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Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Mordatch, I., et al. (2021) Decision Transformer: Reinforcement Learning via Sequence Modeling. Advances in Neural Information Processing Systems, 34, 15084-15097.

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• TITLE: Transformer for the Design of Automatic Control Systems with Prefilters

  AUTHORS: Isaac Hughes, John F. O’Brien

  KEYWORDS: Feedback, Machine Learning, Compensator Design

  JOURNAL NAME: World Journal of Engineering and Technology, Vol.12 No.3, July 29, 2024

  ABSTRACT: A machine learning model, using the transformer architecture, is used to design a feedback compensator and prefilter for various simulated plants. The output of the transformer is a sequence of compensator and prefilter parameters. The compensator and prefilter are linear models, preserving the ability to analyze the system with linear control theory. The input to the network is a window of recent reference and output samples. The goal of the transformer is to minimize tracking error at each time step. The plants under consideration range from simple to challenging. The more difficult plants contain closely spaced, lightly damped, complex conjugate pairs of poles and zeros. Results are compared to PID controllers tuned for a similar crossover frequency and optimal phase margin. For simple plants, the transformer converges to solutions which overly rely on the prefilter, neglecting the maximization of negative feedback. For more complex plants, the transformer designs a compensator and prefilter with more desirable qualities. In all cases, the transformer can start with random model parameters and modify them to minimize tracking error on the step reference.