Sensor-based Control Research Articles

Confidence Weighted Learning Entropy for Fault-Tolerant Control of a PMSM With a High-Resolution Hall Encoder

There are several non-idealities that can degrade magnetic Hall-effect sensors performance and impact related applications. Thus, a confidence weighted learning entropy (CWLE) is proposed as a fault-tolerant control strategy for field-oriented control (FOC) of permanent magnet synchronous machines (PMSM). It combines sensorless and sensor-based control, while capitalizing on their major advantages, such as operation from standstill and at lower speeds, fast dynamic response, and fault tolerance to encoder errors. Encoder fault detection is based on learning entropy, that monitors weights increments of two predictive filters of angular displacement. If the two observed systems behave similarly, the variance of the weights increments is similar. A higher variance, on the other hand, reflects unforeseen misbehavior of a particular system, leading to a decrease in its confidence. A voting mechanism based on confidence weighted average then decides which of the two systems should be used for FOC of PMSM. The method has been experimentally verified on a high-speed PMSM, achieving more reliable control performance with fast response to encoder failures.

Efficiency and Elegance: Exploring Automated Solutions for Public Lighting

This paper explores the intersection of efficiency and elegance in the realm of automated solutions for public lighting. With the rapid advancement of technology, cities around the world are increasingly turning to automated systems to illuminate their streets, parks, and public spaces. These systems offer not only enhanced efficiency in energy consumption but also the opportunity to elevate the aesthetic appeal of urban environments. Through a comprehensive review of existing literature and case studies, this research examines the benefits and challenges associated with automated public lighting solutions. It delves into the technological innovations driving these systems, including sensor-based controls, adaptive lighting algorithms, and integration with smart city infrastructure. Furthermore, the paper explores the role of automated lighting in promoting sustainability and reducing carbon emissions, as well as its impact on public safety and community well-being. By considering factors such as cost-effectiveness, scalability, and user acceptance, it seeks to provide insights into the practical implementation of automated lighting solutions in diverse urban settings. Ultimately, this study aims to shed light on the potential of automated public lighting to not only enhance efficiency but also contribute to the creation of more elegant and livable cities. It underscores the importance of embracing innovation and leveraging technology to create vibrant and sustainable urban environments for current and future generations. When the lights are on it'll dependably stay on until any labor turns off them. So that the support and the fiscal consumption application is more. It might bear checking the board constantly. The crunches of conventional road lighting frame work not only convey premonitory burden to the near plutocrat related part. Yet in addition it confines the administration and keep of the lighting system which leads to the vexation to the public since several lights are n’t glowing and complaints for them has n’t been made. The advancement of green lighting is introductory. An crop among the most abecedarian mortal advancement records is the enhancement of a drop transportation association. These thorough fares and roads must be immaculately lit up with the target that an acceptable perceivable quality is assured to dwindle the failure rate and expansion of the shot of vehicle.

A self-sensing approach for estimating suspension displacement and velocity in semi-active electromagnetic dampers

Electromagnetic damper (EMD) suspension systems have gained significant recognition for their fast response and precise control capabilities. Nonetheless, the necessity of sensor-based control escalates system costs too high and limits its application. This paper introduces an innovative self-sensing approach for semi-active EMD that aims to autonomously acquire vehicle suspension state information without external sensors. The goal is to improve the performance of vibration damping while remaining cost-efficiency. Initially, the EMD is designed and modelled, followed by an analysis of the variable damping principle of the permanent magnet synchronous motor (PMSM). The variable damping EMD model is formulated using the machine-electric coupling equation. Subsequently, a detailed explanation of the principle behind the proposed self-sensing approach is presented. Simulation results confirm its precise capability to estimate the suspension displacement and velocity even in the absence of external sensor input. Moreover, bench experiments for the EMD are conducted to validate the effectiveness of the variable damping and self-sensing approach. Experimental results indicate that the damping characteristic of the EMD can be controlled by manipulating external resistance value. In addition, accurate estimation of suspension displacement and velocity can be achieved under varying excitation conditions, even with changes in damper damping.

Sensor-Based Planning and Control for Conformal Deposition on a Deformable Surface Using an Articulated Industrial Robot

Abstract Robotic manipulators can be used to deposit materials on non-planar surfaces. Conventional sensor-based industrial robots can only work on stationary surfaces, relying on the scanned data prior to printing. As a result, performing depositions that involve changes in plane motion presents significant challenges. The deposition of conformal materials on a time-varying deformable surface requires the manipulators to update coordinates in real time on the plane for positioning and orientation. This can be achieved by employing multiple sensors for manipulator motion planning and control, in order to prevent collisions between the tool and the surface. In this paper, we propose simple tool center point calibration, initial point coordinate estimation, and a gap compensation scheme to combine real-time feedback control and direct conformal deposition. Combining these elements allows us to maintain a controlled gap between the tooltip and the deformable surface during the deposition. We test the efficacy of the proposed approach by printing a single layer of ink patterns with approximately 950 μm line width on a deformable surface. We also characterize the printing quality with different gaps and printing steps and show that sensor-based control is critical in smooth printing. Finally, the effects of changing the relative position of the tooltip, different surface colors, and laser sensor position are characterized.

Robust Stability and Performance Analysis of Incremental Dynamic-Inversion-Based Flight Control Laws

Incremental nonlinear dynamic inversion (INDI) is a sensor-based control law design strategy that is based on the principles of feedback linearization. Contrary to its nonincremental counterpart (nonlinear dynamic inversion), this design method does not require a complete onboard model of the airframe dynamics and is therefore more robust against regular perturbations arising from aerodynamic variations. Therefore, INDI brings a natural design approach to desirable flying qualities. However, robustness against singular perturbations, which may arise due to transport delays, elastic airframe effects, or other types of badly modeled or unknown dynamics, is a known challenge for INDI-based control laws. Therefore, this paper addresses the question of robust stability and performance for INDI and its linear form (incremental dynamic inversion [IDI]) in the context of mixed regular and singular perturbations. This is done through analytical insights and by performing quantitative robustness assessments based on the structured singular value framework. Additionally, inversion loop augmentation solutions are investigated using robust synthesis techniques to improve the robustness characteristics of basic IDI designs.

Applying mass flow analysis and aeration optimization strategy to reduce energy consumption of a full-scale anaerobic/anoxic/oxic system

A systematic aeration control strategy aiming at energy saving was proposed using a comprehensive model composing of a mass flow analysis (MFA) module, an oxygen transfer rate (OTR) module and an aeration optimization module. The model was calibrated and validated using data sets from a full-scale anaerobic/anoxic/oxic (AAO) process wastewater treatment plant (WWTP). The MFA module was extended with a detailed description of total oxygen demand (TOD). The oxygen supply was predicted considering the negative correlation between soluble COD and α factor, which quantified the dynamic effect of wastewater contaminants on aeration equipment. Energy saving potential (ESP) was assessed via model-predictive optimum air supply. After the implementation of the aeration control strategy, the ESP of the blowers was reduced from 26.3 % to 12.2 %, without worsening the overall pollutant removal. As the aeration system can respond in advance to the varying influent characteristics, model-predictive control together with sensor-based control holds promise for application in full-scale WWTPs for energy conservation.

Steering angle sensorless control for four-wheel steering vehicle via sliding mode control method

This paper presents a new sensorless control method for four-wheel steering vehicles. Compared to the existing sensor-based control, this approach improved dynamic stability, manoeuvrability and robustness in case of malfunction of the front steering angle sensor. It also provided a software redundancy and backup solution, as well as improved fault tolerance. The strategy of the sensorless control is based on the sliding mode method to estimate the replacement of the front steering input from the errors between the vehicle’s measured and desired values of the vehicle’s sideslip angle and yaw rate. The simulation results demonstrate that the observer effectively estimated the front wheel steering angle at both low- and high-speed scenarios in the cornering and lane change manoeuvres. Furthermore, the sensorless control approach can achieve equivalent control performances to the sensor-based controller including a small and stable yaw rate response and zero sideslip angle. The results of the study offer a potential solution for improving manoeuvrability, stability and sensor fault tolerance of four-wheel steering vehicles.

Ultrasonic sensor-based automatic control volume sprayer for pesticides and growth regulators application in vineyards

A study was conducted to design a sensor-based control volume sprayer for vineyard cultivation. The study aimed to develop cluster-specific pesticide and growth regulator application systems with improved penetration and minimum off-target losses, thus saving chemicals and overall cost. For the determination of design values of the control volume unit, physical properties (cluster length, width, and level of compactness) of grape clusters from seventeen major genotypes selected from vineyard research farm ICAR-IARI New Delhi were studied. For the design of the grape cluster detection unit, two different types of sensors, i.e., ultrasonic sensor (HC SR-04) and infrared proximity sensor (B0115NCT4U), were evaluated for their ability to detect the target (grape cluster) and the response time. The design of the spraying unit for the control volume sprayer also involved selecting suitable nozzles and operational parameters. The developed sprayer was evaluated for application of plant growth hormone (Gibberellic acid), and the performance was assessed in terms of droplet size, chemical application per cluster, cluster and berry growth characteristics, sprayer application rate, uniformity coefficient, and application time. The data on the physical properties of grape clusters revealed a maximum cluster length and width of 24.00 ± 0.91 cm and 18.80 ± 0.15 cm, respectively. The ultrasonic sensor had a comparatively higher sensing range, beam angle, and lower price than the infrared sensor. The spray uniformity for the hollow cone and flat fan nozzle varied significantly for the operating pressure range of 2–4.5 kg.cm−2. The developed sprayer consisted of a 3D printed control volume unit, ultrasonic sensor, two flat fan nozzles, microcontroller (Atmega 328P), relay switch, voltage converter, spray tank of 16-liter capacity, and 12 VDC battery. In case of developed sensor-based control volume sprayer, 30% and 35.48% saving in chemical use and application time, respectively, were observed compared to the conventional dipping method. The PGR application method has significant interaction with cluster length and berry growth as the main effect; however, failed to show a significant interaction with cluster width. Maximum cluster growth (16.89 ± 1.72 mm) was observed in PGR application with a developed sprayer.

Toward Sustainable Farming: Implementing Artificial Intelligence to Predict Optimum Water and Energy Requirements for Sensor-Based Micro Irrigation Systems Powered by Solar PV

Future trends in climate change, water scarcity, and energy costs will motivate agriculturists to develop innovative agricultural systems. In order to achieve sustainable farming in arid regions, there is an urgent need to use artificial intelligence (AI) to predict and estimate the optimum water and energy requirements for the irrigation of date palms. Therefore, this study aimed to predict the optimum water and energy requirements for date palm irrigation depending on the optimum water use efficiency (WUE) and yield in arid conditions. To achieve this aim, four solar-powered micro irrigation systems were developed and evaluated under six irrigation levels for date palm irrigation. Soil moisture sensor-based controllers were used to automate irrigation scheduling for the micro irrigation systems. The water pumping in these systems was powered using a solar photovoltaic (PV) system. In addition, four machine-learning (ML) algorithms, including linear regression (LR), support vector regression (SVR), long short-term memory (LSTM) neural network, and extreme gradient boosting (XGBoost), were developed and validated for prediction purposes. These models were developed in Python programing language using the Keras library. The results indicated that the optimum WUS was achieved when the maximum setpoints of irrigation control were adjusted at the field capacity and by adjusting the minimum setpoints at 40, 50, 70, and 80% of the available water (AW). The optimum yield was achieved by adjusting the minimum setpoints at 60, 70, 80, and 90% of AW for subsurface irrigation, subsurface drip irrigation, drip irrigation, and bubbler irrigation, respectively. Therefore, the dataset was prepared at these levels for four years to train and test the models, and a fifth year was used to validate the performance of the best model. The evaluation of the models showed that the LSTM followed by XGBoost models were more accurate than the SVR and LR models for predicting the optimum irrigation water and energy requirements. The validation result showed that the LSTM was able to predict the water and energy requirements for all irrigation systems with R2 ranging from 0.90 to 0.92 based on limited meteorological variables and date palm age. The findings of the current study demonstrated that the developed LSTM model can be a powerful tool in irrigation water and energy management as a fast and easy-to-use approach.

Linear Model of a Turboshaft Aero-Engine Including Components Degradation for Control-Oriented Applications

The engine fuel control system plays a crucial role in engine performance and fuel economy. Fuel control, in traditional engine control systems, is carried out by means of sensor-based control methods, which correct the fuel flow rate through correlations or scheduled parameters in order to reduce the error between a measured parameter and its desired value. In the presence of component degradation, however, the relationship between the engine measurable parameters and performance may lead to an increase in the control error. In this research, linear models for advanced control systems and for direct fuel control in the presence of components degradation are proposed, with the main objective being to directly predict and correct fuel consumption in the presence of degradation instead of adopting measurable parameters. Two techniques were adopted for model linearization: Small Perturbation and System Identification. Results showed that both models are characterized by high accuracy in predicting the output engine variables, with the mean errors between model prediction and data below 1%. The maximum errors, recorded for shaft power, were about 6% for Small Perturbation and lower than 3% for System Identification. A simple correlation between engine performance and components degradation was also demonstrated; in particular, the achieved results allow one to conclude that the Small Perturbation approach is the best candidate for controller development when a prediction of components degradation is included.

Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control System

This thesis describes research to prevent maritime safety accidents by notifying navigational signs when sea fog and haze occur in the marine environment. Artificial intelligence, a camera sensor, an embedded board, and an LED marine lantern were used to conduct the research. A deep learning-based dehaze model was learned by collecting real marine environment and open haze image data sets. By applying this learned model to the original hazy images, we obtained clear dehaze images. Comparing those two images, the concentration level of sea fog was derived into the PSNR and SSIM values. The brightness of the marine lantern was controlled through serial communication with the derived PSNR and SSIM values in a realized sea fog environment. As a result, it was possible to autonomously control the brightness of the marine lantern according to the concentration of sea fog, unlike the current marine lanterns, which adjust their brightness manually. This novel-developed lantern can efficiently utilize power consumption while enhancing its visibility. This method can be used for other fog concentration estimation systems at the embedded board level, so that applicable for local weather expectations, UAM navigation, and autonomous driving for marine ships.

Reciprocal Safety Velocity Cones for Decentralized Collision Avoidance in Multi-Agent Systems

In this paper, we solve the inter-agent collision avoidance problem in an arbitrary n-dimensional Euclidean space using reciprocal safety velocity cones (RSVCs). We propose a decentralized feedback control strategy that guarantees simultaneously asymptotic stabilization to a reference and collision avoidance. Our algorithm is purely decentralized in the sense that each agent uses only local information about its neighbouring agents. Moreover, the proposed solution can be implemented using only inter-agent bearing measurements. Therefore, the algorithm is a sensor-based control strategy which is practically implementable using a wide range of sensors such as vision systems and range scanners. Simulation results in a two dimensional environment cluttered with agents shows that the number of possible deadlocks is marginal and decrease with the decrease in the clutteredness of the workspace.

Multisensor Information Fusion for Condition Based Environment Monitoring

Destructive wildfires are becoming an annual event, similar to climate change, resulting in catastrophes that wreak havoc on both humans and the environment. The result, however, is disastrous, causing irreversible damage to the ecosystem. The location of the incident and the hotspot can sometimes have an impact on early fire detection systems. With the advancement of intelligent sensor-based control technologies, the multi-sensor data fusion technique integrates data from multiple sensor nodes. The primary objective to avoid wildfire is to identify the exact location of wildfire occurrence, allowing fire units to respond as soon as possible. Thus to predict the occurrence of fire in forests, a fast and effective intelligent control system is proposed. The proposed algorithm with decision tree classification determines whether fire detection parameters are in the acceptable range and further utilizes a fuzzy-based optimization to optimize the complex environment. The experimental results of the proposed model have a detection rate of 98.3. Thus, providing real-time monitoring of certain environmental variables for continuous situational awareness and instant responsiveness.

Human-robot interactions in manufacturing: A survey of human behavior modeling

Human behavior, despite its complexity, follows structured principles. Better understanding the underlying concepts of human behavior in an industrial setting can help us to create more reliable and effective collaborative robotic environments. In factories, robots can interact with humans in unseen situations through sensing, processing, and predicting human behaviors. This work summarizes the efforts to express human behavior in industrial human-robot interactions (HRI) in manufacturing. For this purpose, all papers related to HRI in production systems, additive manufacturing, agent behavior, human cognition, cloud robotics, cooperative Markov decision process, multi-robot design, and collaborative robots are reviewed in this work. The discussion includes the existing methods, research gaps, and future research directions for envisioning a safer yet more efficient HRI practice. In detail, we discuss current research about human-centered HRI and robot-centered HRI based on the focus areas such as factorial analysis, predictive analysis, and control structures for social and/or industrial/manufacturing robots. In the last part, based on our findings, we report major limitations of the existing literature and propose future research directions such as cognitive modeling, perception development, interaction design, sensor-based control, and social effects in manufacturing.

Design and Validation of Automated Sensor-Based Artificial Ripening System Combined with Ultrasound Pretreatment for Date Fruits

Climate change affects fruit crops’ growth and development by delaying fruit ripening, reducing color development, and lowering fruit quality and yield. The irregular date palm fruit ripening in the past few years is assumed to be related to climatic change. The current study aimed to design and validate an automated sensor-based artificial ripening system (S-BARS) combined with ultrasound pretreatment for artificial ripening date fruits cv. Khalas. A sensor-based control system was constructed to allow continuous real-time recording and control over the process variables. The impact of processing variables, i.e., the artificial ripening temperature (ART-temp) and relative humidity (ART-RH) using the designed S-BARS combined with ultrasound pretreatment variables, i.e., time (USP-Time) and temperature (USP-Temp) on the required time for fruit ripening (RT), the percentage of ripened fruits (PORF), the percentage of damaged fruits (PODF), and the electrical energy consumption (EEC) were investigated. The quadratic predictive models were developed using the Box–Behnken Design (B-BD) to predict the RT, PORF, PODF, and EEC experimentally via Response Surface Methodology(RSM). Design Expert software (Version 13) was used for modeling and graphically analyzing the acquired data. The artificial ripening parameter values were determined by solving the regression equations and analyzing the 3D response surface plots. All parameters were simultaneously optimized by RSM using the desirability function. The Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) between the predicted and actual experimental values were used to evaluate the developed models. The physicochemical properties of the ripened fruit were assessed under the optimization criteria. The results indicated that the pretreated unripe date fruits with 40 kHz ultrasound frequency, 110 W power, and USP-Temp of 32.49 °C for 32.03 min USP-Time under 60 °C ART-Temp and 59.98% ART-RH achieved the best results. The designed S-BARS precisely controlled the temperature and relative humidity at the target setpoints. The ultrasound pretreatment improved the color and density of the artificially ripened date fruits, decreased the RT and EEC, and increased the PORF without negatively affecting the studied fruit quality attributes. The developed models could effectively predict the RT, PORF, PODF, and EEC. The designed S-BARS combined with ultrasound pretreatment is an efficient approach for high-quality ripening date fruits.

Paint With the Sun: A Thermal-Vision Guided Robot to Harness Solar Energy for Heliography

In this article, we present a novel robotic system for heliography, which literally refers to painting with the Sun. In the context of artistic creation, heliography involves the precise manipulation of a magnifying glass to burn artwork on wooden panels by concentrating solar energy. Since this painting procedure exposes the human operator to prolonged periods of intensive sunlight, it can potentially cause sunburns to the skin or damage the eyes. To avoid these issues, in this article, we propose to automate the heliography process with a robot in lieu of a human operator. The proposed thermal servoing capabilities have the potential to robotize various solar power generation technologies such as concentrated solar power (CSP) and concentrated photovoltaics (CPVs). To perform this task, our robotic system is equipped with a magnifying glass attached to the end-effector and is instrumented with vision and thermal sensors. The proposed sensor-based controls enable to automatically: 1) track the orientation of the Sun in real time to maximize the concentrated solar energy; 2) direct the solar rays toward the point of interest; and 3) control the heat power intensity at the target point to achieve the required steady-state temperature. The performance of our system is evaluated by conducting autonomous heliography experiments with various patterns.

Design of Virtual Reality-Based Hippotherapy Simulator Exergaming Software and Its Controller for Rehabilitation of Children with Cerebral Palsy in Indonesia: An Engineering Concept

Horse riding exercise, also known as hippotherapy is a popular treatment for children with cerebral palsy (CP). However, the need for trained therapist, massive land use, and expensive maintenance of the horse ranch makes hippotherapy not affordable or even available for most patients in Indonesia. This problem motivates us to consider mechanical horse riding simulator machines to replace actual horse hippotherapy. However, most patients are children and are easily bored when asked to do monotonous activities for an extended period. The room setting also does not give the patient visual inputs that usually help motivates the children in real-horse hippotherapy activities. To solve this problem, we designed an exercise game (exergaming) software which we named Sirkus Apel, providing the patients with fun activities while doing the therapy. We also design an inertial sensor-based controller that lets the patients control the in-game horse by their back movements, which also benefits CP patients. To make the visual input enjoyable to the user while also considering the user’s safety, we built a convex mirror-based dome virtual reality to provide an immersive 3-D experience. We then project the game content to the dome to provide an immersive experience to the patients making it as if they are riding a real horse inside the game.

Sensor-based design of a Delta parallel robot

This paper proposes a sensor-based design methodology in order to design a Delta robot with guaranteed accuracy performance for a dedicated sensor-based controller. This sensor-based design methodology takes into account the accuracy performance of the controller in the design process in order to find optimal geometric parameters of the robot. Three types of controllers are envisaged to be applied to the Delta robot, leading to three different optimal designs: leg-direction-based visual servoing, line-based visual servoing and image moment visual servoing. Based on these three controllers, positioning error models taking into account the error of observation coming from the camera are developed, and the controller singularities are analyzed. Then, design optimization problems are formulated in order to find the optimal geometric parameters and relevant parameters of the camera for the Delta robot for each type of controller. Prototypes of Delta robots have been manufactured based on the obtained optimum design parameters in order to test the performance of the pair {robot-controller}.

On Radiation-Based Thermal Servoing: New Models, Controls, and Experiments

In this article, we introduce a new sensor-based control method that regulates (by means of robot motion) the temperature of objects that are subject to a radiative heat source. This valuable sensorimotor capability is needed in many industrial, dermatology, and field robot applications, and it is an essential component for creating machines with advanced thermomotor intelligence. To this end, we derive a geometric-thermal-motor model, which describes the relation between the robot’s active configuration and the produced dynamic thermal response. We then use the model to guide the design of two new thermal servoing controllers (one model-based and one adaptive), and analyze their stability with Lyapunov theory. To validate our method, we report a detailed experimental study with a robotic manipulator conducting autonomous thermal servoing tasks. We show that the temperature of multiple objects with unknown thermophysical properties attached to the same end-effector can be effectively regulated by controlled robot motion. Although thermal sensing is a mature technology in many industrial thermal engineering applications, its use as a feedback signal for robot control has not been sufficiently studied in the literature. To the best of our knowledge, this is the first time that temperature regulation is formulated as a motion control problem for robots.

Experimental Investigation of Azimuth- and Sensor-Based Control Strategies for a PV Solar Tracking Application

Today, the world continuously investigates the promising potential of advanced and optimized technologies for harvesting green renewable energy sources, such as a solar tracking system (STS). This study presents an actual implementation of a single-axis solar tracking system (SAST), where an azimuth control scheme is developed to precisely follow the sun’s orientation. This is demonstrated by computing in real-time the optimal azimuth angle based on the actual altitude, date, and time using an embedded microcontroller. At the same time, experimental comparisons using the azimuth-based control method compared with the proportional, integral, derivative (PID) control scheme and fixed-tilt PV systems (FTPV) are derived. Experimental results show that the developed azimuth solar tracker can autonomously follow the sun’s orientation primarily from time and location-based information and independent from the actual solar irradiance. Nevertheless, the electrical energies gained from all three tested systems revealed that using the sensor-based STS would increase the net energy production by 12.68% compared to the azimuth-based SAST, with an energy production increase of 7.7%. In addition, energy consumption by the azimuth-based STS is 65% greater than the energy consumption of a sensor-based STS due to the continuous movements of the tracker, regardless of the increase or change in power production within short periods.