Wireless Control Research Articles

An agile multimodal microrobot with architected passively morphing wheels.

Multimodal microrobots are of growing interest due to their capabilities to navigate diverse terrains, with promising applications in inspection, exploration, and biomedicine. Despite remarkable progress, it remains challenging to combine the attributes of excellent maneuverability, low power consumption, and high robustness in a single multimodal microrobot. We propose an architected design of a passively morphing wheel that can be stabilized at distinct geometric configurations, relying on asymmetric bending stiffness of bioinspired tentacle structures. By integrating such wheels with electromagnetic motors and a flexible body, we develop a highly compact, lightweight, multimodal microrobot (length ~32 mm and mass ~4.74 g) with three locomotion gaits. It has high motion speed (~21.2 BL/s), excellent agility (relative centripetal acceleration, ~206.9 BL/s2), low power consumption (cost of transport, ~89), high robustness, and strong terrain adaptabilities. Integration of batteries and a wireless control module enables developments of an untethered microrobot that maintains high motion speed and excellent agility, with capabilities of traveling in hybrid terrains.

Flexible bioelectronic systems with large-scale temperature sensor arrays for monitoring and treatments of localized wound inflammation

Continuous monitoring and closed-loop therapy of soft wound tissues is of particular interest in biomedical research and clinical practices. An important focus is on the development of implantable bioelectronics that can measure time-dependent temperature distribution related to localized inflammation over large areas of wound and offer in situ treatment. Existing approaches such as thermometers/thermocouples provide limited spatial resolution, inapplicable to a wearable/implantable format. Here, we report a conformal, scalable device package that integrates a flexible amorphous silicon-based temperature sensor array and drug-loaded hydrogel for the healing process. This system can enable the spatial temperature mapping at submillimeter resolution and high sensitivity of 0.1 °C, for dynamically localizing the inflammation regions associated with temperature change, automatically followed with heat-triggered drug delivery from hydrogel triggered by wearable infrared light-emitting-diodes. We establish the operational principles experimentally and computationally and evaluate system functionalities with a wide range of targets including live animal models and human subjects. As an example of medical utility, this system can yield closed-loop monitoring/treatments by tracking of temperature distribution over wound areas of live rats, in designs that can be integrated with automated wireless control. These findings create broad utilities of these platforms for clinical diagnosis and advanced therapy for wound healthcare.

Robustness of Deep-Learning-Based RF UAV Detectors.

The proliferation of low-cost, small radar cross-section UAVs (unmanned aerial vehicles) necessitates innovative solutions for countering them. Since these UAVs typically operate with a radio control link, a promising defense technique involves passive scanning of the radio frequency (RF) spectrum to detect UAV control signals. This approach is enhanced when integrated with machine-learning (ML) and deep-learning (DL) methods. Currently, this field is actively researched, with various studies proposing different ML/DL architectures competing for optimal accuracy. However, there is a notable gap regarding robustness, which refers to a UAV detector's ability to maintain high accuracy across diverse scenarios, rather than excelling in just one specific test scenario and failing in others. This aspect is critical, as inaccuracies in UAV detection could lead to severe consequences. In this work, we introduce a new dataset specifically designed to test for robustness. Instead of the existing approach of extracting the test data from the same pool as the training data, we allowed for multiple categories of test data based on channel conditions. Utilizing existing UAV detectors, we found that although coefficient classifiers have outperformed CNNs in previous works, our findings indicate that image classifiers exhibit approximately 40% greater robustness than coefficient classifiers under low signal-to-noise ratio (SNR) conditions. Specifically, the CNN classifier demonstrated sustained accuracy in various RF channel conditions not included in the training set, whereas the coefficient classifier exhibited partial or complete failure depending on channel characteristics.

Development Of An Artificial Intelligence Body Gesture Detection Dustbin With An Autonomous System

Effective waste collection is essential in homes and offices to maintain a healthy society, particularly for the elderly and individuals with disabilities. Smart technologies offer an innovative approach to addressing real-world challenges and fostering sustainable development. This system uses a microcontroller, ultrasonic sensors (HC-SR04) to detect the distance or movement of objects, an Arduino Uno as software frame work, a servo motor to open the lid, and a battery for continous power. Automatic activation is triggered by body gestures, making the system both user-friendly and efficient in waste disposal. The ultrasonic sensor detects a user's presence and sends signal to the Arduino Uno board which in turn activates the servo motor to open the dustbin lid. This operation minimizes the risk of contamination, as waste can be disposed without direct contact with the bin. The dustbin is mounted on an autonomous system that moves towards the user and returns to its designated position using features like line-following, path-finding, and Bluetooth-based (HC-05) wireless control for intelligent navigation. Keywords: Ultrasonic sensor, Autonomous system, smart bin, AI, Navigation, Gesture, Body.

IoT-Based Control, Monitoring, and Protection System for 3-Phase Induction Motors in Electric Motorcycles

This study investigates the application of the Internet of Things (IoT) for wireless control and monitoring of a 3-phase electric motor using a smartphone. The system integrates PZEM-004T, DS18B20, and Hall Effect sensors to collect data on voltage, current, temperature, and rotational speed using the NodeMCU ESP8266 microcontroller. Measurements are displayed on an LCD and transmitted to the Blynk server for smartphone access. A comparative method evaluates the accuracy of sensor readings against standard measuring instruments. Results obtained an average percentage error of 0.5% for the R phase voltage, 0.2% for the S phase, and 0.1% for the T phase. Current measurements reveal errors of 5% for the R phase, 10.3% for the S phase, and 11.7% for the T phase. The control system’s performance varies with internet speed, with an average delay of 0.99 seconds on a 4G network and 2.51 seconds on 3G. Additionally, the study evaluates three protection mechanisms, demonstrating that the motor stops within 4.03 seconds in the event of a phase failure, while overcurrent and overheating protections activate within 8.47 seconds and 3.64 seconds, respectively. Overall, the findings affirm the viability of IoT in motor monitoring and control, emphasizing accuracy and response times under varying conditions.

Evaluation of 5G-based closed-loop control on part quality for milling processes

Implementing wireless communication technologies in manufacturing systems shows the potential for a disruptive shift towards wireless networked control systems and edge computing for machine tool control. Especially 5G mobile communications enable wireless closed-loop control with ultra-reliable low-latency communication (uRLLC) and time-sensitive networking (TSN)-features. However, currently available hardware and software cannot yet meet the high requirements for closed-loop control of machine tools. This study evaluates the quality of a manufactured part when produced on a wireless closed-loop machine tool. A setup for emulating network characteristics (latency, jitter) is developed and implemented into a computerized numerical control (CNC) of a machine tool to synthesize the effects of 5G connectivity. The main objective is to evaluate how different network performance characteristics in combination with machine parameters influence surface quality parameters and geometric dimensioning and tolerancing (GD&T) of a milled specimen. Full factorial experimental design paradigm is used in the evaluation. The results show that latency and feed rate have no or insignificant influence on the surface quality of the manufactured part while jitter does have a significant influence. The surface quality deteriorates significantly above a jitter level of 100μs, so that the quality of the manufactured components is no longer sufficient. Moreover, the deviation of quality parameters starts to increase significantly with higher jitter. GD&T are just influenced by the variation of the feed rate, independent of latency and jitter. The results indicate that processes with lower requirements on the control loop (e.g. robot control, control of automated guided vehicles, etc.) can already be implemented using 5G mobile communications. However, closed-loop control of machine tools to achieve high quality manufactured parts, will only be possible with future developments in wireless communications. The results experimentally determine the minimum requirements for the communication architecture and the resulting quality losses for milling if these are not met.

Comprehensive Optimal Network Scheduling Strategies for Wireless Control Systems

Although wireless control is one of the key technologies for future industries, most wireless networks are only used for monitoring. When wireless networks are applied to transmit control commands, the uncertain link qualities and limited network resources may destroy the performance of multi-loop control systems. Hence, it is critical to allocate these resources to optimize the control performance as the network condition changes and plants evolve. This article presents comprehensive optimal scheduling strategies for wireless control systems based on adaptive dynamic programming. First, we propose an effective adaptive dynamic programming scheduling (ADPS) strategy to solve the optimal scheduling problem based on the single-step control performance at runtime while significantly reducing computational complexity. Moreover, to overcome the “short-sightedness” of single-step performance prediction, we extend ADPS to ADPS-m ( m ulti-step prediction), which optimizes multi-step performance by incorporating a longer-horizon evolution of the plants. Furthermore, we propose ADPS-H ( H eterogeneous flow scheduling) to support heterogeneous flows with different data rates and sizes and ADPS-H-m ( m ulti-step prediction for H eterogeneous flow scheduling), which schedules heterogeneous flows in a longer prediction horizon. We prove that all these scheduling strategies can achieve optimality and stability under mild assumptions. Extensive experiments integrating TOSSIM and MATLAB/Simulink are performed to evaluate all of the proposed methods in case studies of four- and ten-loop control systems. The simulation results demonstrate that these strategies can effectively improve the control performance at lower computing costs under both cyber and physical disturbances. Under the noise level of \(-\) 76 dBm, for the four-loop case, ADPS achieves the same control performance as the linear programming while saving 99.5% of the execution time. ADPS-m further improves the control performance by up to 27.0% compared with ADPS at the prediction horizon of 3, and ADPS-H-m improves the performance by up to 32.3% and 8.4% compared with round-robin and ADPS-H, respectively. The ten-loop case indicates the effectiveness and scalability of the proposed approaches.

Optimization Analysis of Low-Noise Amplifier

This paper mainly analyzes the important component of a wireless receiver – low-noise amplifiers (LNA). Low-noise amplifiers can reduce the effect of noise on the received signal and amplify signals. For the better development of wireless communication technology, remote control, and other related fields, it is necessary to optimize the design of low-noise amplifiers, so that low-noise operational amplifiers can finally obtain high gain, low noise figure, and other advantages. This paper analyzes the optimization design of LNA in the field of Global Navigation Satellite Systems (GNSS). This paper analyzes a low-noise amplifier design with the operation of concurrent dual-band for GNSS receivers with a single channel. The optimized design of a low-noise amplifier in this field uses a low-cost 0.18µm CMOS tube and adds load capacitances and a feedforward path to reduce the noise figure (NF) of the system. Noise figures in 1.2 GHz and 1.57 GHz bands are reduced by about 0.9 dB. In addition, the high gain, high linearity, and other excellent properties are shown in 1.2 GHz and 1.57GHz. In the application of 5G/6G network technology, the optimized design of Gallium Arsenide (GaAs) PHEMT technology with a gate length of 0.3µmm and inductively degraded common source topology phase structure is adopted. From this optimization, the noise figure is optimized to 1.3-1.4, and the gain of the system is 20-22 dB. At the same time, good linearity is also shown. Then, the prospects of semiconductor materials such as Gallium Nitride (GaN) and Indium Phosphide (InP), which have the advantages of high electron mobility and low-cost design, in optimizing low noise operational amplifiers. To a certain extent, the in-depth study of the optimal design of LNA can improve the technical level of wireless communication, aerospace, and other fields.

Hollow Magnetic Nanocarrier-Based Microrobot Swarms for NIR-Responsive Targeted Drug Delivery and Synergistic Therapy.

Nanocarriers are frequently used for drug delivery due to their large surface area, biocompatibility, and photothermal effects. However, they face the problem of premature drug leakage during drug transport. To address this challenge, we developed near-infrared light (NIR)-responsive hollow magnetic nanocarriers (HMC) by incorporating a chitosan-based molecular valve onto hollow magnetic nanocarriers (CHMC) to enable NIR-triggered drug release. Despite this advancement, this material still encounters the challenge of inadequate targeting. Recognizing the efficacy of magnetically driven micro/nanorobot swarms in remote wireless control, targeted motion, and efficient transport, we merged CHMC with magnetically controlled micro/nanorobot swarms. We evaluated their performance under programmable magnetic fields, which can be precisely controlled in biological fluid and directed toward targeting cells. Additionally, they demonstrated the ability to execute a responsive drug release under NIR irradiation. Ultimately, we confirmed their capacity for targeted delivery, responsive drug release, and photothermal therapy for liver cancer treatment in vivo. This approach heralds new possibilities for responsive drug therapy facilitated by micro/nanorobot swarms, offering promising advancements in medical treatment.

Comparative Validation of Scintillator Materials for X-Ray-Mediated Neuronal Control in the Deep Brain.

When exposed to X-rays, scintillators emit visible luminescence. X-ray-mediated optogenetics employs scintillators for remotely activating light-sensitive proteins in biological tissue through X-ray irradiation. This approach offers advantages over traditional optogenetics, allowing for deeper tissue penetration and wireless control. Here, we assessed the short-term safety and efficacy of candidate scintillator materials for neuronal control. Our analyses revealed that lead-free halide scintillators, such as Cs3Cu2I5, exhibited significant cytotoxicity within 24 h and induced neuroinflammatory effects when injected into the mouse brain. In contrast, cerium-doped gadolinium aluminum gallium garnet (Ce:GAGG) nanoparticles showed no detectable cytotoxicity within the same period, and injection into the mouse brain did not lead to observable neuroinflammation over four weeks. Electrophysiological recordings in the cerebral cortex of awake mice showed that X-ray-induced radioluminescence from Ce:GAGG nanoparticles reliably activated 45% of the neuronal population surrounding the implanted particles, a significantly higher activation rate than europium-doped GAGG (Eu:GAGG) microparticles, which activated only 10% of neurons. Furthermore, we established the cell-type specificity of this technique by using Ce:GAGG nanoparticles to selectively stimulate midbrain dopamine neurons. This technique was applied to freely behaving mice, allowing for wireless modulation of place preference behavior mediated by midbrain dopamine neurons. These findings highlight the unique suitability of Ce:GAGG nanoparticles for X-ray-mediated optogenetics. The deep tissue penetration, short-term safety, wireless neuronal control, and cell-type specificity of this system offer exciting possibilities for diverse neuroscience applications and therapeutic interventions.

A Machine Learning–Based Ergonomic Assessment of Wireless Hand Control System for Lower‐Limb Disabled Tractor Operators and Abled Female Agricultural Workers

ABSTRACTTractor being the most used power source for agricultural operations needs hand control (HC) and foot control (FC) to maneuver it. FCs restrict lower‐limb disabled agricultural workers from participating in tractor operation, and high requirement of actuation forces to operate FCs may create overexertion and early fatigue to female agricultural workers. Therefore, a sensor‐based HC system has been developed to assist them in tractor operation with minimal actuating force. This study focuses on ergonomic assessment of the HC system to assess the suitability for the abled and disabled agricultural workers, including physiological, psychophysical, and muscle fatigue parameters. Heart rate (HR) of abled male and female, and disabled male and female was observed in the range of 83–118, 85–117, 93–118, and 92–114 beats/min, respectively, during tractor operation. Energy expenditure rate (EER) during tractor operation with FCs (9.7–17.4 kJ/min) was observed higher than with the HC system (7.3–16.5 kJ/min). Body parts discomfort was observed highest for the right hand of all the subjects (4.9–5.3) and maximum overall discomfort was experienced by abled females during the operation with FCs (5.4) as they have to exert higher force. The root mean square (RMS) value of the electromyography signal obtained for extensor digitorum muscle was found to be higher for all the subjects and with both HC and FC (abled male, 17.37–40.43 µV; abled female, 14.76–45.29 µV; disabled male, 15.49–40.23 µV; disabled female, 30.32–54.29 µV) than other upper arm muscles middle deltoid, flexor carpi radialis, and brachioradialis. Muscle workload for all the selected muscles of all the subjects was observed within the recommended limit during the tractor operation with a developed HC system (< 30%). Categorization of overall discomfort rating (ODR) of the subjects using HR, EER, and RMS through machine learning algorithms such as k‐nearest neighbor (KNN), random forest classifier, and support vector machine predicted the ODR with accuracies in the range of 77%–83%. KNN algorithm was found to be most accurate with prediction accuracy of 83%. The developed HC system provides assistantship to the lower‐limb disabled agricultural workers (1%–100% disability of lower limbs) and allows female workers to operate the tractor with minimal physical exertion.

METHOD OF RECOGNITION OF FPV-UAV RADIO SIGNALS FORMED ACCORDING TO CROSSFIRE AND EXPRESSLRS STANDARDS

Unmanned aerial vehicles (UAVs) with the First Person View (FPV) system are the most common type of attack UAVs used at the tactical level by the armed forces of the russian federation. Timely detection of facts of their using by the enemy and countering them are important tasks solved by units of the Ukrainian Defense Forces. An effective countermeasure way for FPV-UAVs is to create interference in their radio control channels frequency bands. To increase the radio suppression range, it is necessary to use interference matched in frequency and structure, the necessary condition for the formation of which is the presence of a priori information about the structure of the radio signal. Analysis of the available information on the construction of enemy FPV-UAVs shows that their radio control and telemetry channels are usually combined, have time division multiple access, the signals are formed according to CrossFire and ExpressLRS standards and have fixed sets of parameter values that can be used as features in recognition. The proposed FPV-UAV radio signal recognition technique is based on decision tree and minimum metric methods and uses a priori information on frequency, time and modulation parameters of radio signals. The combination of the two decision-making methods made it possible to reduce the computational complexity by eliminating operations for determining the parameters of radio signals that do not correspond to the decisions taken at the previous stages, and provides the possibility of adding new time parameters without changing the developed recognition technique. The method consists of a sequence of operations for estimation radio signal parameters, comparing them with known values, making decisions about the type of standard and command- telemetry radio line operation mode. Only one fragment with a duration of one frequency element is sufficient to recognize the FPV-UAV radio signal, which reduces the technical requirements for detection means in which the method can be used.

棉花精量铺膜穴播机自动断膜带系统设计与试验

To achieve the process of automatic film cutting for cotton and improve the efficiency of cotton precision film hole seeding machine operation, this paper proposes a wireless handle button control method and designs a automatic film and tape cutting system for cotton precision film-laying hole seeder. The system includes electromagnetic integrated valve group, hole opener lifting hydraulic cylinder, membrane pressing hydraulic cylinder, multifunctional hydraulic cylinder for cutting film, strapping support hydraulic cylinder, hydraulic limit sensor, arc-shaped film cutting shovel reset sensor, wireless control handle for film cutting and film cutting controller. The system is based on the VisualTFT platform, equipped with a multi-functional touch screen. By integrating functions such as obtaining the forward distance of the machine, hydraulic control principles, and wireless control models for film cutting and burying, it achieves automatic cutting and burying of ground film and drip tape processes. The experiment verified the working performance of the automatic film cutting and feeding system of the cotton seed precision film mulching hole seeder and the results showed that the average monitoring accuracy of the machine's forward distance was 98.16%; the response time for controlling the film cutting belt and burying film belt was ≤0.78s, and the execution time was ≤2.68s; the success rate of cutting film strips was 100.00% and the success rate of burying film strips was above 98.33%, which met the operational requirements of automatic film strip cutting for precise cotton seeding film hole planting machines.

Power Control for NN-Based Wireless Distributed Inference With Improved Model Calibration

Power Control for NN-Based Wireless Distributed Inference With Improved Model Calibration

A Stability-Guarantee Beamforming Scheme in Multi-Loop Wireless Control Systems

A Stability-Guarantee Beamforming Scheme in Multi-Loop Wireless Control Systems

A Review on Enhancing Crop Yield through Smart Irrigation

Improving irrigation efficiency is crucial for the sustainability of agricultural production. The rise of smart irrigation techniques, particularly with advancements in wireless communication systems, monitoring devices, and control techniques, has the potential to significantly enhance irrigation efficiency. This study explores a broad spectrum of scientific approaches to smart irrigation, comparing and analyzing various methodologies to understand the effectiveness of these technologies. The research spans a wide range of topics, including irrigation methods, decision-making processes, and the technologies employed in smart irrigation. The information was meticulously gathered from a variety of scientific papers, with a particular focus on documents published over the past four years by authors from around the globe. Special attention was also given to various irrigation initiatives that demonstrate the practical application of these techniques. The subsequent evaluation in this study focuses on the critical components of smart irrigation, such as real-time irrigation scheduling, the Internet of Things (IoT), the significance of a reliable internet connection, smart sensing technologies, and energy harvesting. These components are essential for developing efficient and sustainable irrigation practices that can meet the challenges faced by modern agriculture.

A vibro-impact remote-controlled capsule in millimeter scale: Design, modeling, experimental validation and dynamic response

This paper represents a new solution to the design problem of integrating sufficient power source, actuator, and controller into a "pill-sized" form of a typical capsule. All components are enclosed in an 11 mm diameter, 36 mm long cylinder weighing 7.26 gs. The actuator was designed so that a battery with a capacity of 35 mAh can theoretically supply sufficient energy for 7 h of operation. The excitation's amplitude, frequency, and duty cycle are remotely controlled by a wireless pulse width modulation signal. All steps of the new system development are fully described, including conceptual and embodiment design, fabrication, experimental setup, and parameter identification. The mathematical model is then experimentally validated and used to analyze the system response, where the bifurcation technique is applied to carry out the coexisting attractions and their basins. Recommendations on design and operational parameters are then provided, considering progression and energy efficiencies. The results provide the feasibility of further studies to realize vibro-impact driven and remote-controlled capsules using wireless control in standard size.

Patterned wireless transcranial optogenetics generates artificial perception.

Synthesizing perceivable artificial neural inputs independent of typical sensory channels remains a fundamental challenge in the development of next-generation brain-machine interfaces. Establishing a minimally invasive, wirelessly effective, and miniaturized platform with long-term stability is crucial for creating a clinically meaningful interface capable of mediating artificial perceptual feedback. In this study, we demonstrate a miniaturized fully implantable wireless transcranial optogenetic encoder designed to generate artificial perceptions through digitized optogenetic manipulation of large cortical ensembles. This platform enables the spatiotemporal orchestration of large-scale cortical activity for remote perception genesis via real-time wireless communication and control, with optimized device performance achieved by simulation-guided methods addressing light and heat propagation during operation. Cue discrimination during operant learning demonstrates the wireless genesis of artificial percepts sensed by mice, where spatial distance across large cortical networks and sequential order-based analyses of discrimination performance reveal principles that adhere to general perceptual rules. These conceptual and technical advancements expand our understanding of artificial neural syntax and its perception by the brain, guiding the evolution of next-generation brain-machine communication.

Development and Application of a Liquid Sampling Device with Timing, Quantitative and Depth Determination

This thesis describes the development process of a liquid sampling device that can be timed, quantitative, and deep. The structural composition, working principle, and innovative features of the liquid sampling device are introduced, and the application prospects are analyzed. The device has the function of controllable and adjustable sampling point depth and sampling volume, and adopts quantitative collection and collection methods without contact with accessories. The device has a silent function in the sample body, which can realize pre-booking of collection time points and multi-point synchronous collection. Wireless remote control automatic mode is adopted for sending and stopping collection instructions, which can be sent one-to-one or one-to-many at the same time. It provides automation links for water quality monitoring projects and meets the requirements of modern scientific and technological development.

Real-time optical-wireless cooperative switching on wireless quality degradation for end-to-end video delay guarantee

This paper proposes an optical-wireless cooperative control method to guarantee end-to-end delays in the case of radio quality degradation in wireless networks by switching optical paths. The method leverages the control information from mobile systems to dynamically switch the optical path between the distributed unit (DU) and the far aggregated central unit (CU) to another path forward to the nearest virtualized central unit (vCU) at the edge sight, on the basis of the radio quality. This enables demanding applications that require a combination of high bandwidth and low latency, such as remote operation using drones. Preliminary simulations were conducted to investigate the effect of quality degradation in the wireless domain and the potential effectiveness of the proposed delay compensation method. In the experiments conducted to verify the feasibility of the proposed method, the results demonstrated that the optical path switching was successfully performed without packet loss and within 4 ms from the transmission of the control frame. The proposed method was also able to correctly switch back when the processing rate of mobile edge computing (MEC) was exceeded after the radio quality recovered. This study demonstrates the effectiveness of further coordination between wired and wireless control for a wider range of real-time applications using video.