Simulation Outcomes Research Articles

Ionic Liquid Assisted Green Synthesis of Quinoxaline Based Bisspirooxindoles: Anticancer Evaluation and Molecular Dynamics

AbstractIn this study, we have synthesized a series of novel quinoxaline based bisspirooxindoles through green protocol using ionic liquid [Bmim]BF4. All the compounds were well characterized by spectroscopic methods like FT‐IR, 1H NMR, 13C NMR, mass and finally the structures were authenticated by single crystal X‐ray diffraction (SCXRD) (4e). The target compounds were evaluated for their anticancer activity with different cancer cell lines like MDAMB‐231, MCF‐7, MCF‐10A, HCC‐1395, Molt‐4, and FaDU. The compounds 4d, 4g, 4m, and 4n showed 42.0%, 43.3%, 52.7%, and 56.0 percentage of growth inhibition respectively with the T cell acute lymphoblastic Molt‐4 cell line. The compounds 4b, 4c, 4 h, 4k, and 4m have shown 30%–39% of growth inhibition against FaDU cell line. Further, anticancer activity was validated with in silico molecular docking and molecular dynamics simulations. The outcomes of dynamics simulations exclusively emphasize that the compounds bind to HIS862 and TYR896 residues which are among the catalytic triad of PARP1. Finally, the results of ADME is also used to assess the drug likeness, which clearly shows that our target compounds are adaptable as potential drug pathways for medicinal chemists.

Modified Smith predictor-based robust tracking control design for fractional-order singular semi-Markovian jump systems

This paper focuses on addressing the robust tracking control problem as well as the compensation of delay and external disturbances for fractional-order singular semi-Markovian jump systems. Moreover, a multi-periodic modified repetitive control scheme is developed to guarantee the exact tracking performance of the considered system. Subsequently, by implementing the Majhi-Atherton modified Smith predictor approach, input delays and disturbances occurring in the addressed system are compensated. Precisely, the incorporation of disturbance estimator with the conventional Smith predictor frame can accurately estimate and attenuate the external disturbances. Further, the linear matrix inequality-based conditions for ascertaining the required results are procured with the aid of Lyapunov stability theory. Following this, by solving the acquired criteria, the precise framework of the gain matrices can be reckoned. Finally, the effectiveness and supremacy of the suggested control approach are assessed by using the outcomes of simulations obtained from two numerical examples.

Recovery of Low-concentration Waste Acid by Electrodialysis: Modelling and Validation

In light of the operational principles of Electrodialysis (ED), it is anticipated that this technology would significantly contribute to the recovery of waste acids through selective separation and subsequent proton concentration. However, the imperfect proton leakage characteristics of anion exchange membranes (AEMs) not only detrimentally affect the efficacy of ED-based acid recovery systems but also present considerable challenges for modeling endeavors. This study introduces a model based on the Nernst-Plank Equation at the cell pair scale, aimed at predicting ED performance. The model incorporates an empirical expression that links operational parameters, such as acid concentration and the concentration ratio between the concentrate and dilute compartments, to the permselectivities of AEMs in terms of anion transport numbers. Furthermore, both numerical and experimental analyses are performed to evaluate energy consumption across various operating conditions. The simulation outcomes derived from the proposed model exhibit a strong correlation with experimental data concerning acid transport (where the acid concentration increases from 0.25 M to 1.1 M through a two-stage concentration process), water migration (which demonstrates a nearly linear increase over time with applied currents, specifically a 15% increase under low-current conditions and a 100% increase under high-current conditions), and energy consumption. It is hoped that this model will aid in the design and optimization of ED-based acid reclamation processes, thereby enhancing their practical applications.

Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment

Intrusion detection in the Internet of Things (IoTs) is a vital unit of IoT safety. IoT devices face diverse kinds of attacks, and intrusion detection systems (IDSs) play a significant role in detecting and responding to these threats. A typical IDS solution can be utilized from the IoT networks for monitoring traffic, device behaviour, and system logs for signs of intrusion or abnormal movement. Deep learning (DL) approaches are exposed to promise in enhancing the accuracy and effectiveness of IDS for IoT devices. Blockchain (BC) aided intrusion detection from IoT platforms provides many benefits, including better data integrity, transparency, and resistance to tampering. This paper projects a novel sandpiper optimizer with hybrid deep learning-based intrusion detection (SPOHDL-ID) from the BC-assisted IoT platform. The key contribution of the SPOHDL-ID model is to accomplish security via the intrusion detection and classification process from the IoT platform. In this case, the BC technology can be used for a secure data-sharing process. In the presented SPOHDL-ID technique, the selection of features from the network traffic data takes place using the SPO model. Besides, the SPOHDL-ID technique employs the HDL model for intrusion detection, which involves the design of a convolutional neural network with a stacked autoencoder (CNN-SAE) model. The beetle search optimizer algorithm (BSOA) method is used for the hyperparameter tuning procedure to increase the recognition outcomes of the CNN-SAE technique. An extensive simulation outcome is created to exhibit a better solution to the SPOHDL-ID method. The experimental validation of the SPOHDL-ID method portrayed a superior accuracy value of 99.59 % and 99.54 % over recent techniques under the ToN-IoT and CICIDS-2017 datasets.

Lippmann-Schwinger equation representation of Green's function and its preconditioned generalized over-relaxation iterative solution in wavelet domain

The calculation of Green's function is the core of seismic forward and inverse methods based on integral operators. When the Lippmann-Schwinger (L-S) equation is used to calculate Green's function in strongly scattering media, both the Born scattering series and the numerical iterative method encounter issues of slow convergence or divergence. Although the renormalization method derived from quantum mechanics can effectively address the convergence problem of Born scattering series in strong scattering problems, it is acknowledgeed that the convergence conditions and rates of convergence of different reformulation series may vary, and no universal convergence reformulation scattering series exists. Numerical methods for solving integral equations tend to be more general and mathematically robust. In this work, we focus on the numerical solution method of L-S equations. By using a wavelet-domain preconditioner to a reformulated or equivalent Lippmann-Schwinger (L-S) equation, we present an iterative method for numerically solving the equivalent L-S equation aimed at improving the rate of convergence and iteration efficiency in strongly inhomogeneous media. Following Jakobsen et al. (2020), we first introduce a small imaginary component into the background wave number，then rewrite the L-S equation to derive the equivalent complex wave number L-S equation. This reformulation ensures that the coefficient matrix exhibits a banded structure after numerical discretization, allowing the wavelet coefficient matrix to maintain good sparsity. We employ a multi-level fill-in incomplete LU (ILU) factorization method along with a block ILU-based algebraic recursive multilevel solve (ARMS) method in the wavelet domain to generate sparse approximate inverses as preconditioning operators, thereby accelerating the convergence of the generalized successive over-relaxation (GSOR) iterative method. This method is applied to compute numerical Green's functions in strongly inhomogeneous media. Numerical results demonstrate that our method yields simulation outcomes consistent with those obtained from the direct method for solving the original real wave number L-S equation. By testing various preconditioners, we find that the ARMS preconditioner offers significant advantages in operator generation efficiency and non-zero element filling ratio, effectively accelerating the convergence of the GSOR iterative method while achieving higher computational efficiency.

The Importance of Impoundment interception in Simulating Riverine Dissolved Organic Carbon

AbstractModeling of riverine dissolved organic carbon (DOC) dynamics is of great importance for the global carbon budget. Impoundment interception changes the travel time of water and DOC from upslope contributing areas, exerting substantial influence on riverine DOC dynamics in the catchments with many impoundments. However, the impact of impoundment interception representation on riverine DOC modeling has not been evaluated so far. This study investigated to what extent impoundment interception representation affects DOC simulations using a newly developed catchment‐scale DOC model, which can represent the upslope contributing areas of impoundments and the impoundment interception process. The results showed that streamflow and DOC load simulation were well simulated regardless of whether impoundment interception was represented, but the simulation of DOC concentrations was satisfiable only when impoundment interception was taken into account. The simulation without impoundment interception produced unrealistic fluctuation of DOC concentration due to the direct mixing of DOC from different sources with contrasting concentration gradients. These results underscored the significance of employing an appropriate model structure for riverine DOC simulation. It is strongly recommended that DOC concentration be utilized for model evaluation in order to attain robust simulation outcomes. Moreover, the newly developed model in this study keeps a balance between the completeness of process presentation and model complexity, occupying a unique “ecological niche” among catchment‐scale riverine DOC models.

State-of-the-Art CFD Simulation: A Review of Techniques, Validation Methods, and Application Scenarios

This review examines recent advancements in Computational Fluid Dynamics (CFD) simulation, focusing on state-of-the-art techniques, validation methods, and their application across various fields. CFD techniques, including Direct Numerical Simulation (DNS), Large Eddy Simulation (LES), Reynolds-Averaged Navier-Stokes (RANS), and hybrid methods, are compared in terms of computational demands, accuracy, and suitability for different fluid flow scenarios. Effective validation methods such as wind tunnel testing, analytical solutions, and field measurements are highlighted to ensure simulation reliability and align models with experimental data. Additionally, this review explores application scenarios in aerospace, automotive, environmental, and biomedical engineering, discussing how each field adapts CFD techniques to optimize performance and ensure realistic simulation outcomes. The comparative analysis offers insights into the trade-offs between accuracy and computational cost. It underscores the need for careful technique selection and robust validation to enhance CFD's applicability in both research and industry. The review aims to guide practitioners in selecting the most appropriate CFD approaches for their specific engineering challenges.

Towards Sustainable Transportation: Adaptive Trajectory Tracking Control Strategies of a Four-Wheel-Steering Autonomous Vehicle for Improved Stability and Efficacy

The objective of continuous increase in the evolution of autonomous and intelligent vehicles is to attain a trustworthy, economical, and safe transportation system. Four-wheel steering (4WS) vehicles are favored over traditional front-wheel steering (FWS) vehicles because they have excellent dynamic characteristics. This paper exhibits the trajectory tracking task of a two degree of freedom (2DOF) underactuated 4WS Autonomous Vehicle (AV). Because the system is underactuated, MIMO, and has a nontriangular form, the traditional adaptive backstepping control scheme cannot be utilized to control it. For the purpose of rectifying this issue, two-state feedback-based methods grounded on the hierarchical steps of the block backstepping controller are proposed and compared in this paper. In the first strategy, a modified block backstepping is applied for the entire dynamic system. Global stability of the overall system is manifested by Lyapunov theory and Barbalat’s Lemma. In the second strategy, a block backstepping controller has been applied after a reduction of the high-order model into various first-order subsystems, consisting of Lyapunov-based design and stability warranty. A trajectory tracking controller that can follow a double lane change path with high accuracy is designed, and then simulation experiments of the CarSim/Simulink connection are carried out against various vehicle longitudinal speeds and road surface roughness to demonstrate the effectiveness of the presented controllers. Furthermore, a PID driver model is introduced for comparison with the two proposed controllers. Simulation outcomes show that the proposed controllers can attain good response implementation and enhance the 4WS AV performance and stability. Indeed, enhancement of the stability and efficacy of 4WS autonomous vehicles would afford a sustainable transportation system by lessening fuel consumption and gas emissions.

Enhancing soil water stability and retention through plastic mulching under atypical climatic conditions on the Chinese loess plateau

Mulching is an agricultural practice that is extensively implemented worldwide to conserve water in soil to enhance agricultural production，and especially in the temperate continental monsoon climate regions. However, the mechanism controlling soil moisture evaporation, infiltration, and retention by mulching is unclear. We assess the impact of various mulching regimes on the soil–water equilibrium in the root zone of corn fields under atypical climate conditions(Excessive Precipitation) from 2020–2021 in five treatments: (1) ridges mulched with plastic film and furrows without mulching (RF), (2) conventional flat planting with full plastic mulching (FPM), (3) conventional flat planting with straw mulching (SM), (4) conventional flat planting with partial plastic mulching (PPM), and (5) conventional (control) flat planting with no mulching (CK). The HYDRUS-2D model was calibrated and validated using experimental data, to assess soil water content, water flux, and soil water balance within a two-dimensional soil profile. This model accurately replicated the root zone within the soil profile under all mulching scenarios, with numerical simulation outcomes closely aligning with observed measurements. Average R² values for FPM, PPM, RF, SM, and CK scenarios were 0.76, 0.75, 0.86, 0.85, and 0.77, respectively. During the 2020 and 2021 growing seasons, characterized by increased rainfall, plastic-covered treatments (FPM, PPM, RF) more efficiently reduced soil evaporation and enhanced soil-water retention. The combined soil drainage and storage changes for FPM, PPM, RF, and SM treatments exceeded those of CK by an average of 114.57, 64.93, 77.38, and 6.74 mm, respectively. FPM, PPM, and RF treatments had substantial water-retention capabilities during years of atypical climate. Notably, FPM ensured adequate water supply, facilitated deep soil water replenishment, and more effectively maintained soil water stability and retention. This underscores the pivotal regulatory function of mulching in mitigating the impacts of consecutive years of unusual climatic conditions.

Geant4 Monte Carlo simulation of human exposure to indoor 222Rn from building materials

The present study aimed to develop a Monte Carlo model to estimate the annual effective dose due to radon exposure sourced by radon gas in the walls and floor of a standard model room. With the purpose of developing a tool for radon level assessment in dwellings and workplaces, Geant4 toolkit was used to simulate the energy deposited by gamma rays emitted by radioactive radon progeny in a water phantom positioned at three different locations within the model room. The energy deposition was then used to estimate the annual effective dose through a deterministic approach. The simulation outcomes showed good agreement with experimental data, with the ratio between the simulated and the experimental data displaying the overestimation by a factor of approximately 1.09. Both simulation and experimental data fell within the same range, with a relative deviation of 7.7%. Additionally, the influence of various parameters, such as receptor position in the room, wall, and floor thicknesses, wall cover, and building material bulk density, on the annual effective dose due to radon inhalation in the room was evaluated. Geant4 Monte Carlo toolkit proved to be a reliable tool for radon modeling in real exposure situations.

Design and optimization of photonic crystal fibre THz bio‐sensor for extremely impressible identification of dengue

AbstractThe detection of human diseases is a major application for biosensors. During this work, a 2D photonic crystal fibre (PCF) biosensor design for dengue virus detection has been suggested. This work presents the decagonal hollow core PCF‐based dengue virus bio‐sensor and quantitatively investigates it over the terahertz regime. The suggested biosensor's performance is assessed using COMSOL Multiphysics, a professional tool that uses the Finite Element Method. The simulation's outcomes show that the suggested sensor performed better than earlier research, with a high sensitivity of 98.79%, 97.96%, 97.71%, 98.58%, 96.99% and 97.47% with more less confinement loss 1.2766 × 10−12 dB/m, 1.6385 × 10−12 dB/m, 2.8015 × 10−13 dB/m, 1.1798 × 10−13 dB/m, 7.0336 × 10−12 dB/m and 0.00 dB/m respectively for infected Haemoglobin (Hgb), Normal Haemoglobin (Hgb), Infected Platelets (Plt), Normal Platelets (Plt), Infected Plasma (Psm) and Normal Plasma (Psm) at 3.0 THz using the ideal geometric configuration. Very soon, its remarkable sensitivity and guiding capabilities will be crucial to dengue virus detection technology.

A nonlinear filter based on GSK for relative navigation using relative orbital elements

Due to the sensitivity of the relative orbital elements model to the measurement noise, the non-stationary heavy-tailed noise(NSHT) induced by the time-varying environment during the relative navigation usually leads to filter divergence. To address this problem, a new nonlinear filter based on Gaussian-Student's-Multivariate K(GSK) mixture distribution is proposed in this paper. A Dirichlet stochastic mixture vector fusing Gaussian, Student's t, and Multivariate K distributions is introduced, thus proposing a GSK mixture distribution modeling measurement likelihood; then the Kullback-Leibler Divergence (KLD) of the true posteriori probability density function(PDF) and the approximate posteriori PDF are minimized by a variational Bayesian(VB) technique to solve for the state and parameter approximate a posteriori estimations, and finally a new nonlinear filter based on the GSK mixture distribution is derived for angles-only relative navigation in time-varying environments. Simulation outcomes indicate that the filter can realize state estimation in non-stationary states effectively with 45.16% higher estimation accuracy than the existing advanced filters.

Phase disposition PWM control topology based: A novel multilevel inverter with reduced switch for power electronics applications

In the field of industrial drive applications, a neutral point clamped multilevel inverter (NPC MLI) is an extensively used option. The NPC MLI architecture involves more number of components for higher level and higher switching frequency operation. In this work paper, a novel three-phase 3-Level MLI is proposed evading the usage of clamping diodes and quadratic switches. Additionally, phase disposition pulse width modulation (PD-PWM) control technique is also employed for the proposed MLI. In comparison to NPC MLI, proposed MLI reduces the voltage switching stress as only one switch is operated per inverter leg. Another feature is that there is a considerable reduction in power losses as the current flows only through fewer elements. The proposed 3L inverter topology is explored thoroughly using the MATLAB simulation model. The evaluation of results is also demonstrated concerning the proposed PD-PWM technique by comparing its performance with the conventional sinusoidal PWM method. The Real-Time hardware-in-loop (HIL) simulator is also used to validate the simulation outcomes of the proposed model.

Research on the Construction Mechanics and Stability Control Technology of Expressway Tunnel Based on Numerical Analysis

As an effective structural form, connecting arch tunnel has been widely used in practical engineering because of its smooth linear shape, small footprint and good bridge and tunnel connection. With the increase of domestic traffic volume year by year, the width of the even arch tunnel is gradually changed from two-way four lanes to two-way six lanes. I20 I-steel, spacing 0.75∼1 m, anchor length 3∼4 m, ring spacing 0.5∼1 m, the side ring spacing of the middle wall is 0.75 m, the side ring spacing of the side wall is 1 m, and the shotcrete thickness is 0.25∼0.3 m. The excavation span of a two-way six-lane continuous-arch tunnel is considerable, leading to complex stress characteristics, thereby posing risks to construction safety and structural stability. Following the construction of the initial tunnel, disturbance to the surrounding rock occurs, affecting subsequent excavation phases. The initial support requirements for subsequent excavations are more intricate, with increased internal forces compared to the initial tunnel segment. As the rear tunnel progresses, the surrounding rock near the middle wall shifts towards the rear tunnel, reducing internal forces in the concrete and steel arch frame. However, as the rear tunnel distance increases, concrete stress and axial forces on the middle wall side of the initial tunnel begin to rise. Throughout the construction, the steel arch frame’s internal forces and spray concrete stress on the middle wall side’s arch waist and foot are the highest, making them susceptible to disruption from construction on the opposite side, thus constituting critical tunnel components. Numerical simulations effectively capture surrounding rock and structural stress dynamics during large-span arch tunnel construction. Simulation outcomes align closely with field measurements, particularly in three-dimensional simulations, enhancing construction understanding and management.

A mMSA-FOFPID controller for AGC of multi-area power system with multi-type generations

The exceptional growth in the penetration of renewable sources as well as complex and variable operating conditions of load demand in power system may jeopardize its operation without an appropriate automatic generation control (AGC) methodology. Hence, an intelligent resilient fractional order fuzzy PID (FOFPID) controlled AGC system is presented in this study. The parameters of controller are tuned utilizing a modified moth swarm algorithm (mMSA) inspired by the movement of moth towards moon light. At first, the effectiveness of the controller is verified on a nonlinear 5-area thermal power system. The simulation outcomes bring out that the suggested controller provides the best performance over the lately published strategies. In the subsequent step, the methodology is extended to a 5-area system having 10-units of power generations, namely thermal, hydro, wind, diesel, gas turbine with 2-units in each area. It is observed that mMSA based FOFPID is more effective related to other approaches. In order to establish the robustness of the controller, a sensitivity examination is executed. Then, experiments are conducted on OPAL-RT based real-time simulation to confirm the feasibility of the method. Finally, mMSA based FOFPID controller is observed superior than the recently published approaches for standard 2-area thermal and IEEE 10 generator 39 bus systems.

Artificial Neural Network-Based Fault Detection on Nigerian 330kv Power Transmission Line

In Nigeria, there will inevitably be ongoing difficulties with 330kv power transmission lines. This drop in insulation strength between the phase conductors and the earthed screen encircling the conductors may be the cause of the difficulties that have emerged on the electric transmission circuit. Many scholars have approached these problems in different ways. Fast Fourier Transform (FFT), Wavelet Transform, Fourier Transform, S-Transform, and Fourier Series are a few of the techniques utilized to address these transmission line problems. It has not been widely and thoroughly adopted to solve problems in Power System Engineering, according to numerous study studies on artificial neural networks (ANNs). Consequently, the 330kV Power Transmission Line problems have been addressed in this research by using artificial neural networks (ANNs). the training efficacy of any ANN challenges diagnosis system. The MATLAB/Simulink R2018a was utilized to conduct simulations on an ANN challenges detector. The ANN detector was trained with pre-fault and fault signals as inputs, allowing for the identification of different types of line difficulties. Results indicated that at Mean Square Error (MSE) of 1.6856e-5, the best training performance for the challenges was attained. At Regression, it approaches zero (9.8958e-1). Because it met the requirements, the simulation's outcome is acceptable. Keywords: Power System, Transmission Line, Matlab/Simulink, Three Phase, Artificial neural networks, fault detection.

Development of Semi-Empirical and Machine Learning Models for Photoelectrochemical Cells

We introduce a theoretical model for the photocurrent-voltage (I-V) characteristics designed to elucidate the interfacial phenomena in photoelectrochemical cells (PECs). This model investigates the sources of voltage losses and the distribution of photocurrent across the semiconductor–electrolyte interface (SEI). It calculates the whole exchange current parameter to derive cell polarization data at the SEI and visualizes the potential drop across n-type cells. The I-V model’s simulation outcomes are utilized to distinguish between the impacts of bulk recombination and space charge region (SCR) recombination within semiconductor cells. Furthermore, we develop an advanced deep neural network model to analyze the electron–hole transfer dynamics using the I-V characteristic curve. The model’s robustness is evaluated and validated with real-time experimental data, demonstrating a high degree of concordance with observed results.

Identification of Critical Nodes for Delay Propagation in Susceptible-Exposed-Infected-Recovered (SEIR) and Genetic Algorithm (GA) Route Networks

In response to the challenges associated with forecasting the trajectory of flight delay propagation, pinpointing pivotal nodes within the route network, and the substantial costs involved in enhancing operational efficiency, this study introduces an innovative approach to identifying critical nodes that influence delay propagation across route networks. The methodology commences by establishing a route network model for East China, leveraging the principles of complex network theory. It then incorporates the SEIR (Susceptible-Exposed-Infected-Recovered) model, typically used for analyzing the dynamics of infectious disease spread, to examine the propagation of delays between routes. Subsequently, the approach employs a GA to identify key nodes, which are then compared against those identified by network topology indices. The simulation outcomes demonstrate that the GA’s identification of key nodes offers superior insights into the overall network’s susceptibility to infection, thereby presenting operational managers with novel perspectives for analyzing the spread of flight delays.

Evaluating Natural Course Performance in Parametric G-formula: Review of Current Practice and Illustration Based on the United Autoworkers-General Motors Cohort.

The parametric g-formula is a causal inference method that appropriately adjusts for time-varying confounding affected by prior exposure. Like all parametric methods, it assumes correct model specification, usually assessed by comparing the observed outcome with the simulated outcome under no intervention (natural course). However, it is unclear how to evaluate natural course performance and whether other variables should also be considered. We reviewed current practices for evaluating model misspecification in applications of parametric g-formula. To illustrate the pitfalls of current practices, we then applied the parametric g-formula to examine cardiovascular disease mortality in relation to occupational exposure in the United Autoworkers-General Motors cohort (UAW-GM), comparing 20 parametric model sets and qualitatively assessing natural course performance for all time-varying variables over follow-up. We found that current practices of evaluating model misspecification are often insufficient, increasing risk of bias and statistical cherry picking. Based on our motivational analyses of the UAW-GM cohort, good natural course performance of the outcome does not guarantee good simulations of other covariates; poor predictions of exposures and covariates may still exist. We recommend reporting natural course performance for all time-varying variables at all time-points. Objective criteria for evaluating model misspecification in parametric g-formula need to be developed.

Control Design and Implementation of Autonomous Robotic Lawnmower

This paper presents the trajectory tracking control design and implementation of feedback linearization (FL) and robust feedback linearization (RFL), applicable to a robotic lawnmower with four mecanum driving wheels. The RFL control design additionally includes a robust control law. These two nonlinear control laws are developed to enable the controlled robotic lawnmower to accurately follow any specified trajectory. The simulation outcomes illustrate that the suggested control law based on RFL displays superior trajectory tracking accuracy and resilience compared to the FL control method in the case of a robotic lawnmower operating under demanding conditions. These conditions encompass environmental disturbances and uncertainties in modeling. The RFL control method also exhibits lower energy consumption compared to the FL control method. Finally, using the RFL controller derived from this study, the error in trajectory tracking in computer simulations and the actual mowing performance have demonstrated outstanding results.