Proportional Integral Derivative Controller Emulation Using Long Short-Term Memory for Temperature Control

Abstract

Driven by the shortcomings of traditional Proportional Integral Derivative (PID) controllers, particularly their limited responsiveness to changes in system parameters, suboptimal performance in nonlinear systems, and challenges in managing disturbances and noise, this study explores an alternative approach. Long Short-Term Memory (LSTM) controllers, designed to mimic PID controller behavior, are anticipated to effectively learn and predict nonlinear system dynamics, exhibit robustness against disturbances and noise, and automatically adapt their parameters. This paper proposes emulating a PID controller using an LSTM network, with the Internet-Based Temperature Control Lab (iTCLab) Kit as the test platform. The results demonstrate successful modeling and emulation of an adaptive PID controller using LSTM on the iTCLab, evidenced by satisfactory control performance, characterized by a relatively slow rise time, minimal overshoot, and low steady-state error.