A novel adaptive PD-type iterative learning control of the PMSM servo system with the friction uncertainty in low speeds

High precision demands in a large number of emerging robotic applications strengthened the role of the modern control laws in the position control of the Permanent Magnet Synchronous Motor (PMSM) servo system. This paper proposes a learning-based adaptive control approach to improve the PMSM position tracking in the presence of the friction uncertainty. In contrast to most of the reported works considering the servos operating at high speeds, this paper focuses on low speeds in which the friction stemmed deteriorations become more obvious. In this paper firstly, a servo model involving the Stribeck friction dynamics is formulated, and the unknown friction parameters are identified by a genetic algorithm from the offline data. Then, a feedforward controller is designed to inject the friction information into the loop and eliminate it before causing performance degradations. Since the friction is a kind of disturbance and leads to uncertainties having time-varying characters, an Adaptive Proportional Derivative (APD) type Iterative Learning Controller (ILC) named as the APD-ILC is designed to mitigate the friction effects. Finally, the proposed control approach is simulated in MATLAB/Simulink environment and it is compared with the conventional Proportional Integral Derivative (PID) controller, Proportional ILC (P-ILC), and Proportional Derivative ILC (PD-ILC) algorithms. The results confirm that the proposed APD-ILC significantly lessens the effects of the friction and thus noticeably improves the control performance in the low speeds of the PMSM

Minimum Distance and Minimum Time Optimal Path Planning with Bioinspired Machine Learning Algorithms for Impaired Unmanned Air Vehicles

Unmanned air vehicles operate in highly dynamic and unknown environments where they can encounter unexpected and unseen failures. In the presence of emergencies, autonomous unmanned air vehicles should be able to land at a minimum distance or minimum time. Impaired unmanned air vehicles define actuator failures and this impairment changes their unstable and uncertain dynamics; henceforth, path planning algorithms must be adaptive and model-free. In addition, path planning optimization problems must consider the unavoidable actuator saturations, kinematic and dynamic constraints for successful real-time applications. Therefore, this paper develops 3D path planning algorithms for quadrotors with parametric uncertainties and various constraints. In this respect, this paper constructs a multi-dimensional particle swarm optimization and a multi-dimensional genetic algorithm to plan paths for translational, rotational, and Euler angles and generates the corresponding control signals. The algorithms are assessed and compared both in the simulation and experimental environments. Results show that the multi-dimensional genetic algorithm produces shorter minimum distance and minimum time paths under the constraints. The real-time experiments prove that the quadrotor exactly follows the produced path utilizing the available maximum rotor speeds

A Machine Learning-Based 10 Years Ahead Prediction of Departing Foreign Visitors by Reasons: A Case on Türkiye

The most important underlying reasons for marketing failures are incomplete understanding of customer wants and needs and the inability to accurately predict their future behaviors. This study develops a machine learning model to estimate the number of departing foreign visitors from Türkiye by reasons for the next 10 years to gain a deeper understanding of their future behaviors. The data between 2003 and 2021 are extensively analyzed, and a multi-dimensional model having a higher-order fractional-order polynomial structure is constructed. The resulting model can predict the 10 reasons of departing foreign visitors for the next 10 years and can update the predictions every year as new data becomes available as it has stable polynomial parameters. In addition, a batch-type genetic algorithm is modified to learn the unknown model parameters by considering the disruptions, such as the coup attempt in 2016 and the COVID-19 pandemic outbreak in 2019, termed as uncertainties. Thus, the model can estimate the overall behavior of the departing foreign visitors in the presence of uncertainties, which is the dominant character of the foreign visitors by their reasons. Furthermore, the developed model is utterly data-driven, meaning it can be trained with the data collected from different cities, regions, and countries. It is predicted that the departing foreign visitors for all reasons will increase at various rates between 2022 and 2031, while the increase in transit visitors is predicted to be higher than the others. The results are discussed, and suggestions are given considering the marketing science. This study can be helpful for global and local firms in tourism, governmental agencies, and civil society organizations

A Machine Learning-Based 10 Years Ahead Prediction of Departing Foreign Visitors by Reasons: A Case on Türkiye

The most important underlying reasons for marketing failures are incomplete understanding of customer wants and needs and the inability to accurately predict their future behaviors. This study develops a machine learning model to estimate the number of departing foreign visitors from Türkiye by reasons for the next 10 years to gain a deeper understanding of their future behaviors. The data between 2003 and 2021 are extensively analyzed, and a multi-dimensional model having a higher-order fractional-order polynomial structure is constructed. The resulting model can predict the 10 reasons of departing foreign visitors for the next 10 years and can update the predictions every year as new data becomes available as it has stable polynomial parameters. In addition, a batch-type genetic algorithm is modified to learn the unknown model parameters by considering the disruptions, such as the coup attempt in 2016 and the COVID-19 pandemic outbreak in 2019, termed as uncertainties. Thus, the model can estimate the overall behavior of the departing foreign visitors in the presence of uncertainties, which is the dominant character of the foreign visitors by their reasons. Furthermore, the developed model is utterly data-driven, meaning it can be trained with the data collected from different cities, regions, and countries. It is predicted that the departing foreign visitors for all reasons will increase at various rates between 2022 and 2031, while the increase in transit visitors is predicted to be higher than the others. The results are discussed, and suggestions are given considering the marketing science. This study can be helpful for global and local firms in tourism, governmental agencies, and civil society organizations

Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification

Abstract Background Blood test is extensively performed for screening, diagnoses and surveillance purposes. Although it is possible to automatically evaluate the raw blood test data with the advanced deep self-supervised machine learning approaches, it has not been profoundly investigated and implemented yet. Results This paper proposes deep machine learning algorithms with multi-dimensional adaptive feature elimination, self-feature weighting and novel feature selection approaches. To classify the health risks based on the processed data with the deep layers, four machine learning algorithms having various properties from being utterly model free to gradient driven are modified. Conclusions The results show that the proposed deep machine learning algorithms can remove the unnecessary features, assign self-importance weights, selects their most informative ones and classify the health risks automatically from the worst-case low to worst-case high values

Development of a Multi-Dimensional Parametric Model With Non-Pharmacological Policies for Predicting the COVID-19 Pandemic Casualties

Coronavirus Disease 2019 (COVID-19) has spread the world resulting in detrimental effects on human health, lives, societies, and economies. The state authorities mostly take non-pharmacological actions against the outbreak since there are no confirmed vaccines or treatments yet. In this paper, we developed Suspicious-Infected-Death with Non-Pharmacological policies (SpID-N) model to analyze the properties of the COVID-19 casualties and also estimate the future behavior of the outbreak. We can state the key contributions of the paper with three folds. Firstly, we propose the SpID-N model covering the higher-order internal dynamics which cause the peaks in the casualties. Secondly, we parametrize the non-pharmacological policies such as the curfews on people with chronic disease, people age over 65, people age under 20, restrictions on the weekends and holidays, and closure of the schools and universities. Thirdly, we explicitly incorporate the internal and coupled dynamics of the model with these multi-dimensional non-pharmacological policies. The corresponding higher-order and strongly coupled model has utterly unknown parameters and we construct a batch type Least Square (LS) based optimization algorithm to learn these unknown parameters from the available data. The parametric model and the predicted future casualties are analyzed extensively

A novel adaptive PD-type iterative learning control of the PMSM servo system with the friction uncertainty in low speeds

High precision demands in a large number of emerging robotic applications strengthened the role of the modern control laws in the position control of the Permanent Magnet Synchronous Motor (PMSM) servo system. This paper proposes a learning-based adaptive control approach to improve the PMSM position tracking in the presence of the friction uncertainty. In contrast to most of the reported works considering the servos operating at high speeds, this paper focuses on low speeds in which the friction stemmed deteriorations become more obvious. In this paper firstly, a servo model involving the Stribeck friction dynamics is formulated, and the unknown friction parameters are identified by a genetic algorithm from the offline data. Then, a feedforward controller is designed to inject the friction information into the loop and eliminate it before causing performance degradations. Since the friction is a kind of disturbance and leads to uncertainties having time-varying characters, an Adaptive Proportional Derivative (APD) type Iterative Learning Controller (ILC) named as the APD-ILC is designed to mitigate the friction effects. Finally, the proposed control approach is simulated in MATLAB/Simulink environment and it is compared with the conventional Proportional Integral Derivative (PID) controller, Proportional ILC (P-ILC), and Proportional Derivative ILC (PD-ILC) algorithms. The results confirm that the proposed APD-ILC significantly lessens the effects of the friction and thus noticeably improves the control performance in the low speeds of the PMSM

A Novel Parametric Model for the Prediction and Analysis of the COVID-19 Casualties

Coronavirus disease (COVID-19) outbreak has affected billions of people, where millions of them have been infected and thousands of them have lost their lives. In addition, to constraint the spread of the virus, economies have been shut down, curfews and restrictions have interrupted the social lives. Currently, the key question in minds is the future impacts of the virus on the people. It is a fact that the parametric modelling and analyses of the pandemic viruses are able to provide crucial information about the character and also future behaviour of the viruses. This paper initially reviews and analyses the Susceptible-Infected-Recovered (SIR) model, which is extensively considered for the estimation of the COVID-19 casualties. Then, this paper introduces a novel comprehensive higher-order, multi-dimensional, strongly coupled, and parametric Suspicious-Infected-Death (SpID) model. The mathematical analysis results performed by using the casualties in Turkey show that the COVID-19 dynamics are inside the slightly oscillatory, stable (bounded) region, although some of the dynamics are close to the instability region (unbounded). However, analysis with the data just after lifting the restrictions reveals that the dynamics of the COVID-19 are moderately unstable, which would blow up if no actions are taken. The developed model estimates that the number of the infected and death individuals will converge zero around 300 days whereas the number of the suspicious individuals will require about a thousand days to be minimized under the current conditions. Even though the developed model is used to estimate the casualties in Turkey, it can be easily trained with the data from the other countries and used for the estimation of the corresponding COVID-19 casualties