Abstract
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.