LSTM Replaces MIMO MPC

The video tutorial explores the use of multivariate model predictive control (MPC) with multiple manipulated and control variables. It demonstrates replacing traditional control methods with LSTM networks, testing on an Arduino Leonardo microcontroller, and using a digital twin simulator. The tutorial covers code implementation, training LSTM models with Keras, and deploying the model on hardware. It highlights the effectiveness of LSTM in learning MPC responses and its potential to replace MPC in simpler applications.

What is the primary goal of multivariate MPC?

• To replace hardware components

• To simplify the control process

• To coordinate multiple variables

• To control a single variable

Which device is used as the test system in the setup?

• Raspberry Pi

• Arduino Leonardo

• BeagleBone Black

• ESP32

What is the purpose of using a digital twin simulator in this context?

• To improve the accuracy of measurements

• To test the system without physical hardware

• To enhance the speed of computations

• To replace the physical hardware

Which platform is used to run the code for MPC and LSTM networks?

• Amazon AWS

• IBM Cloud

• Google Colab

• Microsoft Azure

What is the first step in running the code for the MPC and LSTM networks?

• Testing the system

• Writing the code

• Installing necessary libraries

• Connecting the hardware

Which library is used for training the LSTM network?

• Scikit-learn

• TensorFlow

• PyTorch

• Keras

What is the output layer configuration of the LSTM network?

• 50 units, dense output of 3

• 200 units, dense output of 2

• 100 units, dense output of 1

• 150 units, dense output of 4

What is the purpose of pickling the LSTM model?

• To improve training speed

• To save the model for deployment

• To reduce model size

• To enhance model accuracy

What is a key advantage of using LSTM over MPC for simpler applications?

• LSTM requires less data

• LSTM is easier to implement

• LSTM can learn responses effectively

• LSTM is more cost-effective

What is the final step in deploying the LSTM model?

• Re-training the model

• Adjusting the model parameters

• Running the model on a microcontroller

• Testing the model on new data