TR2017-120

Co-design of Safe and Efficient Networked Control Systems in Factory Automation with State-dependent Wireless Fading Channels


    •  Hu, B., Wang, Y., Orlik, P.V., Koike-Akino, T., Guo, J., "Co-design of Safe and Efficient Networked Control Systems in Factory Automation with State-dependent Wireless Fading Channels", arXiv, August 2017.
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      • @techreport{MERL_TR2017-120,
      • author = {Hu, B. and Wang, Y. and Orlik, P.V. and Koike-Akino, T. and Guo, J.},
      • title = {Co-design of Safe and Efficient Networked Control Systems in Factory Automation with State-dependent Wireless Fading Channels},
      • institution = {MERL - Mitsubishi Electric Research Laboratories},
      • address = {Cambridge, MA 02139},
      • number = {TR2017-120},
      • month = aug,
      • year = 2017,
      • url = {http://www.merl.com/publications/TR2017-120/}
      • }
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  • Research Areas:

    Advanced Control Systems, Electronics & Communications, Mechatronics, Wireless Communications


In factory automation, heterogeneous manufacturing processes need to be coordinated over wireless networks to achieve safety and efficiency. These wireless networks, however, are inherently unreliable due to shadow fading induced by the physical motion of the machinery. To assure both safety and efficiency, this paper proposes a state-dependent channel model that captures the interaction between the physical and communication systems. By adopting this channel model, sufficient conditions on the maximum allowable transmission interval are then derived to ensure stochastic safety for a nonlinear physical system controlled over a state-dependent wireless fading channel. Under these sufficient conditions, the safety and efficiency co-design problem is formulated as a constrained cooperative game, whose equilibria represent optimal control and transmission power policies that minimize a discounted joint-cost in an infinite horizon. This paper shows that the equilibria of the constrained game are solutions to a non-convex generalized geometric program, which are approximated by solving two convex programs. The optimality gap is quantified as a function of the size of the approximation region in convex programs, and asymptotically converges to zero by adopting a branch-bound algorithm. Simulation results of a networked robotic arm and a forklift truck are presented to verify the proposed co-design method.