A Neural Reinforcement Learning Approach to Gas Turbine Control

Abstract

In this paper a new neural network based approach to control a gas turbine for stable operation on high load is presented. A combination of recurrent neural networks (RNN) and reinforcement learning (RL) is used. The authors start by applying an RNN to identify the minimal state space of a gas turbine's dynamics. Based on this the optimal control policy is determined by standard RL methods. The authors proceed to the recurrent control neural network, which combines these two steps into one integrated neural network. This approach has the advantage that by using neural networks one can easily deal with the high dimensions of a gas turbine. Due to the high system-identification quality of RNN one can further cope with the only limited amount of available data. The proposed methods are demonstrated on an exemplary gas turbine model where, compared to standard controllers, it strongly improves the performance.