Hardware Development for Cyber-Physical Systems adaptive control algorithms

信息物理系统（CPS）是计算与物理过程的集成。嵌入式计算机和网络监视和控制物理过程，通常具有反馈回路，其中物理过程影响计算，反之亦然。近十年来，CPS因其自适应控制特性而受到重视。为此，为CPS提供精确的自适应控制需要解决几个挑战，包括当系统受到外部干扰和存在不确定性时的实时物理模型识别。最近提出的基于估计的多模型切换自适应控制（EMMSAC）在存在不确定性和外部干扰的情况下具有较好的自适应控制性能。这些算法已在数字信号处理（DSP）平台上实现。一般来说，这些算法需要大量的计算资源和减少的执行时间。为了应对所有这些挑战，设计师可以依靠越来越成熟的数字电子技术。更先进的实现受到速度的限制。此外，在网络物理系统中，如果我们希望自适应控制物理系统，EMMSAC应该在微秒或纳秒内执行操作，从而使物理系统能够在预定时间内适应控制器指令。为了实现上述特性，我们打算在现场可编程门阵列（FPGA）上开发这些EMMSAC算法。这将有助于建立一个高效、稳定、准确、可靠的网络物理系统。在工作的初始阶段，我们在Virtex超大规模FPGA上开发了一个2x2卡尔曼滤波器，这是EMMSAC算法的基础模块。我们还开发了一个通过适应道路条件来控制汽车速度的案例研究。未来，我们打算将这些算法应用于升压变换器，我们可以在实验室环境中开发不同负载条件下的升压变换器。此外，我们打算增加数量型号50-400。未来的下一个挑战是实现EMMSAC的动态在线改进策略。

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.