Tracking Control of a Continuous Stirred Tank Reactor Using Direct and Tuned Reinforcement Learning Based Controllers

Abstract

Abstract The need for linear model, of the nonlinear system, while tuning controllers limits the use of classic controllers. Also, the tuning procedure involves complex computations. This is further complicated when it is necessary to operate the nonlinear system under different operating constraints. Continues Stirred Tank Reactor (CSTR) is one of those non-linear systems which is studied extensively in control and chemical engineering due to its highly non-linear characteristics and its diverse operating range. This paper proposes two different control schemes based on reinforcement learning algorithm to achieve both servo as well as regulatory control. One approach is the direct application of Reinforcement Learning (RL) with ANN approximation and another is tuning of PID controller parameters using reinforcement learning. The main objective of this paper is to handle multiple set point control for the CSTR system using RL. The temperature of the CSTR system is controlled here for multiple setpoint changes. A comparative study is also done between the two proposed algorithm and from the test result, it is seen that direct RL approach with approximation performs better than tuning a PID using RL as oscillations and overshoot are less for direct RL approach. Also, the learning time for the direct RL based controller is lesser than the later.