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Digital twins: Understanding the added value of integrated models for through-life engineering services
Digital twins are digital representations of physical products or systems that consist of multiple models from various domains describing them on multiple scales. By means of communication, digital twins change and evolve together with their physical counterparts throughout their lifecycle. Domain-specific partial models that make up the digital twin, such as the CAD model or the degradation model, are usually well known and provide accurate descriptions of certain parts of the physical asset. However, in complex systems, the value of integrating the partial models increases because it facilitates the study of their complex behaviours which only emerge from the interactions between various parts of the system. The paper proposes that the partial models of the digital twin share a common model space that integrates them through a definition of their interrelations and acts as a bridge between the digital twin and the physical asset. The approach is illustrated in a case of a mechatronic product - a differential drive mobile robot developed as a testbed for digital twin research. It is demonstrated how the integrated models add value to different stages of the lifecycle, allowing for evaluation of performance in the design stage and real-time reflection with the physical asset during its operation
Design and Implementation of Takagi-Sugeno Fuzzy Tracking Control for a DC-DC Buck Converter
This paper presents the design and implementation of a Takagi-Sugeno (T-S) fuzzy controller for a DC-DC buck converter using Arduino board. The proposed fuzzy controller is able to pilot the states of the buck converter to track a reference model. The T-S fuzzy model is employed, firstly, to represent exactly the dynamics of the nonlinear buck converter system, and then the considered controller is designed on the basis of a concept called Virtual Desired Variables (VDVs). In this case, a two-stage design procedure is developed: i) determine the reference model according to the desired output voltage, ii) determine the fuzzy controller gains by solving a set of Linear Matrix Inequalities (LMIs). A digital implementation of the proposed T-S fuzzy controller is carried out using the ATmega328P-based Microcontroller of the Arduino Uno board. Simulations and experimental results demonstrate the validity and effectiveness of the proposed control scheme
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