6,726 research outputs found

    The Efficiency of an Optimized PID Controller Based on Ant Colony Algorithm (ACO-PID) for the Position Control of a Multi-articulated System

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    In this article, a robot manipulator is controlled by the PID controller in a closed loop system with unit feedback. The difficulty of using the controller is parameter tuning, because the tuning parameters still use the trial and error method to find the PID parameter constants, namely Proportional Gain (Kp), Integral Gain (Ki) and Derivative Gain (Kd). In this case the Ant colony Optimization algorithm (ACO) is used to find the best gain parameters of the PID. The Ant algorithm is a method of combinatorial optimization, which utilizes the pattern of ants search for the shortest path from the nest to the place where the food is located, this concept is applied to tuning PID parameters by minimizing the objective function such that the robot manipulator has improved performance characteristics. This work uses the Matlab Simulink environment, First, after obtaining the system model, the ant colony algorithm is used to determine the proper coefficients p, i, and Kd in order to minimize the trajectory errors of the two joints of the robot manipulator. Then, the parameters will implement in the robot system. According to the results of the computer simulations, the proposed method (ACO-PID) gives a system that has a good performance compared with the classical PID

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    An incremental input-to-state stability condition for a generic class of recurrent neural networks

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    This paper proposes a novel sufficient condition for the incremental input-to-state stability of a generic class of recurrent neural networks (RNNs). The established condition is compared with others available in the literature, showing to be less conservative. Moreover, it can be applied for the design of incremental input-to-state stable RNN-based control systems, resulting in a linear matrix inequality constraint for some specific RNN architectures. The formulation of nonlinear observers for the considered system class, as well as the design of control schemes with explicit integral action, are also investigated. The theoretical results are validated through simulation on a referenced nonlinear system

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence

    Optimization of passive ultrafast fiber lasers based on indium nitride for novel applications

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    Los láseres ultrarrápidos en fibra constituyen una de las fuentes de luz más utilizadas actualmente debido a su fiabilidad y flexibilidad, convirtiéndose en la pieza clave de múltiples aplicaciones, como las comunicaciones ópticas, el procesamiento de materiales o la espectroscopía. Entre ellos, los láseres en fibra anclado en modos basados en el uso de absorbentes saturables demuestran características superiores de estabilidad, simplicidad y bajo coste, capaces de emitir pulsos ultracortos con potencias extremadamente altas en un amplio rango espectral. En las últimas décadas, se han probado varios absorbentes saturables, donde los materiales de semiconductor destacan por su amplia profundidad de modulación, su elevada absorción no lineal y su baja intensidad de saturación. Sin embargo, presentan algunas limitaciones como un estrecho ancho de banda y un bajo umbral de daño. Por tanto, en este trabajo se propone el uso de un semiconductor de InN en un láser todo en fibra anclado en modos para la generación de láseres ultrarrápidos de alta potencia en la región del infrarrojo cercano. Esta configuración ha demostrado trenes de pulsos Gaussianos en el rango de los femtosegundos mediante un sistema sencillo y de bajo coste. En esta tesis, el objetivo es optimizar las características de un láser de fibra anclado en modos basado en un absorbente saturable de InN, y desarrollar un novedoso dispositivo espectroscópico para aplicaciones de detección. Primeramente, se estudia la mejora de las propiedades del absorbente saturable de semiconductor mediante un mayor control del dopaje residual así como del crecimiento de material, demostrando el máximo comportamiento no lineal para este tipo de absorbentes saturables en un láser en fibra. También se discute como estas características podrían mejorarse mediante el desarrollo de un nuevo diseño de láser totalmente en fibra, capaz de contrarrestar las limitaciones actuales de ruido y perdidas de inserción dentro de la cavidad láser. De este modo, se demuestra la duración de pulso más corta y la máxima potencia óptica, conservando una configuración sencilla, lo que allana el camino hacia el desarrollo de sistemas láser comerciales en aplicaciones de alta potencia. A continuación, se introducen nuevas aplicaciones potenciales del sistema láser de fibra: en la detección de gases, mediante la generación de supercontinuo del pulso láser ultrarrápido en fibras monomodo capaces de cubrir espectros de absorción más amplios; y en la caracterización de moléculas biológicas mediante el uso de una novedosa estructura espectroscópica SF-CARS conectada a la fuente láser totalmente en fibra. Además, se exponen las implicaciones del chirp-matching en el rendimiento de la medición de la absorción, y el impacto de la dispersión y los efectos no lineales generados por diferentes fibras ópticas en la compresión y el ensanchamiento de los pulsos de fibra ultrarrápidos. La configuración láser propuesta supera la máxima resolución medible y la cobertura espectral, las limitaciones más importantes a las que se enfrenta la espectroscopía moderna. Finalmente, se resumen los objetivos alcanzados en esta tesis, evaluando el potencial de las aplicaciones propuestas, así como futuras líneas de investigación basadas en dichos hallazgos

    Energy-Aware, Collision-Free Information Gathering for Heterogeneous Robot Teams

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    This paper considers the problem of safely coordinating a team of sensor-equipped robots to reduce uncertainty about a dynamical process, where the objective trades off information gain and energy cost. Optimizing this trade-off is desirable, but leads to a non-monotone objective function in the set of robot trajectories. Therefore, common multi-robot planners based on coordinate descent lose their performance guarantees. Furthermore, methods that handle non-monotonicity lose their performance guarantees when subject to inter-robot collision avoidance constraints. As it is desirable to retain both the performance guarantee and safety guarantee, this work proposes a hierarchical approach with a distributed planner that uses local search with a worst-case performance guarantees and a decentralized controller based on control barrier functions that ensures safety and encourages timely arrival at sensing locations. Via extensive simulations, hardware-in-the-loop tests and hardware experiments, we demonstrate that the proposed approach achieves a better trade-off between sensing and energy cost than coordinate-descent-based algorithms.Comment: To appear in Transactions on Robotics; 18 pages and 16 figures. arXiv admin note: text overlap with arXiv:2101.1109

    Optimization of a PID Controller within a Dynamic Model of a Steam Rankine Cycle with Coupled Energy Storage

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    Fusion energy is an appealing option for future energy generation, but also presents unique design challenges. The UK Atomic Energy Authority is leading the Spherical Tokamak for Energy Production (STEP) programme to build a fusion power plant capable of net electricity generation. This work addresses the use of dynamic models in an optimization framework for the design of the thermal power generation cycle for STEP. The optimization of a proportional-integral-derivative controller regulating the power output of a steam Rankine cycle with a coupled thermal energy storage system is presented. A lumped-parameter dynamic model of the system has been implemented. The effectiveness of a controller design is evaluated by simulating the system under a perturbation to the power demand on the system. By minimizing the mean absolute power deviation, there is a reduction of 97 % compared to the initial controller design, as well as a reduction of 95 % in the maximum absolute power deviation and a faster return to setpoint. The optimized design does introduce more oscillations in the system, which are undesirable for control systems and are challenging for the optimization procedure

    A Survey on Multi-Active Bridge DC-DC Converters: Power Flow Decoupling Techniques, Applications, and Challenges

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    Multi-port DC-DC converters are a promising solution for a wide range of applications involving multiple DC sources, storage elements, and loads. Multi-active bridge (MAB) converters have attracted the interest of researchers over the past two decades due to their potential advantages such as high power density, high transfer ratio, and galvanic isolation, for example, compared to other solutions. However, the coupled power flow nature of MAB converters makes their control implementation difficult, and due to the multi-input, multi-output (MIMO) structure of their control systems, a decoupling control strategy must be designed. Various control and topology-level strategies are proposed to mitigate the coupling effect. This paper discusses the operating principles, applications, methods for analyzing power flow, advanced modulation techniques, and small signal modelling of the MAB converter. Having explained the origin of cross-coupling, the existing power flow decoupling methods are reviewed, categorized, and compared in terms of effectiveness and implementation complexity

    A Machine Learning-oriented Survey on Tiny Machine Learning

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    The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures. TinyML carries an essential role within the fourth and fifth industrial revolutions in helping societies, economies, and individuals employ effective AI-infused computing technologies (e.g., smart cities, automotive, and medical robotics). Given its multidisciplinary nature, the field of TinyML has been approached from many different angles: this comprehensive survey wishes to provide an up-to-date overview focused on all the learning algorithms within TinyML-based solutions. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature survey. In particular, firstly we will examine the three different workflows for implementing a TinyML-based system, i.e., ML-oriented, HW-oriented, and co-design. Secondly, we propose a taxonomy that covers the learning panorama under the TinyML lens, examining in detail the different families of model optimization and design, as well as the state-of-the-art learning techniques. Thirdly, this survey will present the distinct features of hardware devices and software tools that represent the current state-of-the-art for TinyML intelligent edge applications. Finally, we discuss the challenges and future directions.Comment: Article currently under review at IEEE Acces

    Comparison of LSTM, Transformers, and MLP-mixer neural networks for gaze based human intention prediction

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    Collaborative robots have gained popularity in industries, providing flexibility and increased productivity for complex tasks. However, their ability to interact with humans and adapt to their behavior is still limited. Prediction of human movement intentions is one way to improve the robots adaptation. This paper investigates the performance of using Transformers and MLP-Mixer based neural networks to predict the intended human arm movement direction, based on gaze data obtained in a virtual reality environment, and compares the results to using an LSTM network. The comparison will evaluate the networks based on accuracy on several metrics, time ahead of movement completion, and execution time. It is shown in the paper that there exists several network configurations and architectures that achieve comparable accuracy scores. The best performing Transformers encoder presented in this paper achieved an accuracy of 82.74%, for predictions with high certainty, on continuous data and correctly classifies 80.06% of the movements at least once. The movements are, in 99% of the cases, correctly predicted the first time, before the hand reaches the target and more than 19% ahead of movement completion in 75% of the cases. The results shows that there are multiple ways to utilize neural networks to perform gaze based arm movement intention prediction and it is a promising step toward enabling efficient human-robot collaboration
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