Networked Control of Nonlinear Systems under Partial Observation Using Continuous Deep Q-Learning
This addresses networked control systems where delays and partial observations pose challenges, but it is incremental as it builds on existing deep Q-learning methods.
The paper tackles the problem of designing a model-free networked controller for nonlinear systems with unknown models, partial state observation, and fluctuating network delays, achieving a control policy robust to delay fluctuations through simulation.
In this paper, we propose a design of a model-free networked controller for a nonlinear plant whose mathematical model is unknown. In a networked control system, the controller and plant are located away from each other and exchange data over a network, which causes network delays that may fluctuate randomly due to network routing. So, in this paper, we assume that the current network delay is not known but the maximum value of fluctuating network delays is known beforehand. Moreover, we also assume that the sensor cannot observe all state variables of the plant. Under these assumption, we apply continuous deep Q-learning to the design of the networked controller. Then, we introduce an extended state consisting of a sequence of past control inputs and outputs as inputs to the deep neural network. By simulation, it is shown that, using the extended state, the controller can learn a control policy robust to the fluctuation of the network delays under the partial observation.