Selection Of Algorithm Parameters Research Articles

A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry.

Feature detection plays a crucial role in non-target screening (NTS), requiring careful selection of algorithm parameters to minimize false positive (FP) features. In this study, a stochastic approach was employed to optimize the parameter settings of feature detection algorithms used in processing high-resolution mass spectrometry data. This approach was demonstrated using four open-source algorithms (OpenMS, SAFD, XCMS, and KPIC2) within the patRoon software platform for processing extracts from drinking water samples spiked with 46 per- and polyfluoroalkyl substances (PFAS). The designed method is based on a stochastic strategy involving random sampling from variable space and the use of Pearson correlation to assess the impact of each parameter on the number of detected suspect analytes. Using our approach, the optimized parameters led to improvement in the algorithm performance by increasing suspect hits in case of SAFD and XCMS, and reducing the total number of detected features (i.e., minimizing FP) for OpenMS. These improvements were further validated on three different drinking water samples as test dataset. The optimized parameters resulted in a lower false discovery rate (FDR%) compared to the default parameters, effectively increasing the detection of true positive features. This work also highlights the necessity of algorithm parameter optimization prior to starting the NTS to reduce the complexity of such datasets.

Incremental Trainable Parameter Selection in Deep Neural Networks.

This article explores the utilization of the effective degree-of-freedom (DoF) of a deep learning model to regularize its stochastic gradient descent (SGD)-based training. The effective DoF of a deep learning model is defined only by a subset of its total parameters. This subset is highly responsive or sensitive toward the training loss, and its cardinality can be used to govern the effective DoF of a model during training. To this aim, the incremental trainable parameter selection (ITPS) algorithm is introduced in this article. The proposed ITPS algorithm acts as a wrapper over SGD and incrementally selects the parameters for updation that exhibit the maximum sensitivity toward the training loss. Hence, it gradually increases the DoF of the model during training. In ideal cases, the proposed algorithm arrives at a model configuration (i.e., DoF) optimum for the task at hand. This whole process results in a regularization-like behavior induced by a gradual increment of the DoF. Since the selection and updation of parameters is a function of the training loss, the proposed algorithm can be seen as a task and data-dependent regularization mechanism. This article exhibits the general utility of ITPS by evaluating it on various prominent neural network architectures such as CNNs, transformers, recurrent neural networks (RNNs), and multilayer perceptrons. These models are trained for image classification and healthcare tasks using the publicly available CIFAR-10, SLT-10, and MIMIC-III datasets.

Optimal Temperature Calculation for Multicriteria Optimization of the Hydrogenation of Polycyclic Aromatic Hydrocarbons by NSGA-II Method

Introduction. Multicriteria optimization, taking into account contradicting criteria, is used to improve production efficiency, reduce costs, improve product quality and environmental safety of processes. The literature describes the application of multicriteria optimization for production purposes, including the selection of reaction conditions and improvement of technological processes. In the presented paper, the object of research is the process of hydrogenation of polycyclic aromatic hydrocarbons (PAH) in the production of high-density fuels. To determine the optimal conditions of the process, the problem of multicriteria optimization based on the kinetic model is solved. The criteria include maximizing the yield of targeted naphthenes and conversion of feedstock. The research objective is to create a program implementing the multicriteria optimization non-dominated sorting genetic algorithm-II (NSGA-II). Due to this, it is possible to calculate the optimal temperature for the PAH hydrogenation process on the basis of the kinetic model.Materials and Methods. The NSGA-II genetic algorithm was used to solve the multicriteria optimization problem. Modified parental and survival selection within the Pareto front was also used. If it was necessary to divide the front, solutions based on the Manhattan distance between them were selected. The program was implemented in Python.Results. In the system of ordinary nonlinear differential equations of chemical kinetics, the concentration was designated yi, the conditional contact time of the reaction mixture with the catalyst — τ. The system was solved for the hydrogenation reaction of polycyclic aromatic hydrocarbons. The calculations showed that at τ = 0 y1(0) = 0.025; y2(0) = 0.9; y6(0) = 0.067; y9(0) = 0.008; yi(0) = 0, i = 3–5, 7, 8, 10–20; Q(0) = 1. The process temperature was considered as a control parameter according to two optimality criteria: maximizing the yield of target naphthenes (f1) at the end of the reaction, and maximizing the conversion of feedstock (f2). Values f1 were in the range of 0.43–0.79; conversion — 0.01–0.03; temperature — 200–300 K. The growth of temperature was accompanied by an increase in the yield of target naphthenes and a decrease in the conversion of feedstock. Each solution obtained was not an unimprovable one. When modeling the process of hydrogenation of PAH, an algorithm was launched with a population size of 100 and a number of generations of 100. A program implementing the NSGA-II algorithm was developed. The optimal set of values of the PAH hydrogenation reaction temperature was calculated, which made it possible to obtain unimprovable values of the optimality criteria — maximizing the yield of target naphthenes and conversion of feedstock.Discussion and Conclusion. The NSGA-II algorithm is effective for solving the problem of non-dominance, and deriving the optimal solution for all criteria. Future research should be devoted to the selection of optimal algorithm parameters to increase the speed of the solution. Based on the obtained theoretical optimal conditions of the PAH hydrogenation reaction, it is possible to implement the process in industry

Fed-MPS: Federated learning with local differential privacy using model parameter selection for resource-constrained CPS

In Cyber-Physical Systems (CPS), distributed learning is essential for efficiently handling complex tasks when sufficient resources are available. However, when resources are limited, traditional distributed learning struggles to complete even simple tasks and presents a risk of privacy leakage. As a promising distributed learning paradigm, federated learning only requires the client to send the trained model to the server instead of private data, thereby preserving the client’s privacy to some extent. However, with the rapid development of artificial intelligence technology, attack methods such as inference attacks still cause privacy leakage for clients participating in federated learning. Moreover, due to its distributed learning nature, federated learning cannot escape the dilemma of model accuracy being constrained by resources. To address the aforementioned problems, this paper proposes a Federated local differential privacy scheme using Model Parameter Selection, named Fed-MPS, for resource-constrained CPS. Specifically, to resolve the issue of limited CPS resources, Fed-MPS adopts an update direction consistency-based parameter selection algorithm in federated learning to extract parameters that enhance model accuracy for subsequent training, thereby improving the final model accuracy and reducing communication overhead. Furthermore, Fed-MPS applies the local differential privacy mechanism to further enhance clients’ privacy. By adding noise only to the chosen parameters, the privacy budget is significantly reduced while ensuring model accuracy. Through privacy analysis, we prove that the proposed Fed-MPS scheme satisfies (ϵ,δ)−DP. Additionally, convergence analysis guarantees that Fed-MPS will converge to the global optimum with a convergence ratio of O(1T2) within T rounds of federated learning. Extensive experiments on prominent benchmark datasets Cifar10, Mnist, and FashionMNIST demonstrate that, compared with baseline schemes, the proposed Fed-MPS provides higher model accuracy for CPS under resource constraints.

Improvement of photogrammetric joint roughness coefficient value by integrating automatic shooting parameter selection and composite error model

In order to improve the accuracy of the photogrammetric joint roughness coefficient (JRC) value, the present study proposed a novel method combining an autonomous shooting parameter selection algorithm with a composite error model. Firstly, according to the depth map-based photogrammetric theory, the estimation of JRC from a three-dimensional (3D) digital surface model of rock discontinuities was presented. Secondly, an automatic shooting parameter selection algorithm was novelly proposed to establish the 3D model dataset of rock discontinuities with varying shooting parameters and target sizes. Meanwhile, the photogrammetric tests were performed with custom-built equipment capable of adjusting baseline lengths, and a total of 36 sets of JRC data was gathered via a combination of laboratory and field tests. Then, by combining the theory of point cloud coordinate computation error with the equation of JRC calculation, a composite error model controlled by the shooting parameters was proposed. This newly proposed model was validated via the 3D model dataset, demonstrating the capability to correct initially obtained JRC values solely based on shooting parameters. Furthermore, the implementation of this correction can significantly reduce errors in JRC values obtained via photographic measurement. Subsequently, our proposed error model was integrated into the shooting parameter selection algorithm, thus improving the rationality and convenience of selecting suitable shooting parameter combinations when dealing with target rock masses with different sizes. Moreover, the optimal combination of three shooting parameters was offered. JRC values resulting from various combinations of shooting parameters were verified by comparing them with 3D laser scan data. Finally, the application scope and limitations of the newly proposed approach were further addressed.

Parameter selection of suppressed relative entropy fuzzy c-means clustering algorithm

The relative entropy fuzzy c-means (REFCM) clustering algorithm improves the robustness of the fuzzy c-means (FCM) algorithm against noise. However, its increased complexity results in slower convergence. To address this issue, we have proposed a suppressed REFCM (SREFCM) algorithm, in which a constant suppression rate, α, is selected. However, in cases where external factors, such as changes in the data structure, are present, relying on a fixed α value may result in a decline in algorithm performance, which is clearly unsuitable. Therefore, the adaptive selection of parameters is a critical step. Based on the data structure itself, this paper proposes an algorithm for adaptive parameter selection utilizing partition entropy coefficient and alternating modified partition coefficient, and compares it to six parameter selection algorithms based on generalized rules: θ′ type, ρ type, β type, τ type, σ type and ξ type. Empirical findings indicate that adapting parameters can enhance the partitioning capability of the algorithm while ensuring a rapid convergence rate.

Synchronization of non-smooth chaotic systems via an improved reservoir computing

The reservoir computing (RC) is increasingly used to learn the synchronization behavior of chaotic systems as well as the dynamical behavior of complex systems, but it is scarcely applied in studying synchronization of non-smooth chaotic systems likely due to its complexity leading to the unimpressive effect. Here proposes a simulated annealing-based differential evolution (SADE) algorithm for the optimal parameter selection in the reservoir, and constructs an improved RC model for synchronization, which can work well not only for non-smooth chaotic systems but for smooth ones. Extensive simulations show that the trained RC model with optimal parameters has far longer prediction time than those with empirical and random parameters. More importantly, the well-trained RC system can be well synchronized to its original chaotic system as well as its replicate RC system via one shared signal, whereas the traditional RC system with empirical or random parameters fails for some chaotic systems, particularly for some non-smooth chaotic systems.

Quantized Zeroth-Order Gradient Tracking Algorithm for Distributed Nonconvex Optimization Under Polyak- [Formula: see text] ojasiewicz Condition.

This article focuses on distributed nonconvex optimization by exchanging information between agents to minimize the average of local nonconvex cost functions. The communication channel between agents is normally constrained by limited bandwidth, and the gradient information is typically unavailable. To overcome these limitations, we propose a quantized distributed zeroth-order algorithm, which integrates the deterministic gradient estimator, the standard uniform quantizer, and the distributed gradient tracking algorithm. We establish linear convergence to a global optimal point for the proposed algorithm by assuming Polyak- [Formula: see text] ojasiewicz condition for the global cost function and smoothness condition for the local cost functions. Moreover, the proposed algorithm maintains linear convergence at low-data rates with a proper selection of algorithm parameters. Numerical simulations validate the theoretical results.

Robust small area estimation for unit level model with density power divergence.

Unit level model is one of the classical models in small area estimation, which plays an important role with unit information data. Empirical Bayesian(EB) estimation, as the optimal estimation under normal assumption, is the most commonly used parameter estimation method in unit level model. However, this kind of method is sensitive to outliers, and EB estimation will lead to considerable inflation of the mean square error(MSE) when there are outliers in the responses yij. In this study, we propose a robust estimation method for the unit-level model with outliers based on the minimum density power divergence. Firstly, by introducing the minimum density power divergence function, we give the estimation equation of the parameters of the unit level model, and obtain the asymptotic distribution of the robust parameters. Considering the existence of tuning parameters in the robust estimator, an optimal parameter selection algorithm is proposed. Secondly, empirical Bayesian predictors of unit and area mean in finite populations are given, and the MSE of the proposed robust estimators of small area means is given by bootstrap method. Finally, we verify the superior performance of our proposed method through simulation data and real data. Through comparison, our proposed method can can solve the outlier situation better.

Selection of Real-Coded Genetic Algorithm parameters in solving simulation–optimization problems for the design of water distribution networks

Abstract The design of the water distribution network (WDN) is very difficult mainly due to the nonlinear relation between head and flow. The distribution network should also be cost-effective. In any simulation–optimization approach, the computational time requirement is very high for very complex problems. The optimization module plays a crucial role in reducing the computational time. This study aims to apply a simulation–optimization approach to designing a WDN. EPANET is selected as the simulation model and Real-Coded Genetic Algorithm (RCGA) is selected as the optimization module. To link the simulation module with the optimization module, a program is written in MATLAB. The developed simulation–optimization approach was applied to two benchmark network problems to check the suitability of the method. The parameters of the RCGA were optimized for the two networks. The computational efficiency of the developed simulation–optimization model is checked based on the number of function evaluations. For both networks, the number of function evaluations to get the optimum network design was less than the number of function evaluations required for other methods mentioned in the literature.

A Cooperative Game Hybrid Optimization Algorithm Applied to UAV Inspection Path Planning in Urban Pipe Corridors

This paper proposes an improved algorithm applied to path planning for the inspection of unmanned aerial vehicles (UAVs) in urban pipe corridors, which introduces a collaborative game between spherical vector particle swarm optimization (SPSO) and differential evolution (DE) algorithms. Firstly, a high-precision 3D grid map model of urban pipe corridors is constructed based on the actual urban situation. Secondly, the cost function is formulated, and the constraints for ensuring the safe and smooth inspection of UAVs are proposed to transform path planning into an optimization problem. Finally, a hybrid algorithm of SPSO and DE algorithms based on the Nash bargaining theory is proposed by introducing a cooperative game model for optimizing the cost function to plan the optimal path of UAV inspection in complex urban pipe corridors. To evaluate the performance of the proposed algorithm (GSPSODE), the SPSO, DE, genetic algorithm (GA), and ant colony optimization (ACO) are compared with GSPSODE, and the results show that GSPSODE is superior to other methods in UAV inspection path planning. However, the selection of algorithm parameters, the difference in the experimental environment, and the randomness of experimental results may affect the accuracy of experimental results. In addition, a high-precision urban pipe corridors scenario is constructed based on the RflySim platform to dynamically simulate the optimal path planning of UAV inspection in real urban pipe corridors.

A High-Precision Insulation Inspection Method Based on the Double-Arm Unbalanced Bridge for Electric Ships

In order to ensure the safe navigation of electric ships, it is especially important to detect the insulation status between the ship’s high-voltage system and the ship’s hull in real time. In this paper, a real-time insulation resistance (IR) detection method based on the double-arm unbalanced bridge method was proposed for the problem that the IR calculation results of electric ships have large errors. The detection principle was described, and an experimental test platform was built. The influence law of each branch resistance and voltage on the IR measurement error in this circuit was studied to provide a reference for the parameter selection of the double-arm unbalanced bridge and the effect of temperature on the measurement results was also considered. Besides, the Extended Kalman filtering (EKF) algorithm was used to reduce the influence of voltage fluctuation on the insulation detection error. The results show that the insulation testing method could accurately measure the IR, and the insulation testing error was less than 2.4%, and the error was about 0.18% after processing with the EKF algorithm. This proves that this paper improves the measurement accuracy of IR from three aspects: circuit design of insulation testing, bridge parameter selection and filtering algorithm.

Classification models for assessing coronary artery disease instances using clinical and biometric data: an explainable man-in-the-loop approach

The main goal driving this work is to develop computer-aided classification models relying on clinical data to identify coronary artery disease (CAD) instances with high accuracy while incorporating the expert’s opinion as input, making it a "man-in-the-loop" approach. CAD is traditionally diagnosed in a definite manner by Invasive Coronary Angiography (ICA). A dataset was created using biometric and clinical data from 571 patients (21 total features, 43% ICA-confirmed CAD instances) along with the expert’s diagnostic yield. Five machine learning classification algorithms were applied to the dataset. For the selection of the best feature set for each algorithm, three different parameter selection algorithms were used. Each ML model’s performance was evaluated using common metrics, and the best resulting feature set for each is presented. A stratified ten-fold validation was used for the performance evaluation. This procedure was run both using the assessments of experts/doctors as input and without them. The significance of this paper lies in its innovative approach of incorporating the expert's opinion as input in the classification process, making it a "man-in-the-loop" approach. This approach not only increases the accuracy of the models but also provides an added layer of explainability and transparency, allowing for greater trust and confidence in the results. Maximum achievable accuracy, sensitivity, and specificity are 83.02%, 90.32%, and 85.49% when using the expert's diagnosis as input, compared to 78.29%, 76.61%, and 86.07% without the expert's diagnosis. The results of this study demonstrate the potential for this approach to improve the diagnosis of CAD and highlight the importance of considering the role of human expertise in the development of computer-aided classification models.

Real-time cable force identification based on block recursive Capon spectral estimation method

Cables are critical force-bearing components of cable-supported structures that greatly influence structure security. The changes in cable forces can reflect the cable service state and provide an important basis for structural health assessment. This paper proposes a method for real-time cable force identification (CFI) to tracking its varying rules. First, a recursive Capon method based on block updating is derived to estimate the amplitude spectrum at each observation moment. Second, determine the natural frequency (and corresponding mode order) in the transformed frequency domain and calculate the cable tension according to related formulas. Proper algorithm optimization and parameter selection are studied to ensure that the calculation cost meets the real-time requirement of CFI. The proposed method is applied to an experimental cable and the cables of a half-through arch bridge for validation.

Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network

Rotor fault diagnosis has attracted much attention due to its difficulties such as non-stationarity of fault signals, difficulty in fault feature extraction and low diagnostic accuracy of small samples. In order to extract fault feature information of rotors more effectively and to improve fault diagnosis precision, this paper proposed a fault diagnosis method based on variational mode decomposition (VMD) symmetrical polar image and fuzzy neural network. Firstly, the original rotor vibration signal is decomposed by using the VMD method and the relevant parameter selection algorithm of the VMD method is also proposed. Secondly, the intrinsic mode functions (IMF), which are sensitive to the signal characteristics, are selected for signal reconstruction based on a comprehensive evaluation factor method. As well, the reconstructed signal is transformed into a two-dimensional snowflake image through using the symmetrical polar coordinate method. Finally, the image features are extracted by the gray level co-occurrence matrix to form the state feature vector, which is input into the fuzzy neural network to realize the rotor fault diagnosis. Through the analysis of measured signals, the experimental results show that the proposed method can reach a higher recognition rate of 98% and the k-cross-validation experiment is used to demonstrate the robustness of the fuzzy neural network, and the average recognition accuracy of this experiment is 99.2%. Compared with some similar methods, the proposed method still has the highest fault recognition precision 98.4%, and the smallest standard deviation 0.5477.

An innovative hybrid approach for better detection of bearing faults in highly noisy environments

Rolling element bearings (REBs) are critical components in rotating machinery, and timely detection of faults in these bearings can prevent catastrophic failure and costly downtime. Vibration signals collected from REBs contain important information regarding the bearing health states. A localized defect in a REB produces periodic impulses in the vibration signals at relevant bearing characteristic frequencies (BCFs) that are commonly used for bearing condition monitoring. Envelope analysis that has been a powerful technique in detecting BCFs in signals, however, poses a significant challenge as it requires the selection of an effective band-pass filtering region due to the variable nature of resonant frequencies in bearing systems. A novel hybrid method for bearing fault diagnosis based on Cepstrum Pre-Whitening (CPW) and high-pass filtering has been developed to overcome this shortcoming in envelope analysis and has shown to be a powerful technique in accurately detecting early bearing faults on a range of machines under different operating conditions. In contrast to the existing signal processing methods, the new hybrid method eliminates the need for manual parameter selection or optimization algorithms, such as filtering bands selection in enveloping analysis. Instead, it requires inputs including bearing geometries and its operating conditions such as shaft rotating speed for bearing fault diagnosis. This enhances the practicality and accuracy of bearing fault diagnosis in real-world scenarios, especially when data is limited. This study evaluates the effectiveness of this hybrid method for bearing fault diagnosis in the highly noisy environments under which machine is often operated in real applications. To achieve this purpose, firstly, numerical simulations of bearing vibration signals have been conducted, wherein Gaussian white noise with varying levels of signal-to-noise ratio (SNR) is DOI: 10.1784/cm2023.3f4 added to the simulated signals. Secondly, the effectiveness of the method is tested on vibration data collected from an experimental run-to-failure bearing dataset, which contains significant amount of background noise in the early stage of the fault progression. The results from this work have shown that the new hybrid method achieves high accuracy in diagnosing fault features in the simulated signals with a SNR of down to ‐ 13 dB for outer ring fault and ‐ 15 dB for inner ring and ball faults, much more effective than other methods in the literature. It has also shown to detect bearing faults at very early stages from a run-to-failure test.

A Novel Nonconvex Low-Rank Tensor Completion Approach for Traffic Sensor Data Recovery From Incomplete Measurements

Complete traffic sensor data is considered to be one of the critical ingredients for intelligent transportation systems (ITS). However, the traffic measurements prevalently suffer from the inevitable and ubiquitous missing values. Current missing data completion algorithms are difficult to leverage the global low-rank property and the fine-grained spatial-temporal structure simultaneously. To circumvent this problem, this work presents a novel collaborative nonconvex low-rank spatial-temporal data tensor completion model that can take full advantage of the inherent spatial-temporal characteristics of traffic measurement data. First, the tensor Schatten p-norm, as an effective nonconvex surrogate of rank function, is used to exploit the global multi-dimensional correlation of traffic data. Then, we present an elastic net self-representation method and utilize an autoregression model in order to simultaneously capture the self-similarity and the temporal continuity of traffic data acquired in the same sensor network. By integrating the above elements in a unified nonconvex learning model, our method can explore the inherent structure of traffic data from the viewpoints of both global multi-dimensional correlation and fine-grained spatial and temporal dependency. Then, in the view of the general framework of the alternating directions method of multipliers (ADMM), an efficient iterative algorithm is designed to solve our model. Besides, to optimize the parameter combination of the model, a Bayesian optimization-based parameter selection algorithm is developed, which avoids manual parameter adjustment. Extensive experiments and analyses on two real-world traffic datasets are carried out. The results demonstrate the advantages of our model under diverse missing patterns and missing ratios.

SOFTWARE DEVELOPMENT FOR AUTOMATED DESIGN OF FLOW METERS

This work presents the development of a software complex for the automated design of variable pressure drop flowmeters. It includes flowmeters with standard primary devices, namely a diaphragm, nozzles and a venturi tube. To fulfill the task, a review of various software solutions has been conducted. As a result, it was concluded that the existing software does not fully solve the task, because it does not calculate most of the design parameters for metering device. The next stage was a review of the flow rate measuring method by variable pressure drop, and based on it, a mathematical model of flow meters has been implemented taking into account the existing standards. Next, the initial geometric parameters of the flow meter were evaluated and the parameter selection algorithm for the diaphragm with pressure taking chambers was given.
 As a result of the performed analysis, developed software allows you to conveniently enter input parameters, such as flowrate, pressure drop, pipeline diameter, type of constriction device, method of pressure selection, and at the output receive a corresponding schematic image of the flowmeter with all design parameters and the flow calculation formula, based on the pressure difference. For a better understanding of the software operation, an overview of it was offered and an example of a nozzle calculation has been demonstrated.
 The implementation of the created software application makes it possible to increase the accuracy of the accounting of consumed resources, and also reduces time and money costs for measuring instrument development.
 In the future, it is planned to create the software complex by adding to it the design capabilities of calorimetric, ultrasonic flowmeters and rotameters, as well as special software for selecting the most optimal flow measurement method based on the specified parameters of the measured medium, pipeline diameter and flow measurement range.
 Therefore, it will be possible to carry out metrological control and supervision of such measuring complexes, develop and certify methods for measuring the flow of liquids and gases.

An Algorithm for Offline Measurement of Motor Stator Resistance and Voltage Drop Across Inverter Switches for Washing Machine Drives

The knowledge of the exact value of motor phase resistance is extremely important for modern electrical drives, which use it for tuning of current controllers, position estimators, temperature monitoring, and so on. At the same time, the value of voltage drop across inverter switches is necessary for providing proper and efficient control, especially in the low-speed region, where the impact of system nonlinearities is significant. This article proposes a novel algorithm for the offline measurement of motor resistance and voltage drop across inverter switches, which significantly improved the performance of washing machines in open-loop starting and low-speed operation. The proposed technique uses two step injection of dc current into stator windings, which provides data for the estimation of resistance and voltage drop. This article describes traps and pitfalls on the way of implementation and tuning and provides recommendations on avoiding them. After analysis of detailed experimental results, the author gives recommendation on a selection of algorithm parameters in order to provide low estimation errors. Following successful implementation and a series of tests, this technique has been accepted for mass production and has become an inevitable part of the control system of washing machines.

Optimised Sliding Mode Control of a Hexacopter: Simulation and Experiments

Hexacopters are a kind of unmanned aerial vehicle (UAV) with six actuators and six degrees-of-freedom motions. The control of a hexacopter drone is a critical challenge. This paper presents a nonlinear dynamical model for a hexacopter and complete control approaches based on sliding mode control theory. Furthermore, this study proposed an effective control tuning method based on an optimisation algorithm. The controller has been improved by the grey wolf optimisation (GWO) algorithm, an iteration algorithm inspired by the social hierarchy and hunting behaviour of grey wolves. The improvement of the controller has been verified both experimentally and in simulations. The performance of the sub-controller for an attitude angle was tested in a test bench, and the whole flight controller was tested in simulation hexacopters, which are highly manoeuvrable, nonlinear aerial vehicles with six independent rotors and capacity for vertical take-off and landing. This article presents a derivation of the nonlinear dynamical model for a hexacopter, which includes the aerodynamic drag, and inertia counter torques. Flight control based on sliding mode control theory, which generally shows good performance on nonlinear systems, is developed and implemented. Given the need to simultaneously tune controller parameters, two nature inspired optimisation routines (GWO and PSO) are applied and compared for effectiveness in controller tuning. Results indicate that GWO-based tuning produces superior outcomes in terms of controller performance. This is in addition to the fact that PSO parameters require tuning rather than random selection of algorithm parameters. A reduced-order physical prototype is presented for the validation of the tuning routine on the roll/pitch control. The results indicate good agreement between simulation and experimental outcomes, with about 10.4% improvement in the tracking performance of roll DOF when GWO is applied to tune the controller.