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. Assess the build quality of parts generated through control model algorithms. Validate that methodologies developed are transferrable between different LPBF platforms through evaluation of parts generated
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explore data-driven methods including machine learning (ML) and artificial intelligence (AI) techniques, to develop predictive HMPM tools that can diagnose, detect, and predict faults in machinery
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Development of Digital Twin Models for Real-Time Condition Monitoring of Electrical Machines in Electric Vehicle Applications School of Electrical and Electronic Engineering PhD Research Project
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decision with multiple data sources. One example is to develop the semi-supervised methods and dynamic system interfacing algorithms to produce an automated and real-time information exchange across
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/adaptive algorithms, offline and online data analysis, conducting experimental research, and online evaluation of the developed adaptive strategies with a robotic application. The prospective students can
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intelligent sensing, followed by detection of the important events.In the light of autonomous decision making, the project aims at developing machine learning algorithms for knowledge extraction from data
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Optimization-based control explores the use of optimization algorithms for feedback control of dynamical systems. For example, model predictive control (MPC) is a widely used optimization-based control method
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in handling stall onset and recovery. This project will focus on the modelling and control of hysteresis effects, under the framework of numerical optimal control. The aim is to develop advanced
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adapted based on the abilities and needs of patients. Moreover, automatic intelligent algorithms will be developed in to make the control intuitive, natural and adaptive. Such that the model can learn new
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Deadline: Applications accepted all year round Details The aim of this project is to develop scalable and efficient techniques and algorithms for localisation in different environments, based on data in