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This paper presents a comprehensive tutorial on the identification process for a class of continuously dynamic systems expressed in state-space form using the model reference adaptive approach. The proposed algorithm does not require prior knowledge of the systems but needs all state variables to adapt the estimated parameters. Through simulations using m-script code, software-in-the-loop (SIL), and hardware-in-the-loop (HIL) simulations, the effectiveness of the proposed method in identifying the system model of a DC motor is evaluated. Simulation results demonstrate consistency across various platforms. Steady-state estimated models can be achieved using the proposed estimation algorithm with adaptation gains of [Formula: see text] after 5 s. Furthermore, this paper demonstrates the implementation of the proposed method on both SIL and HIL platforms, using Python and MicroPython programming languages, respectively. This approach leverages the Numpy library for efficient matrix computations. It is evident that the proposed estimation algorithm is readily applicable in real-world scenarios.

Magnetic flux leakage (MFL) testing is a widely employed non-destructive testing (NDT) method, particularly for ferromagnetic materials. This paper proposes a systematic design procedure for the yoke-magnetization component in MFL systems utilizing permanent magnets. The dimensions of the yoke-magnetization are parameterized, and the design methodology is formulated in the form of analytical equations based on fundamental engineering principles and optimization techniques. The proposed design procedure outlines all the required parameters and metrics to establish a yoke-magnetization configuration tailored for a specified application, thereby facilitating scalability. The theoretical framework identifies the optimal operating point that maximizes leakage flux while minimizing the magnetic field amplitude. A genetic algorithm is employed to minimize the dimensions of the permanent magnets while maintaining a sufficient magnetic field strength for specimen magnetization. An experimental setup is constructed to validate the accuracy of the proposed design procedure. The experimental results exhibit consistency with finite element method (FEM) simulations conducted using a case study. Specifically, the findings reveal that the thickness of the magnetic bridge significantly impacts the leakage fluxes at defect sites within the specimen. When the thickness of the magnetic bridge is 10 mm, slightly smaller than the optimal thickness of approximately 11 mm, the leakage flux at the defect site leaks out towards the surrounding air insignificantly. In contrast, a thickness of 15 mm is observed to strongly improve the leakage flux at the defect site.

Optimizing speed and propulsive efficiency are the most crucial survival skills for biomimetic robots. This paper investigates a swimming mode controller inspired by the black Knifefish to govern the fast-swimming gait with high propulsive efficiency for an elongated undulating fin robot. The proposed swimming mode controller is composed of a couple of Hopf oscillator-based central pattern generators (CPG) to generate the moving gait of robotic fish and a novel variant of Reinforcement Learning (RL) known as Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG) for optimizing the propulsive efficiency. The proposed swimming controller facilitates the autonomous optimization of the oscillatory amplitude of the robotic fish to improve its propulsive efficiency. The proposed MA-DDPG demonstrates an aptitude for functioning within mixed cooperative-competitive environments. Furthermore, it effectively mitigates the drawback of zero amplitude in the updating process of conventional reinforcement learning (RL) methodologies. These findings highlight the potential utility of the MA-DDPG in optimizing the performance of multi-agent systems in dynamic, real-world scenarios. The simulation results show that the undulating fin robot reaches a maximum thrust of 0.9 N with a propulsive efficiency of 12.48 %, which is higher than that of traditional reinforcement learning methods.

In this paper, a novel backstepping terminal super-twisting sliding mode (TSTSM) with high order sliding mode observer (HOSMO) is proposed to control the two degrees of freedom (DOFs) Serial Elastic Actuator (SEA), inspired by a lower limb of humanoid robots. First, the dynamic model, extended from our previous study, is presented for developing the control algorithm. Secondly, the backstepping technique is utilized to separate the overall system into two subsystems. One of the challenges of SEA is to deal with the evident oscillations caused by the elastic element, which might lead to the degrading performance of load position control. In order to reduce this adverse effect, a TSTSM is proposed to control the position tracking of two subsystems. The advantages of TSTSM are the finite-time convergence despite the bounded perturbation and the dramatic reduction of the chattering phenomenon. To construct and implement the TSTSM controller, it requires the knowledge of all states, which is not available in the current lower limb system setup. Therefore, a HOSMO is utilized to estimate the unknown states. Finally, experiment results are carried out to assess the effectiveness of the proposed controller and compare it with those of different control schemes.

The utilization of photovoltaic (PV) cleaning robots has proven to be an effective method for maintaining the conversion efficiency of utility-scale PV power plants by mitigating the impact of dust accumulation. However, ensuring the safe operation of these robots, resembling tanks in appearance, particularly in wet working conditions, relies heavily on their adherence to PV panels. This study focuses on assessing the slip resistance of candidate materials coated on endless polyurethane timing belts, which are equipped on PV cleaning robots to enable the efficient cleaning of uneven and misaligned PV arrays. A novel apparatus is proposed to evaluate the coefficient of static friction (COSF) of slip specimens, considering factors such as outsole patterns, area density, and shore hardness. The results highlight the significant influence of shore hardness and area density on the slip resistance of the specimens. Based on the findings, it is recommended to design track grooves with hexagon or zigzag patterns and maintain a low area density (e.g., 0.44 g·mm−2) to ensure the safe operation of PV cleaning robots, irrespective of the working conditions they encounter. By addressing the slip resistance challenge, this research contributes to the overall efficiency and reliability of PV cleaning robots, enhancing their performance in maintaining clean and optimal PV panel surfaces.

Numerous studies about solar panel cleaning robot (SPCR) have been conducted globally to enhance the performance of photovoltaic panels (PV panels). However, there is a reality: scant attention has been paid to the large pressure and vibration that SPCR movements induce, not only on the photovoltaic panel surface but also on the mounting structure. Most of the research is focused on evaluating the “cleanliness” of the PV surface by using a clearing robot or the effects of natural factors (wind, dust, etc.) on the PV panels. Nonetheless, the large pressure and vibration constitute one of the primary factors contributing to the degradation of photovoltaic panel longevity and efficiency, especially affecting poorly installed PV arrays. To address these issues, this study proposes the design of a multi-suspension unit for the SPCRs equipped with track-wheeled, which might reduce vibration on the PV panel surface generated by the SPCRs’ motion and brushing action during operation. The multi-suspension unit facilitates the expansion of the contact area between the track-wheeled and the PV panel surface; hence, the negative effect is reduced owing to the vibration-absorbing properties of the rubber track pads. In the case of a static SPCR state, with only the impact of rotating brushes on vibration, the effectiveness of the multi-suspension unit can reduce vibration by a maximum of 72.63. Moreover, a metric (Δz) is proposed to evaluate the change in deflection of PV panels over time. The results show that the number of significant changes in PV panel deflection gradually decreases or disappears, while the number of small changes increases. In the case of SPCR operating on portrait PV modules, the number of occurrences of Δz (greater than 0.5 mm) is reduced from 18 counts to 5 counts, while the number of occurrences of Δz (less than 0.5 mm) increased from 61 counts to 91 counts. Overall, the proposed multi-system suspension is effective in reducing or eliminating large deflections while keeping the vibration frequency constant.

This article proposes a locomotion controller inspired by black Knifefish for undulating elongated fin robot. The proposed controller is built by a modified CPG network using sixteen coupled Hopf oscillators with the feedback of the angle of each fin-ray. The convergence rate of the modified CPG network is optimized by a reinforcement learning algorithm. By employing the proposed controller, the undulating elongated fin robot can realize swimming pattern transformations naturally. Additionally, the proposed controller enables the configuration of the swimming pattern parameters known as the amplitude envelope, the oscillatory frequency to perform various swimming patterns. The implementation processing of the reinforcement learning-based optimization is discussed. The simulation and experimental results show the capability and effectiveness of the proposed controller through the performance of several swimming patterns in the varying oscillatory frequency and the amplitude envelope of each fin-ray.

Diseases transmitted by mosquitoes and snails cause a large burden of disease in less developed countries, especially those with low-income levels. An approach to control vectors and intermediate hosts based on readily available essential oils, which are friendly to the environment and human health, may be an effective solution for disease control. Guava is a fruit tree grown on a large scale in many countries in the tropics, an area heavily affected by tropical diseases transmitted by mosquitoes and snails. Previous studies have reported that the extracted essential oils of guava cultivars have high yields, possess different chemotypes, and exhibit toxicity to different insect species. Therefore, this study was carried out with the aim of studying the chemical composition and pesticide activities of six cultivars of guava grown on a large scale in Vietnam. The essential oils were extracted by hydrodistillation using a Clevenger-type apparatus for 6 h. The components of the essential oils were determined using gas-chromatography–mass-spectrometry (GC-MS) analysis. Test methods for pesticide activities were performed in accordance with WHO guidelines and modifications. Essential oil samples from Vietnam fell into two composition-based clusters, one of (E)-β-caryophyllene and the other of limonene/(E)-β-caryophyllene. The essential oils PG03 and PG05 show promise as environmentally friendly pesticides when used to control Aedes mosquito larvae with values of 24 h LC50-aegypti of 0.96 and 0.40 µg/mL while 24 h LC50-albopictus of 0.50 and 0.42 µg/mL. These two essential oils showed selective toxicity against Aedes mosquito larvae and were safe against the non-target organism Anisops bouvieri. Other essential oils may be considered as molluscicides against Physa acuta (48 h LC50 of 4.10 to 5.00 µg/mL) and Indoplanorbis exustus (48 h LC50 of 3.85 to 7.71 µg/mL) and with less toxicity to A. bouvieri.

Ventilators are drawn to many researchers during the Covid-19 pandemic because it's essential equipment that's accustomed to treat severe Covid-19 patients. In low-income countries, there's a shortage of pricy respiratory devices resulting in exceeding the provision of taking care of Covid-19's patients in ICU. This paper attempts to design and implement an appropriate respiratory device referred to as a bag valve mask (BVM) ventilator for those who are Covid-19 patients in medical care, those patients have a requirement of safe transport and also palliative care. The BVM ventilator comprises a man-made manual breath unit (AMBU) bag and paddles for squeezing the AMBU bag which is popular in medical aid settings. The BVM ventilator is required to travel airflow through the system to the patient's lung with the specified volume for every breath cycle within a threshold air pressure. Since the AMBU bag is straightforward to be deformed over time, it's difficult to get mathematical modelling for constructing a reliable controller. Therefore, a model-free control (MFC) control approach is utilized successfully to style a controller for our BVM ventilator model with a PEEP valve and a HEPA filter. Some experimental scenarios are administered to gauge the effectiveness of the proposed controller for the BVM ventilator to control the airflow and control air pressure mode.

Using essential oils to control vectors, intermediate hosts, and disease-causing microorganisms is a promising approach. The genus Croton in the family Euphorbiaceae is a large genus, with many species containing large amounts of essential oils, however, essential oil studies are limited in terms of the number of Croton species investigated. In this work, the aerial parts of C. hirtus growing wild in Vietnam were collected and analyzed by gas chromatography/mass spectrometry (GC/MS). A total of 141 compounds were identified in C. hirtus essential oil, in which sesquiterpenoids dominated, comprising 95.4%, including the main components β-caryophyllene (32.8%), germacrene D (11.6%), β-elemene (9.1%), α-humulene (8.5%), and caryophyllene oxide (5.0%). The essential oil of C. hirtus showed very strong biological activities against the larvae of four mosquito species with 24 h LC50 values in the range of 15.38–78.27 μg/mL, against Physella acuta adults with a 48 h LC50 value of 10.09 μg/mL, and against ATCC microorganisms with MIC values in the range of 8–16 μg/mL. In order to provide a comparison with previous works, a literature survey on the chemical composition, mosquito larvicidal, molluscicidal, antiparasitic, and antimicrobial activities of essential oils of Croton species was conducted. Seventy-two references (seventy articles and one book) out of a total of two hundred and forty-four references related to the chemical composition and bioactivity of essential oils of Croton species were used for this paper. The essential oils of some Croton species were characterized by their phenylpropanoid compounds. The experimental results of this research and the survey of the literature showed that Croton essential oils have the potential to be used to control mosquito-borne and mollusk-borne diseases, as well as microbial infections. Research on unstudied Croton species is needed to search for species with high essential oil contents and excellent biological activities.

Mobile robots have played a vital role in the transportation industries, service robotics, and autonomous vehicles over the past decades. The development of robust tracking controllers has made mobile robots a powerful tool that can replace humans in industrial work. However, most of the traditional controller updates are time-based and triggered at every predetermined time interval, which requires high communication bandwidth. Therefore, an event-triggered control scheme is essential to release the redundant data transmission. This paper presents a novel parameter-adaptive event-trigger sliding mode to control a two-wheeled mobile robot. The adaptive control scheme ensures that the mobile robot system can be controlled accurately without the knowledge of physical parameters. Meanwhile, the event trigger sliding approach guarantees the system robustness and reduces resource usage. A simulation in MATLAB and an experiment are carried out to validate the efficiency of the proposed controller.

The demand for renewable energy sources is growing fast because of the negative impact of the utilization of fossil energy, nuclear energy, and hydroelectricity. One of the renewable energy sources, known as solar energy, which uses the photovoltaic panel (PV) to generate electricity from the sun, is a promising alternative that has great potential to deal with the power crisis. However, the power productivity and efficiency conversion are affected significantly by dust accumulation on PVs. Many researchers investigated PV panel dust cleaning methods to improve performance, yield, and profitability. Various dust cleaning and mitigation methods such as rainfall, labor-based, and mechanized cleaning are explored, and we demonstrated that dust removal could be automated with cleaning robots effectively. Due to the specified geographical site of PV panel installation, cleaning robots might work on the misalignment and uneven PV arrays, presenting huge challenges for an autonomous cleaning robot. Thus, a rubber wheel crawler robot with semi-autonomous handling provides a flexible motion that is a well-suited solution to clean rooftop PV arrays. Nevertheless, the rubber wheel crawler robot might suffer slippage on the wet glass of tilted PV arrays. This paper studies the anti-slip effect of the rubber wheel crawler equipped with a cleaning robot under the wet surface of tilted PV panels. First, a theoretical model consisting of several parameters is established to validate the slippage of the rubber wheel crawler on the wet tilted PV. Then, some parameters of the theoretical model are approximated through experimental tests. Finally, simulation results of the theoretical model are conducted to evaluate the accuracy of the proposed theoretical model in comparison to the experimental results under the same working conditions. The merits provide the efficient design of rubber wheel crawlers, enabling the anti-slip ability of robots.