Fixed structure controllers (such as proportional-integral-derivative
controllers) are used extensively in industry. Finding a practical and
versatile method to tune these controllers, particularly with imprecise
process models and limited online computational resources, is an
industrially relevant problem which could improve the efficiency of many
plants. In this paper, we present two flexible neural network-based
approaches capable of tuning any fixed structure controller for any
control objective and process model and compare their advantages and
disadvantages. The first approach is derived from supervised learning
and classical optimization techniques, while the second approach applies
techniques used in deep reinforcement learning. Both approaches
incorporate model uncertainties when selecting controller parameters,
reducing the need for costly experiments to precisely estimate model
parameters in a plant. Both methods are also computationally efficient
online, enabling their widespread usage.
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