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Electrical Engineering and Systems Science > Systems and Control
Title: Optimal Resource Allocation in Wireless Control Systems via Deep Policy Gradient
(Submitted on 25 Oct 2019)
Abstract: In wireless control systems, remote control of plants is achieved through closing of the control loop over a wireless channel. As wireless communication is noisy and subject to packet dropouts, proper allocation of limited resources, e.g. transmission power, across plants is critical for maintaining reliable operation. In this paper, we formulate the design of an optimal resource allocation policy that uses current plant states and wireless channel states to assign resources used to send control actuation information back to plants. While this problem is challenging due to its infinite dimensionality and need for explicit system model and state knowledge, we propose the use of deep reinforcement learning techniques to find neural network-based resource allocation policies. In particular, we use model-free policy gradient methods to directly learn continuous power allocation policies without knowledge of plant dynamics or communication models. Numerical simulations demonstrate the strong performance of learned policies relative to baseline resource allocation methods in settings where state information is available both with and without noise.
Submission history
From: Vinicius Lima Silva [view email][v1] Fri, 25 Oct 2019 18:43:25 GMT (355kb)
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