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Adaptive Dual-Layer Sliding Mode control for Ball on Plate System Driven by Shape Memory Alloy Wire

Journal of The Institution of Engineers (India): Series B 2024年第4期105卷 989-997页

作者： Sundareswari, M. Banu Mozhi, G. Then Dhanalakshmi, K. Department of Instrumentation and Control Engineering National Institute of Technology Tamil Nadu Tiruchirappalli India

This paper describes a dual-layer adaptive sliding mode control for a ball-on-plate system propelled by shape memory alloy (SMA) wire actuators. Conventionally, hydraulic, pneumatic, and electric actuators are used to power ball-balancing systems. However, they are heavy and bulky and take up more space. SMA actuators offer a solution to these drawbacks due to their unique actuating properties. Naturally, two-dimensional ball-balancing systems exhibit nonlinearity, which is further intensified when integrated with an SMA actuator. Moreover, these systems are sensitive to external disturbances, and controlling such systems with uncertainty using conventional control strategies is difficult. These concerns motivated the design of the dual-layer sliding mode controller (SMC) for the SMA driven ball on plate system to mitigate the causes of nonlinearities. The design is then validated by experimentation using Simulink/MATLAB™. © The Institution of Engineers (India) 2024.

关键词： Robust control

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Enhancing System Performance with Gain-Scheduled Pid controllers in Biological Systems

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Journal of The Institution of Engineers (India): Series B 2024年 1-12页

作者： Vilas, Jadhav Asutkar, V.G. Instrumentation and Control Shri Guru Gobind Singh Institute of Engineering & Technology Nanded India

The research on Gain Scheduling P.I.D. controllers for L.P.V. (Linear Parameter Varying) Systems follows a three-step methodology. Firstly, an appropriate plant is chosen for designing the controller with gain-scheduled PD, and its transfer function is derived. The controller with gain-scheduled P.I.D. will next be implemented in the identified plant transfer function. Finally, the outcomes of applying the GS PID controller are contrasted with those of a conventional P.I.D. controller utilizing L.M.I. (Linear Matrix Inequality) Recurrent Gain Scheduling for comparison. The focus on L.P.V. systems acknowledges the variations in system parameters, allowing the controller to adapt to different operating conditions. The study aims to assess the performance improvement achieved by the GS PID controller compared to the conventional P.I.D. in handling parameter changes. Challenges in this context may include identifying an accurate plant transfer function, determining appropriate scheduling parameters, and addressing potential instability or overshooting issues associated with varying system dynamics. Using L.M.I. recurrent gain scheduling suggests a systematic approach to ensure stability and robustness, but challenges may arise in tuning the L.M.I. parameters effectively. The study contributes valuable insights into the effectiveness and challenges of gain-scheduled P.I.D. controllers for L.P.V. systems, providing a basis for further advancements in control strategies for systems with parameter variations. © The Institution of Engineers (India) 2024.

关键词： Three term control systems

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Traffic sign recognition using deep learning

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International Journal of Vehicle Autonomous Systems 2023年第2-4期16卷 97-107页

作者： Patel, Vraj Mehta, Joy Iyer, Saurab Sharma, Ankit K. Instrumentation and Control Engineering Department Institute of Technology Nirma University Gujarat India

Recognition of traffic signs is an integral step towards achieving Advanced Driver Assistance Systems (ADAS) as distracted driving is one of the primary causes of road accidents and fatalities. This paper attempts to exploit the capabilities of Convolutional Neural Networks (CNN) to recognise traffic signs under various computational and environmental constraints. The German Traffic Sign Recognition Benchmark (GTSRB) dataset is used for the classification of images. The dataset is subjected to various image processing techniques like greyscaling, denoising, filtering, and thresholding to obtain a generalised model for the recognition of traffic signs. The neural network used here comprises three convolution layers each followed by a max pooling layer which further are followed by four fully connected dense layers. The models are trained for 100 epochs with a validation split of 20%. The model performs best with 'Adam' optimiser with a learning rate of 0.001. © 2022 Inderscience Enterprises Ltd.

关键词： Traffic signs

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Bidirectional Position control of a Prismatic joint for Motorized Single Link Robotic Arm Using Adaptive Super- Twisting Sliding Mode control

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Journal of The Institution of Engineers (India): Series B 2023年第5期104卷 1035-1042页

作者： Then Mozhi, G. Banu Sundareswari, M. Dhanalakshmi, K. Department of Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli Tiruchirappalli620015 India

Design of the dual layer adaptive super-twisting sliding mode control (ASTSMC) strategy for position control of a motorized single link flexible robotic arm is presented in this *** system comprises of a cantilever beam, the robotic arm whose horizontal sliding enables positioning at the required length. Prismatic joint at the fixed extremity of the flexure is positioned at the desired point (the set length) by a linear actuation system- the DC motor and the lead-screw. The parameters and hence the model of the DC motor is determined using the nonlinear least-square (NLS) parameter estimation method. The flexible manipulator is modeled using the lumped parameter model approach. The super twisting algorithm and adaptive law are designed in combination to control the position of the robotic arm;the performance of the adaptive SMC controller is compared with that of the classical PID controller and SMC. The simulated and experimental results show that positioning accuracy is ensured and the response time is improved by the ASTSMC controller. © 2023, The Institution of Engineers (India).

关键词： Robotic arms

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IoT for Smart Parking System

IoT for Smart Parking System

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Sondkar, Shilpa Momin, Muhib Nadaf, Sumaiyya Najare, Kaushik Shevkari, Niraj Vishwakarma Institute of Technology Instrumentation and Control Engineering Pune India

ISBN: (纸本)9798331517953

Current smart parking systems primarily use technologies like ultrasonic sensors, RFID, and image recognition to detect parking space occupancy and assist drivers in finding available spots. These methods often face challenges in terms of accuracy, especially in dynamic environments with fluctuating lighting conditions, vehicle types, or obstructions. The proposed system introduces significant improvements by incorporating YOLOv8, an advanced deep learning-based object detection model, to enhance the precision and speed of vehicle detection. By utilizing real-time video feeds and robust data processing, this system can track vehicles more accurately, even in complex environments. Moreover, it offers real-time updates via an HTTP protocol, directly transmitting data to a web-based interface, ensuring seamless user interaction for parking guidance and space availability monitoring. This approach not only reduces time spent searching for parking but also enhances the overall user experience and operational efficiency compared to traditional systems. © 2024 IEEE.

关键词： HTTP

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Parkinson’s Disease Detection: Comparative Study Using Different Machine Learning Algorithms 3rd

Parkinson’s Disease Detection: Comparative Study Using Diff...

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3rd International Conference on Computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Bhanuse, Vijaykumar Chirame, Ankita Beri, Isha Instrumentation and Control Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819966899

Detection of Parkinson’s disease is often established on clinical monitoring and evaluation of medical signs, which include an indication of different motor signs. Furthermore, the ordinary point of view encounters rationality as it is based on analysis of motions that are specifically not recognized, tough to categorize, and steers miscategorization. Since Parkinson’s disease sufferers have distinct voice characteristics, vocal recordings are needed and highly regarded as a diagnostic tool. If Machine Learning techniques can truly note this disease with the help of a voice recording dataset, this would be a useful screening phase before seeing a doctor. This chapter shows comparison of some ML models like XGboost, random forest, Naive Bayes, logistic regression, SVM, decision tree, and K-NN to get the best result for detection of disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Preprocessing of CT Scans for Lung Cancer Detection 4

Preprocessing of CT Scans for Lung Cancer Detection

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Singh, Akanksha Kamath, Surekha Manipal Institute of Technology Instrumentation and Control Engineering Manipal India

ISBN: (纸本)9798350349900

Lung cancer, a pervasive global health concern, requires effective early detection and classification methodologies to improve patient outcomes. This project addresses this challenge by developing a comprehensive framework utilizing medical imaging data for the timely classification of lung cancer with better results of diagnosis. Leveraging advanced processing techniques and DL algorithms, the project aims to make the lung cancer detection system more efficient. By preprocessing DICOM image datasets of lung scans and employing techniques such as noise reduction, contrast enhancement, and feature extraction, the project seeks to improve image quality and extract relevant features. © 2024 IEEE.

关键词： Lung cancer

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An ensemble transfer learning-based deep convolution neural network for the detection and classification of diseased cotton leaves and plants

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Multimedia Tools and Applications 2024年第36期83卷 83991-84024页

作者： Rai, Chitranjan Kumar Pahuja, Roop Department of Instrumentation and Control Engineering Dr. B R Ambedkar National Institute of Technology Punjab Jalandhar144008 India

Agriculture is important for the economy of any country, and India is considered to be an agricultural country. One of the primary goals of agriculture is to produce disease-free crops. Since ancient times, farmers and other planting specialists have had to contend with a variety of problems and current agricultural constraints, such as widespread cotton diseases. There is a great need for a rapid, efficient, economical, and reliable approach to diagnosing cotton infection in the agri-informatics area, as severe cotton disease may result in the loss of grain crops. This paper presents an advanced method that automates the detection and classification of diseased cotton leaves and plants through deep learning techniques applied to images. To address the challenge of supervised image classification, we employ a bagging ensemble technique consisting of five transfer learning models: InceptionV3, InceptionResNetV2, VGG16, MobileNet, and Xception. This ensemble approach was adopted to significantly improve the performance of each individual mode. The ETL-NET framework we introduced was thoroughly evaluated using two publicly accessible datasets. Specifically, it achieved an impressive accuracy rate of 99.48% and a sensitivity rate of 99% when applied to binary datasets. Additionally, on the multi-class dataset, the framework achieved an accuracy rate of 98.52% and a sensitivity rate of 99%. Our method outperformed the state-of-the-art techniques and displayed comparatively better results. Remarkably, our approach demonstrated even higher performance than widely used ensemble techniques, generally considered benchmarks in the field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolution

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Low-power system model for quantum entangled photon-pair source

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Journal of Systems engineering and Electronics 2024年第5期35卷 1287-1294页

作者： FENG Tianxuan ZHANG Hanyi FAN Rong MA Honghao DONG Mengcheng LI Lijing School of Instrumentation and Optoelectronic Engineering Beihang UniversityBeijing 100191China Engineering Training Center Beihang UniversityBeijing 102206China Beijing Aerospace Automatic Control Institute Beijing 100854China

The quantum entangled photon-pair source,as an essential component of optical quantum systems,holds great potential for applications such as quantum teleportation,quan-tum computing,and quantum *** current workhorse technique for preparing photon pairs involves performing spon-taneous parametric down conversion(SPDC)in bulk nonlinear ***,the current power consumption and cost of preparing entangled photon-pair sources are relatively high,pos-ing challenges to their integration and *** this paper,we propose a low-power system model for the quantum entan-gled photon-pair source based on SPDC theory and phase matching *** model allows us to analyze the per-formance of each module and the influence of component cha-racteristics on the overall *** our experimental setup,we utilize a 5 mW laser diode and a typical type-II barium metabo-rate(BBO)crystal to prepare an entangled photon-pair *** experimental results are in excellent agreement with the model,indicating a significant step towards achieving the goal of low-power and low-cost entangled photon-pair *** achievement not only contributes to the practical application of quantum entanglement lighting,but also paves the way for the widespread adoption of optical quantum systems in the future.

关键词： low-power system model optical quantum system entangled photon-pair source spontaneous parametric down conversion

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Knee Joint Vibroarthrographic Signal Analysis and Classification Using Wavelet Transform 4

Knee Joint Vibroarthrographic Signal Analysis and Classifica...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Durge, Gaurav Joshi, Ashutosh Jaid, Atharva Vishwakarma Institute of Technology Department of Instrumentation and Control Engineering Pune India

ISBN: (纸本)9798350352931

The knee joint is susceptible to various pathological conditions that alter its biomechanical properties, leading to changes in vibrational characteristics detectable through Vibroarthrographic (VAG) signals. VAG signals present a valuable early diagnostic tool for knee joint disorders, owing to their nonstationary and nonlinear characteristics. Although previous research has developed different time and frequency-based parameters for VAG signal assessment and classification, limited exploration has been devoted to statistical modeling methods in the spectral domain. This study seeks to contribute to the field by classifying VAG signals into normal and abnormal categories, reflecting the health condition of knee joints, through the introduction of a novel set of features derived from wavelet analysis. This study uses a comprehensive approach integrating wavelet decomposition and sub-band feature extraction to analyze VAG signals. It focuses on spectral domain transformations using wavelet transform to extract statistical characteristic features like Energy, Shannon Entropy, Kurtosis, Skewness, and Standard Deviation, which capture fluctuations in VAG signals. The classification task is then performed by using a Support Vector Machine (SVM) and Random Forest algorithms. The proposed system achieves an impressive overall classification accuracy of 93.33% for the Random Forest classifier. This novel approach, leveraging wavelet analysis and statistical features, demonstrates the promising potential for accurate screening, with an ability to effectively classify more than 90% of subjects. © 2024 IEEE.

关键词： Wavelet decomposition

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