Over the past few years, there has been a broad adoption of electric vehicles (EVs) to reduce energy usage and mitigate environmental emissions. The EVs are characterized by limited range, cost, and short range. This prompts the need for hybrid electric vehicles (HEVs). This study describes the conversion of a 2022 Volkswagen Crafter (VW) 35 TDI 340 delivery van from a conventional diesel powertrain into a hybrid electric vehicle (HEV) augmented with synchronous electrical machines (motor and generator) and a BMW i3 60 Ah battery pack. A downsized 1.5 L diesel engine and an electric motor–generator unit are integrated via a planetary power split device supported by a high-voltage lithium-ion battery. A MATLAB (R2024b) Simulink model of the hybrid system is developed, and its speed tracking PID controller is optimized using genetic algorithm (GA) and particle swarm optimization (PSO) methods. The simulation results show significant efficiency gains: for example, average fuel consumption falls from 9.952 to 7.014 L/100 km (a 29.5% saving) and CO2 emissions drop from 260.8 to 186.0 g/km (a 74.8 g reduction), while the vehicle range on a 75 L tank grows by ~40.7% (from 785.7 to 1105.5 km). The optimized series–parallel powertrain design significantly improves urban driving economy and reduces emissions without compromising performance.

Parameter tuning for Active Disturbance Rejection Control (ADRC) in rolling mill hydraulic synchronization systems is critical for enhancing strip quality. Conventional manual trial-and-error methods often yield suboptimal results. This paper proposes a hybrid algorithm, WMA-PSO, integrating the Humpback Whale Migration Algorithm (WMA) with Particle Swarm Optimization (PSO) through an adaptive fusion weight strategy. This approach effectively balances global exploration and local exploitation, improving optimization accuracy and efficiency. Evaluation on the CEC-2005 benchmark suite shows that WMA-PSO outperforms several state-of-the-art algorithms. Simulation experiments on ADRC tuning in a rolling mill system demonstrate that the WMA-PSO-optimized controller achieves the smallest synchronization error and superior overall control performance compared to other methods. The results validate WMA-PSO as an effective tool for automated parameter tuning in complex industrial control systems.

Optimizing engineering design problems, such as aerodynamic design, is computationally demanding due to the high cost and time required for numerical simulations. This study introduces an innovative approach to speed up this process by combining machine learning and optimization algorithms. A reduced-data neural network model is developed to predict key performance coefficients, specifically targeting the pitching moment and normal force coefficients. By strategically applying dataset reduction techniques and noise injection, we achieve over 58% reduction in the number of numerical simulations needed for training. This optimized neural network is then integrated into a design optimization framework. The particle swarm optimization (PSO) algorithm explores the design space and efficiently finds optimal solutions. The combined use of the reduced-data neural network and PSO significantly lowers computational cost and time while maintaining high accuracy. The model demonstrates a prediction error under 1% compared to high-fidelity numerical simulations. This work emphasizes the effectiveness of merging machine learning and optimization methods to improve computational efficiency in complex engineering design tasks, enabling faster and more effective exploration of design options. Graphical abstract

Inverted pendulum systems (IPSs) are mostly used to demonstrate the control rules for keeping the pendulum at a balanced upright position due to a slight force applied to the cart system. This paper presents an application for nonlinear control of an x-z type IPS by using a proportional-integral-derivative (PID) controller with newly established evolutionary tuning method Lightning Search Algorithm (LSA). A single, double and triple PID controller system is tested with the conventional and the self-tuning controllers to get a better understanding of the performance of the given system. The mathematical modelling of the x-z type IPS, the theoretical explanation of the LSA and the simulation analysis of the x-z type IPS is put forward entirely. The LSA algorithm solves the optimization problem of PID controller in an evolutionary way. The most effective way of making comparisons is evaluating the performance results with a well-known optimization technique or with the previous accepted results. We have compared the system’s performance with particle swarm optimization and with a classical control study in the literature. According to the simulation results, LSA-tuned PID controller has the ability to decrease the overshoot better than the conventional-tuned controllers. Finally, it can be concluded that the LSA-supported PID can better stabilize the pendulum angle and track the reference better for non-disturbed and the slightly disturbed systems.