Operating pressurized water reactor (PWR) nuclear power plants requires effective regulation of the control rod speed to achieve the desired reactor power demand. However, the presence of time delay in the control rod speed due to latency in communication and aging can lead to output power oscillation and instability. This article proposes a backstepping fractional-order proportional-integral-derivative (PID)-based sliding mode control (SMC) with a finite-time convergence for power control of a PWR with control time delay compensated using an auxiliary signal to mitigate any power oscillation. Due to the simple structure and approximation accuracy of the multi-dimensional Taylor network (MDTN), it is employed to identify the complex dynamics of the reactor. The MDTN approximation error is attenuated using an adaptive law to improve the approximation accuracy. The bended tracking error (BTE) scheme is used to eliminate the SMC reaching phase so that the output power can track the desired level at the initial time in the sliding phase, thereby ensuring robustness and convergence within a preassigned settling time. The finite-time stability of the closed-loop system is demonstrated through Lyapunov-based analysis. Simulation results have shown that the proposed power control achieved a root-mean-square error (RMSE) of 0.002305, which is superior compared to high-order SMC (HOSMC) and radial basis function neural network-based event-triggered fixed time (RBFNN-ET-FT) control that achieved an RMSE of 1.745 and 0.880, respectively.

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.

Although electric vehicles (EVs) offer bidirectional charging and can serve as mobile energy storage units, integrating them into microgrids and enabling them to participate in load frequency control (LFC) has proven to be a significant challenge. One of the crucial issues is the unpredictability of EV usage patterns, which can lead to sudden fluctuations in generation-demand balance. This unpredictability makes it difficult to coordinate EV charging and discharging with the grid's frequency control needs. Classical control techniques lack the capability to handle the EV's stochastic behavior. To address this, the study proposes the integration of a battery energy storage system to maintain continuous generation – demand balance. Then, a finite horizon model predictive control (MPC) is applied for the LFC of a two-area islanded MG integrated with the EV charging station. The MPC generates the optimal signals for the optimal adjustment of the power generation of the dispatchable sources as well as the EV aggregators. To evaluate system stability, the local input-to-state stability (ISS) criterion is employed. Simulation results demonstrate that the proposed MPC-based LFC outperforms conventional methods in terms of frequency nadir, settling time, and rate of change of frequency (RoCoF). Under a 2 % step load perturbation in one of the control areas (CAs), the proposed MPC reduces frequency nadir to −0.0306 Hz (a 12.9 % improvement over the best alternative), limits RoCoF to −0.0524 Hz/s (a 17.4 % enhancement), and achieves a minimum steady-state frequency deviation of 4.14 × 10⁻³ Hz (a 1.9 % reduction). Furthermore, in response to random load fluctuations and renewable energy source (RES) variability, the proposed controller ensures that frequency deviations remain within ±0.02 Hz, while RoCoF is constrained within ±0.02 Hz/s, demonstrating superior robustness, constraint handling and faster convergence.

Developing a reliable control algorithm for a mechatronic suspension can be tricky because of the nonlinear nature of the system and the necessity to achieve a good trade-off between the requirements of road handling ability and the comfort of passengers. This paper assesses the performance of two control methods, the H-Infinity (H infinity) and linear quadratic regulator (LQR), applied to an active suspension system. This suspension system uses sensors and actuators in addition to the springs and dampers of the traditional suspension system. It combines software, electrical, and mechanical parts to improve the car handling and convenience of passengers on the road. A quarterly car automotive suspension model consisting of 2 degrees of freedom (2 DOF) is proposed as the case study. The suspension deviation, wheel displacement, and upward acceleration of the car frame are the performance parameters considered in this research. The aim is to strike a perfect balance while achieving minimum readings of car chassis acceleration and wheel deflection demonstrated by each controller when steady state error approaches zero from the suspension response. The body acceleration and wheel deflection affect the passenger comfort and road handling, respectively. Time-based simulation is carried out in MATLAB/Simulink environment to verify the effectiveness of the proposed control mechanisms. The results of the simulation demonstrate the effectiveness and robustness of the proposed control schemes.

Electric vehicles (EVs) have emerged as a compelling solution to mitigate environmental concerns and meet the growing demand for energy-efficient transportation systems. The careful selection of the electric motor is critical in determining the overall performance of the EV. This paper uses an EV from the University ofDebrecen as a reference to comprehensively study the feasibility of using a permanent magnet brushless direct current (PMBLDC) motor in the vehicle. The optimal performance of the overall powertrain is realized based on a proportional integral and derivative (PID) controller. The advanced nonlinear dynamics of the system make the performance of the control algorithm unrealistic. The PID is optimized based on a genetic algorithm (GA-PID) to address this limitation and achieve optimal performance. The integral performance indices are used as a fitness value for the optimization problem. However, MATLAB/Simulink/Simscape is used to comprehensively investigate and compare the simplified and advanced models of a threephase, four-pole, Y-connected PMBLDC motor in the EV application. The simulation results indicate that the proposed electrical machine is promising in EVs, achieving 90.90 % energy efficiency, thereby decreasing the energy consumption by 11.12 % compared to the measured real-world results. This research contributes significantly to energy efficiency, power efficiency, and thermal performance, offering invaluable insights into the optimal selection and modeling of PMBLDC motors at varying complexity levels in EVs. Ultimately, this study steers the industry towards a more sustainable and environmentally conscious trajectory.

Fuel cell (FC) technologies convert chemical energy from hydrogen and oxygen to generate electricity, producing water and oxygen gas as harmless by-products. This makes FC an attractive option for powering electric vehicles (EVs), significantly reducing carbon emissions from road transportation. However, the FC technologies for EV applications face several challenges, including limited fuel, low power density, and slow dynamic response during high load variations. Most of the time, these problems can be avoided by pairing the FC with other energy sources like flywheels, supercapacitors (SC), battery energy storage (BES), and so on. This configuration is called the fuel cell hybrid electric vehicle (FCHEV). The coordination among the various energy sources and the load on the FCHEV is facilitated by energy management strategies (EMSs) and control algorithms. The EMSs share the load demand among the energy sources according to their power characteristics. Meanwhile, the control algorithms ensure that the energy sources produce the required currents while stabilizing the DC bus voltage of the FCHEV. However, aging, external disturbances, faults, parametric variations, and load fluctuations of the FCHEV affect the effectiveness of the control algorithms. As such, several advanced control algorithms have been proposed to mitigate these problems, achieve accurate and reliable power performance, preserve the health of the powertrain components, maintain supply–demand balance, minimize fuel consumption, and extend the range of the FCHEV. This paper provides a comprehensive introduction and classification of the state-of-the-art literature survey on various control methods applied to FCHEV. In addition, the paper presents an overview of energy sources and fuel cell hybrid energy systems used for the FCHEV. This paper helps researchers gain insight into the past, recent progress, and future outlooks in the applied control methods in FCHEV.

The automotive suspension must perform competently to support comfort and safety when driving. Traditionally, car suspension control tuning is performed through trial and error or with classical techniques that cannot guarantee optimal performance under varying road conditions. The study aims at designing a Linear Quadratic Regulator-based Bacterial Memetic Algorithm (LQR-BMA) for suspension systems of automobiles. BMA combines the bacterial foraging optimization algorithm (BFOA) and the memetic algorithm (MA) to enhance the effectiveness of its search process. An LQR control system adjusts the suspension’s behavior by determining the optimal feedback gains using BMA. The control objective is to significantly reduce the random vibration and oscillation of both the vehicle and the suspension system while driving, thereby making the ride smoother and enhancing road handling. The BMA adopts control parameters that support biological attraction, reproduction, and elimination-dispersal processes to accelerate the search and enhance the program’s stability. By using an algorithm, it explores several parts of space and improves its value to determine the optimal setting for the control gains. MATLAB 2024b software is used to run simulations with a randomly generated road profile that has a power spectral density (PSD) value obtained using the Fast Fourier Transform (FFT) method. The results of the LQR-BMA are compared with those of the optimized LQR based on the genetic algorithm (LQR-GA) and the Virus Evolutionary Genetic Algorithm (LQR-VEGA) to substantiate the potency of the proposed model. The outcomes reveal that the LQR-BMA effectuates efficient and highly stable control system performance compared to the LQR-GA and LQR-VEGA methods. From the results, the BMA-optimized model achieves reductions of 77.78%, 60.96%, 70.37%, and 73.81% in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the GA-optimized model. Moreover, the BMA-optimized model achieved a −59.57%, 38.76%, 94.67%, and 95.49% reduction in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the VEGA-optimized model.

This paper uses the MATLAB/Simulink/Simscape environment to evaluate the performance of a conventional Volkswagen (VW) Crafter, equipped with a 6-speed manual gearbox and a 2.0 Turbocharged Direct Injection Common Rail Diesel (TDI CR) engine, which is converted into a hybrid vehicle. A parallel hybrid vehicle powered by a Permanent Magnet Synchronous Motor (PMSM) is compared to the conventional version. A genetic algorithm (GA) is applied to tune the classical PID controller to optimize energy consumption. A novel methodology is also introduced based on the hardware-in-the-loop (HIL) method, leveraging an online data acquisition (DAQ) system integrated into the vehicle’s Controller Area Network (CAN Bus) system. The acquired CAN bus data is analyzed using LabVIEW and Net CAN plus 110 hardware. The simulated vehicle demonstrates better performance than the real vehicle based on the comparison between the simulation and experimental results. The transformed hybrid vehicle achieves 75.11% reduction in fuel consumption and an energy efficiency of 84.62%.

Hybrid electric vehicles (HEVs) have emerged as a trendy technology for reducing over-dependence on fossil fuels and a global concern of gas emissions across transportation networks. This research aims to design the hybridized drivetrain of a Volkswagen (VW) Jetta MK5 vehicle on the basis of its mathematical background description and a computer-aided simulation (MATLAB/Simulink/Simscape, MATLAB R2023b). The conventional car operates through a five-speed manual gearbox, and a 2.0 TDI internal combustion engine (ICE) is first assessed. A comparative study evaluates the optimal fuel economy between the conventional and the hybrid versions based on a proportional-integral-derivative (PID) controller, whose optimal set-point is predicted and computed by a genetic algorithm (GA). For realistic hybridization, this research integrated a Parker electric motor and the diesel engine of a VW Crafter hybrid vehicle from the faculty of engineering to reduce fuel consumption and optimize the system performance of the proposed car. Moreover, a VCDS measurement unit is developed to collect vehicle data based on real-world driving scenarios. The simulation results are compared with experimental data to validate the model’s accuracy. The simulation results prove the effectiveness of the proposed energy management strategy (EMS), with an approximately 89.46% reduction in fuel consumption for the hybrid powertrain compared to the gas-powered traditional vehicle, and 90.05% energy efficiency is achieved.

Modeling of various phenomena in engineering work is always a kind of simplification of real processes, aiming at a model where a certain level of mathematical theory and computational procedures is sufficient. If the complexity of the required theory corresponds to the general mathematical competence of engineers, then technical problems can be treated separately in engineering (or physical) models without regard to the mathematical background. However, in some advanced engineering fields, the harmonized development of engineering and mathematical models and toolboxes is necessary to find efficient solutions. For example, modeling variable structure systems in ideal sliding mode requires a mathematical toolbox that goes far beyond general engineering competence through the theory of discontinuous right-hand-side differential equations. Although sliding mode control is popular in practice and the concept of sliding mode allows a significant reduction of model complexity, its exact mathematical description is rarely encountered. The problem of friction compensation of a micro-telemanipulator using sliding mode control demonstrates a harmonized application of the mathematical and engineering approaches. Based on Filippov’s theory, the ideal sliding mode can be discussed. Although an ideal system cannot be implemented in reality, the real systems can be kept close enough to it; therefore, the discussion of the solution of the ideal model is important for practical applications. Although several elements of the topic are available in the literature, in this paper a unique complex approach is given for users of sliding mode control with experimental considerations, different engineering models, and codes. The paper concludes that sliding mode control is a case where engineering and mathematical modeling are inseparable and requires the competence of both fields.

This paper provides a novel graphical Bond Graph teaching method to benefit the education quality in mechatronics engineering. Mechatronics engineering is considered an interdisciplinary subject since it involves mechanical, electronics, and control theory. Traditional control engineering focuses on dynamic mathematics modeling, such as state-space and ordinary differential equations, which makes it difficult to understand the subjects’ concepts during education. This paper proposes the Bond Graph as a native language for mechatronics engineering students, which can reach the same result as the traditional transfer function and state space methods with slight differences in modeling concepts. The Bond Graph also has a synergy property by representing multiple domains using the same toolset language. The Bond Graph as a Dataflow program helps students digest the complexity of the concept because of its modular characteristics. The Bond Graph provides convenience in mechatronics engineering education from a pure graphical modeling method.

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.

The well-known disturbance observer -which is based on the transfer function of the inverse model -, was published almost four decades ago.To practically implement the inverse of the transfer function a filter is added to it, in order to eliminate high frequency disturbance signals.A key step of the design of this Inverse Model Based Disturbance Observer (IMBDO) is the selection of the filter with appropriate parameters.This paper proposes a disturbance observer, which is based on direct model (DMBDO) and it can work without any additional filter.It simplifies the design and the implemented controller code.Discrete time implementations of IMBDO and DMBDO are compared by a simple internetbased servo system in a non-real-time control environment.The effect of non-equidistant sampling is examined.

The advent of hybrid electric vehicles (HEVs) has significantly reduced emissions of toxic gases and fuel consumption due to conventional cars' internal combustion engines (ICEs). This paper uses MATLAB/Simulink environment to design a parallel hybrid Volkswagen (VW) Crafter, whose optimal consumption is managed by a classical proportional-integral (PI) strategy. The Vehicle is propelled by a permanent magnet synchronous motor (PMSM) and a 2.0 Turbocharged Direct Injection Common Rail (TDI CR) diesel ICE. In addition, an online data acquisition system via CAN (Controller Area Network) Bus framework was developed. The Vehicle CAN Bus system data were extracted and stored on a computer as a CSV file using LabVIEW with Vision Systems GMBH Net CAN Plus 110 hardware. Following the data storage, a graphical user interface was implemented using LabVIEW to analyse the results for engineering understanding of the acquired raw CAN data. The simulation results indicate that the designed electric car has gained better performance over the framework of the actual Vehicle's Hard-In-The-Loop (HIL) test.

The main contribution of this article is creating synergy between subjects; this means that students use the same graphical tool in several subjects. So far, the bond graph has not been used in control theory, but it is the “native language” of mechatronics engineers, so we would like to introduce it into the teaching of control theory. The bond graph method is proposed as a novel teaching method to teach mechatronics subjects in the paper. The bond graph is a graphical alternative to ordinary differential equations from a mathematical standpoint. Traditionally, control theory employs ordinary differential equations, as they are familiar to control theorists. However, mathematically, both approaches are equivalent but require a slightly different approach in their application. This article highlights the mathematical similarities between the two approaches while emphasizing the distinctions in graphical representation. Another contribution is that the PID and sliding mode controller are represented using the bond graph method. In the meantime, through the use of practical examples, we effectively illustrate how the same problem can be solved using either approach. In the training materials, the PID controller and an adaptive robust sliding mode controller (ARSMC) with the bond graph are utilized as examples to demonstrate synergy in mechatronics. Finally, we present proof that mechatronic engineers achieve superior outcomes when utilizing the bond graph approach, based on test results from undergraduate students.

In this article, a hybrid powertrain for the Volkswagen (VW) Crafter is designed using the Model-In-The-Loop (MIL) method. An enhanced Proportional-Integral (PI) control technique based on integral cost functions is developed by carrying out a time-based simulation in MATLAB/Simulink software to realize the optimal fuel economy of the vehicle. Moreover, a comparative study is conducted between the vehicle's hybrid and pure electric versions to assess the optimal battery energy consumption per unit distance traveled. Communication within our vehicles' Electronic Control Units (ECUs) is facilitated by a message-based protocol called a Controller Area Network (CAN). Consequently, this paper presents an online CAN Bus data analysis using the Hardware-In-The-Loop (HIL) method. This method uses a standard frame, J1939 CAN protocol, implemented with Net CAN Plus 110 hardware. A graphical user interface is developed on a host Personal Computer (PC) using LabVIEW for decoding the acquired raw CAN data to physical values. The simulation results reveal that the proposed controller is promising and suitable for realizing optimal performance over the HIL method.

Mechatronics is a hard interdisciplinary subject, which requires a tight synergy of mechanical and electrical knowledge. This paper presents a novel modular approach to teaching mechatronics subjects using the bond graph in LabVIEW. LabVIEW can provide an industrial environment that connects graphical and analytical aspects. The background of the mechatronics students is divided into two directions: mechanical and electrical. During the research, these two directions share a common part, which is the bond graph. The bond graph is a novel hybrid method to connect the mechanical and electrical parts. The bond graph is an efficient technology to teach mechatronics subjects, and it is easy to understand for the students. A modularity teaching method has been developed for undergraduate and master's students at the engineering faculty, University of Debrecen. For the undergraduates, the focus is on the fundamental models, with the goal dedicated to applied technology and linear system. For the master's stage, students will learn more complex controller systems because of the modular learning strategy that adds some modules to the basic models. A DC motor is modeled for the demonstration in the paper under the Lab View programming environment.

Machine Learning models are known for having millions of parameters considering different applications, such as object recognition and self-driving cars. To avoid spending more time than necessary in the training stage and more importantly, to achieve a satisfactory model, the hyperparameters should be correctly defined. The main goal of the proposed project was to identify and evaluate the influence of hyperparameters in the training of neural networks for computer vision applications. Besides that, we aim to provide a didactic approach to analyze the acquired generalization capability of a model and easy visualization of the impact of different parameters during the training phase.

This conference paper presents the sliding mode control of an Internet-based servo motor. The servo motor is equipped with an incremental encoder and the speed signal is obtained by digital derivation. Derivation is known to highlight noises. The state trajectory calculated from the noisy signal cannot remain on the sliding surface. Noise filtering introduces an unmodeled dynamics into the system. Due to this unmodeled dynamics, the state trajectory oscillates around the sliding surface. To reduce these oscillations, the article proposes an observer-based sliding mode controller. The article compares the classic and observer-based sliding mode controls by experimental measurement. The aim of the Internet-based servo motor to allow the students to reach the system, from their own computer via the internet, and to make the measurements from home. To do that they only need to know the basics of programming. With this servo system, the capacity of the measurement is increased. The students can use the system any time of the day.

Electrification of the conventional propulsion system revolution has been taking place in the automotive industry to seek a cleaner and safer transportation network across cities. The electrified powertrain has significantly reduced fossil fuel dependence and improved fuel economy. Moreover, optimal energy consumption as a trade-off between the conflicting demand to prolong driving mileage and the need for the vehicle to operate within the limit of the battery capacity is quite critical. This paper simulates the energy consumption per 1 kilometer and 100 kilometers of the distance covered by a VW Crafter with a 2.0 CR DTI Diesel Engine Vehicle. The traditional Combustion Engine (CI) system as a front-wheel-drive was replaced by a comparative study between a complex and simplified electrified drivetrain-based a 4-pole, Y-connected Three-Phase Permanent Magnet Brushless DC Motor (PMBLDCM) propulsion system. The energy consumed using the New European Drive Cycle (NEDC) test procedure was 0.15 kWh/km and 10.53 kWh/100km, respectively. Furthermore, this research proposes a Proportional Integral (PI) baseline control algorithm to regulate Motor and vehicle speeds. In this manner, the execution of the PI controller was confirmed by implementing a PID Control scheme. However, the performance of the traditional controls is not realistic and, therefore, not reliable due to the nonlinear nature of the system. Accordingly, performance integral criteria such as “Integral Square Error (ISE),” “Integral Absolute Error (IAE)” etc., were used to ascertain the optimal gains of the proposed control techniques.