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\nLSTM Replaces SISO MPC\n

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\nThe purpose of this exercise is to automate a temperature control process with an LSTM network. The LSTM network is trained from a Model Predictive Controller (MPC). LSTM (Long Short Term Memory) networks are a special type of RNN...\nThe purpose of this exercise is to automate a temperature control process with an LSTM network. The LSTM network is trained from a Model Predictive Controller (MPC). LSTM (Long Short Term Memory) networks are a special type of RNN (Recurrent Neural Network) that is structured to remember and predict based on long-term dependencies that are trained with time-series data. An LSTM repeating module has four interacting components.

The LSTM is trained (parameters adjusted) with an input window of prior data and minimized difference between the predicted and next measured value. Sequential methods predict just one next value based on the window of prior data. In this case, the error between the set point and measured value is the feature and the heater value is the output label.

An extension of this work is to train from a multivariate controller with Multiple Inputs (Q1, Q2), Multiple Outputs (T1, T2). The LSTM Network inputs and outputs can be adapted to accommodate the increased dimensions. See LSTM Networks in the Dynamic Optimization course for training an LSTM from a MIMO (2x2) MPC.

The challenge for both the PID and SISO MPC case studies is to deploy the LSTM controller, independent of any training program. The training can occur offline while the online implementation is deployed to a SCADA (Scheduling and Data Acquisition) system such as a Distributed Control System (DCS) or Programmable Logic Controller (PLC). Below is sample code for deploying the saved data in lstm_control.pkl and LSTM network model in lstm_control.h5. Update the cycle time (sleep time) to 1 sec for PID control and 2 sec for MPC. To test this code, either use the device with TCLab() or switch to the digital twin with TCLabModel().\n
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Media Length
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16:42
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Grade
10th - Higher Ed
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Subjects
STEM
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Source:
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\nAPMonitor.com\n\n
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Date
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2022
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English
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For Teacher Use
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Concepts

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time, temperature
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Additional Tags

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apmonitor, lstm, mpc, model predictive control, identification, sysid, recurrent neural network, transformers, set point, digital twin, tc lab, click run, jupiter notebook, h5 file, training region, real time, cycle time, case study, initial temperature, pickle file, physical hardware, lstm network, model file, measured values, lstm function, case, controller, hardware, model, function, code, jump, deploy, train, similar
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