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The use of model-based technologies has made it imperative for the development of a feedback control system to deal with many different tasks such as: plant modeling in all its variety; model reduction to achieve a complexity or level of abstraction suitable for the design task at hand; synthesis of control laws that vary from discrete event reactive control to continuous model predictive control, their analyses, and testing; design of the implementation; modeling of the computational platform and its operating system; analysis of the implementation effects; software synthesis for different platforms to facilitate rapid prototyping, hardware-in-the-loop simulation, etc. Throughout these tasks, different formalisms are used that are very domain specific (e.g., tailored to electrical circuits, multibody systems, reactive control algorithms, communication protocols) and that often need to be coupled, integrated, and transformed (e.g., a block diagram control law in the continuous domain has to be discretized and then implemented in software). Significant improvements in many aspects (performance, cost, development time) of the development process can therefore be achieved by: 1) relating and integrating these different formalisms; 2) automatic derivation of different levels of modeling abstractions; and 3) rigorous and tailored design of the different formalisms by capturing the domain (or meta) knowledge. The emerging field of computer automated multiparadigm modeling (CAMPaM), presented in this paper in the context of control system design, aims to develop a domain-independent formal framework that leverages and unifies different activities in each of these three dimensions.