Automatic tuning of PID controllers using deep recurrent neural networks with pruning based on tracking error

Abstract

Although PID controllers are common in industry, they are often poorly tuned; especially, in uncertain environments. Modern industries, with increasing complexity, motivate us to employ new intelligent methods in order to extend PID controllers beyond their usual capabilities. In this paper, an advanced machine learning scheme is utilized to improve PID controllers; for the first time, a deep dynamic neural network is employed to tune online the parameters of the traditional PID controller in order to overcome the effects of uncertainties in the closed-loop control system. To reduce the computational burden of deep recurrent neural network, a novel structural learning technique is applied to optimize the configuration. Unlike existing pruning methods, the network is pruned based on the values of neurons and the total value of the corresponding layer. Simulation of a benchmark CSTR system demonstrate that the proposed scheme performs more efficiently compared to a shallow network tuner, in the presence of uncertainties.