Hardware Development for Cyber-Physical Systems adaptive control algorithms

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Cyber-Physical Systems (CPS) are integrations of computation with physical processes.Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical process affect computations and vice versa. CPS gained a lot of importance from last decade because of its self-adaptive control nature.To this end, there are several challenges need to be addressed for providing accurate self adaptive control to CPS, including physical model identification in real time when the system subjected to external disturbances and in the presence of uncertainty. Recently proposed estimation based multiple model switched adaptive control (EMMSAC) are shown that the best performance in the presence of uncertainty and external disturbances over the state-of-the-art algorithms for controlling physical system adaptively.These algorithms have been implanting on the Digital Signal Processing (DSP) platform. In general, these algorithms need a lot of computing resources and reduced execution time. To cope with all these challenges, designers can rely on more and more mature digital electronics technologies. Further state-of-the-art implementations are limited by speed. Moreover in cyber-physical system if we desired to control physical system adaptively, EMMSAC should perform operations in micro or nanoseconds thereby physical system will able to adapt the controller instructions within the scheduled time. To achieve aforementioned characteristics we are intended to develop these EMMSAC algorithms on field programmable gate array (FPGA). This will help to build an efficient, stable, accurate and reliable cyber-physical system. In the initial stage of work, we developed a 2x2 kalman filter on Virtex ultra-scale FPGA, which is the fundamental block in the EMMSAC algorithm. We have also developed a case study for controlling the velocity of the car by adapting the road conditions.In future, we are intended to apply these algorithms for boost converter which we can develop in our lab environment under different load conditions. Also we are intended to increase number models 50-400. The next future challenge is to implement the dynamiconline refinement strategy for EMMSAC.