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Model-Based Real-Time Control

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Part of the Springer Tracts in Advanced Robotics book series (STAR,volume 93)

Abstract

Automated modeling paves the way for more advanced uses of models built in a bottom-up fashion. In this chapter, we describe an extension of the M3 framework (Chapter 9) that leverages the concepts of CRN and Markov Decision Process (MDP) to achieve the controlled formation of target assemblies of Lily modules (Case Study IV). Figure 12.1 depicts the global structure of the control framework. The system is monitored by an overhead camera and analyzed using SwisTrack (Section 3.2.3). The resulting trajectories are then used by the M3 framework to build the CMM, and the equivalent CRN, in real time. Finally, the optimal mode of agitation is determined using the optimization scheme described in Section 12.1, and transmitted to the pumps at regular time intervals. The control loop is closed by incorporating the state changes resulting from this choice in the model.

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  • DOI: 10.1007/978-3-319-02609-1_12
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Correspondence to Grégory Mermoud .

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Mermoud, G. (2014). Model-Based Real-Time Control. In: Stochastic Reactive Distributed Robotic Systems. Springer Tracts in Advanced Robotics, vol 93. Springer, Cham. https://doi.org/10.1007/978-3-319-02609-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-02609-1_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02608-4

  • Online ISBN: 978-3-319-02609-1

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