Electric unmanned aerial vehicles (UAVs) demanding complex maneuvering for various applications require precise control of the brushless direct current (BLDC) motor, an essential part of the electric propulsion system. The present work suggests adaptive input-output feedback linearization (IOFL) based field-oriented control (FOC) for precise and quick tracking of the reference speed demanded by flight controllers with tolerable torque ripples. The IOFL control generates the reference voltages for a three-phase inverter circuit. The suggested approach performs reliably even when the temperature changes, owing to the adaptive mechanism that calculates the stator resistance. An enhanced metaheuristic algorithm based on stochastic fractal search (SFS) helps optimize the tuning parameters for PI regulators. In addition, the SFS algorithm, compared with particle swarm optimization (PSO), shows superior performance and faster convergence of the SFS algorithm. The Lyapunov stability analysis helps verify the stability of the proposed controller to ensure reliable functioning under all operating circumstances. Finally, the proposed scheme’s operational performance is evaluated through TMS320F28379D microcontroller and BOOSTXL-DRV8305EVM three-phase inverter module under UAVs’ take-off, hovering, and landing states and compared with classical FOC to demonstrate convergence, robustness, and effectiveness.

The deployment of unmanned aerial vehicles (UAVs) to perform complex maneuvers requires precise control of sensorless motors. However, chattering in UAV brushless direct current (BLDC) motor propeller systems, reaching steady-state in a short time, and physical sensors pose challenges. Field-oriented backstepping control (FOBSC) offers a superior dynamic response, and field-oriented input-output feedback linearizing control (FOIOFL) minimizes torque ripple and power consumption at steady-state. This article proposes a hybrid controller that combines FOBSC and FOIOFL control approaches with a novel rule-based switching algorithm to optimize dynamic and steady-state control performance, such as fast convergence, improved tracking accuracy, and reduced chatter, in the speed and torque of the BLDC motor drive. Besides, a model reference adaptive system disturbance observer is integrated with the proposed hybrid controller to enhance overall performance and actively reject disturbances. Furthermore, instead of using complicated physical sensors for UAV applications, an enhanced adaptive sliding mode observer is developed to estimate real-time rotor positions and speed and adapt variation in stator resistance, improving stability and performance. The stability of the proposed control strategy is validated using Lyapunov approach in all operational conditions. Various numerical and experimental test scenarios are conducted to find the effectiveness and superiority of the proposed sensorless active-disturbance rejection hybrid control approach compared to the individual FOBSC, FOIOFL control, and standard controllers.

A hybrid drone called a biplane quadrotor operates in both low (during the horizontal flight) and high (transition maneuver) Angle of Attack (AoA). So, this paper focuses on enhancing aerodynamic force during the transition maneuver. Synthetic Jet actuators (SJAs) can modify airfoil shapes virtually, so either the flow reattaches or flow separation will be delayed. This delay can enhance the aerodynamic force. In this paper, CFD analysis is performed using Ansys Fluent to study the impact of SJA on NACA 0015 airfoil at high (30(degrees)) AoA. This study aims to find the best location of SJA for high AoA to get maximum lift enhancement. The outcome of this study reveals that at 85 %, we can get maximum enhancement in the lift.

The conventional box-type solar cooker relies on a sole heat source, the sun, and integrates both manual and passive tracker mechanisms to improve its heating performance. Manual tracking necessitates frequent alignment of the reflector towards the sun’s position under sunshine, while passive tracking systems require particular attention at the start and end of the process. The literature frequently explores the tracking mechanisms for the cooker’s reflector or the base, highlighting the lack of active tracking mechanisms for the box-type model. This paper explores the creation of a groundbreaking self-powered active sun-tracking actuator system and secondary heating system, elucidating the fundamental arrangement of essential elements. This foundational model is crafted using the Computer-Aided Design (CAD) software Fusion 360. The innovative design revolves around two single-degree-of-freedom (1 DoF) electromechanical actuators and a retrofitted heating element tailored for a box-type solar cooker. The heating element and electromechanical actuators are powered by electricity generated within the cooker model. To harness electric power, an array of thermoelectric generators (TEGs) leveraging the Seebeck effect is integrated into the cooker’s wall. Additionally, the overall design considers the needs of urban and hilly terrain regions, as well as considerations for ease of transportation focusing cooker’s weight, torque requirement of the actuator, and overall cost of the cooker. The 3D model of the cooker’s actuators undergoes static and dynamic testing to assess the design’s feasibility, strength, and functionality. Additionally, an analysis of the approximate energy flow is conducted. The static stress test is conducted in Fusion 360 software and the dynamic test for torque under loaded conditions is conducted in MATLAB multibody simulation. The overall weight becomes 18.45 kg and the prototype cost will be predicted to be Rs.7554/- only. The aluminum 6061 T4 material sustains wind stress of 10 N with a maximum displacement of 4.08 mm which does not impact reflection on the aperture area. The motor torque required to move the base is 0.3 Nm and for reflection motion is 7 Nm. The Thermoelectric Generator (TEG) produces a total of 28 Ah of energy, of which 19.6 Ah will be available for cooking during periods without sunshine after accounting for all consumption. This design is well-suited for nomad communities, villagers, tribal people, small urban households, adventurers, and those living in hilly and remote areas.

Multi-link inverted pendulum systems pose intricate challenges in control theory and robotics, requiring precise dynamic parameter identification to achieve stability and robustness in control strategy design. We present a novel and efficient experimental identification procedure formulated as an optimization problem based on simple short-term datasets and metaheuristic global optimizers. We use a training dataset for identification and validation dataset to evaluate and analyze the obtained results. The study incorporates three distinct global optimization techniques, namely Stochastic Fractal Search (SFS), Growth Optimizer, and Differential Evolution (DEoptim), selected as candidates to handle the identification of multi-link pendulums and similar extremely demanding optimization jobs to be used when controlling modern mechatronic systems. We illustrate that DEoptim dominates over other global optimizers in several aspects. The proposed identification procedure is innovative, adaptable, and simple, relying solely on selected measurable signals sans further signal processing. Its versatility makes it a valuable tool for parameter identification in diverse domains. The results are supported by experiments with the laboratory triple pendulum setup and simulation experiments on a virtual quadruple inverted pendulum.

Solar farms have PV arrays in arid and semi-arid regions where ensuring the system's reliability is paramount and face uncertain events like dust storms. The deposition of random dust patterns over panel arrays is called uneven soiling, which diminishes the power generation of such farms. This paper finds the most suitable hybrid algorithm model, the wavelet transform-based support vector regression variants (WT-SVR) algorithm, and the empirical model decomposition-based support vector regression variants (EMD-SVR) to predict the extent of soiling levels and uncertain events on PV arrays. The soiling dataset is taken from NREL's Soiling Station Number 3 in Imperial County, Calipatria, California, from December 30, 2014, to December 31, 2015. This research tested four SVR variants on soiling data, viz., εSVR, LSSVR, TSVR, and εTSVR, then compared with the benchmark random forest. The hyperparameters for each model are meticulously tuned to enhance the robustness of the trained algorithms. Results reveal that the WT-TSVR model outperforms the WT-SVR model in terms of wavelet transform decomposition by a margin of 91.6%. Similarly, the EMD-TSVR model showcases an 85.7% enhancement in performance over the EMD-SVR model based on empirical mode decomposition. All SVR variants outperform the benchmark model (RF). Furthermore, EMD models exhibit enhanced efficiency in forecasting random events compared to WT, which is attributed to their reduced computational time. This model applies to multi-cleaning agent robots, aligning with recommendations from the state-of-the-art literature.

The actuator serves as a motion converter, helps transform the speed and direction of motion, and is a motion controller for precise positioning. The actuator facilitates automated movement and is pivotal in determining the machine's performance. Engineers must consider three factors when choosing an actuator: the motor capacity, the reduction ratio of the gearbox, and an encoder. Electro-mechanical actuators fulfill various functions, including precise component positioning, maintaining desired positions, providing ample force or torque for securing closure mechanisms, and enabling controlled acceleration and deceleration at specified speeds. While on power, they excel at holding the desired position, but under off-power conditions, they also securely hold the load in place with less energy consumption. The main objective is to choose an ideal motor and gearhead combination for effectively holding the load when the power is off. This selection process considers crucial factors like output torque, speed, peak power, mass, and size. The research involves a comparative analysis between power-on load holding and power-off load holding in terms of consumed power, control circuitry, stability, and cost. Analytical calculations for finding the optimal motor torque and gearhead (gearbox) are conducted, and a Simscape Multibody simulation of a pendulum system is performed for validation purposes. This study helps select a suitable motor gearhead combination for power-off load holding by comparing the calculated values with simulation results. The method presented here is universally applicable to different types of loads, providing a valuable tool for selecting the most appropriate motor gearhead combination.

Electrically driven unmanned aerial vehicles (UAVs) are gaining popularity due to use in industrial, military, and civil applications. The UAVs have to execute complicated maneuvers in the air requires accurate control of the BLDC motor propeller systems. In this study, we propose easy to implement field-oriented adaptive input-output feedback linearizing control (AIOFL) for controlling propellers as per demand of flight controller. This study aims to compare the proposed field-oriented AIOFL with usual six step control architecture with a focus on the typical back-electromotive force (back-EMF) shapes featured in the propeller motor. The proposed control architecture that does not only regulate speed and torque of the propeller with acceptable torque ripple but it also estimates the rotor magnetic flux and the stator resistance of the BLDC motor to know about stator/rotor condition monitoring, motor fault detection, and temperature rise. To ensure reliable operation in all operational conditions, closed loop stability of proposed speed controller is analyzed based on the Lyapunov method. Particle swarm optimization (PSO) is utilized to accurately tune the PI controller since the trial-and-error technique used to choose the PI controller gains resulted in the low stability and poor transient response of the controller. Finally, comprehensive numerical and experimental tests are performed and compared with conventional field-oriented control to evaluate the effectiveness and robustness of the proposed control system.

Unmanned aerial vehicles (UAVs) powered by electricity are becoming increasingly popular for civil, military, and commercial applications. Accurately controlling the brushless direct current (BLDC) motor propeller system is necessary for UAVs to perform complex maneuvers in the air. A field-oriented backstepping control (FOBSC) is proposed for enhancing the speed control performance of the propeller motor drives as per the demand of the flight controller. The FOBSC is designed to minimize input control efforts and dynamically adopts various beneficial characteristics such as high tracking accuracy, quick convergence, and reduced chattering in control input of the BLDC motor propeller drive. For improved overall performance of the propeller motor drive under internal and external disturbances, especially wind gusts, the rotor speed-based MRAS disturbance observer (MRASDO) has been developed and integrated with FOBSC. An MRAS estimator based on stator current is developed to estimate real-time rotor position and speed, removing the need for physical sensors, which is more complex for UAV applications. The sensorless control algorithm adapts variations in stator resistance and rotor magnetic flux of the BLDC motor, which enhances performance and stability and provides information on temperature rise, motor fault detection, and stator/rotor condition monitoring. The closed-loop stability of the MRAS estimator, MRASDO, and FOBSC is carried out using the Lyapunov method to guarantee reliable operation under any operational conditions. Finally, comprehensive numerical and experimental tests demonstrate that the proposed sensorless FOBSC approach is superior to sliding mode control and classical PI control.

If augmented with fixed-wing design, air vehicles with the capability to land and take off vertically, such as quadcopters, become promising candidates for missions needing longer hover endurance, faster travel times, and higher payload capacities. Therefore, a backstepping controller (BSC) design of a biplane quadrotor during payload delivery, like vaccines in a remote area, and an adaptive backstepping controller (ABC) to handle mass changes despite wind gusts during the mission. Furthermore, we compare BSC, Integral Terminal Sliding Mode (ITSMC), and ABC for payload delivery to demonstrate effective tracking of the desired trajectory through ABC. The ITSMC also tracks the desired trajectory with mass change but provides a sluggish response, and BSC produces a steady-state altitude error whenever a significant mass change happens. Hybrid development of drones like the biplane quadrotor facilitates improved efficiency and impact through innovative control configurations. A step-by-step process of development of a biplane quadrotor is offered [1]. While Sridharan et al. [2] developed a range and load approximation technique on a biplane tail-sitter quadrotor. Dawkins et al. developed a mathematical model and PID control of a micro quadrotor [3]. Task scheduling and a path planning problem are addressed by Mathew et al. for cooperating vehicles enabling autonomous shipment in metropolitan landmarks [4], while Liu et al. presented formation control of a swarm of tail-sitters [5]. A robust nonlinear control action is developed wherein the coordinate system and the controller structures don’t need to switch [6]. Wagter et al. developed a Linear-Quadratic Regulator (LQR) controller [7]. Mofid et al. addressed a sensor failure scenario where a PID-SMC controller is used for a known upper bounded disturbance, and an adaptive PID-SMC for an unknown disturbance [8]. Finally, Muthusamy et al. proposed a new fuzzy brain emotional learning control for a quadrotor UAV trajectory tracking for real-time payload uncertainties [9].

A biplane quadrotor (hybrid vehicle) benefits from rotary-wing and fixed-wing structures. We design a dual observer-based autonomous trajectory tracking controller for the biplane quadrotor. Extended state observer (ESO) is designed for the state estimation, and based on this estimation, a Backstepping controller (BSC), Integral Terminal Sliding Mode Controller (ITSMC), and Hybrid Controller (HC) that is a combination of ITSMC + BSC are designed for the trajectory tracking. Further, a Nonlinear disturbance observer (DO) is designed and combined with ESO based controller to estimate external disturbances. In this simulation study, These ESO-based controllers with and without DO are applied for trajectory tracking, and results are evaluated. An ESO-based Adaptive Backstepping Controller (ABSC) and Adaptive Hybrid controller (AHC) with DO are designed, and performance is evaluated to handle the mass change during the flight despite wind gusts. Simulation results reveal the effectiveness of ESO-based HC with DO compared to ESO-based BSC and ITSMC with DO. Furthermore, an ESO-based AHC with DO is more efficient than an ESO-based ABSC with DO.

The desired changes in flow characteristics are obtained by flow control, which implies manipulating flow behavior such as drag reduction, mixing augmentation, or noise attenuation, employing active or passive devices [...]

People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.

One of the core ideas behind the development of UAVs is payload delivery in remote areas. For such applications, a biplane quadrotor is a better choice than the rotary-wing UAVs because after the payload delivery biplane quadrotor can fly like a fixed-wing UAV and get back to the origin, which saves energy and time. This chapter proposes a novel control structure to stabilize a slung load attached to the vehicle.