## 3 Traditional Controllers: PID and MPC Closed-Loop Control Strategies
### Deep Learning Neural Network
#### 4.2.1 DLNN Architectures, Components, Algorithms and Applications  
The deep learning (DL) architecture originated from artificial neural networks (ANNs) that were inspired by the structure of the human brain. Neural network (NN) architectures consist of several components working together to process and learn from massive datasets, extracting patterns and abstract representations of data as information flows through each layer. The components include the input layer, hidden layers, neurons (nodes), weights and biases, activation functions, the output layer, and the loss function; these are well explained in a comprehensive review presented in [24]. In that review, deep learning is described as a multi-layer machine learning method for performing complex tasks. This layered structure is why the deep learning technique differs from conventional machine learning methods; within this structure, the data flows from an input layer through several hidden layers to an output layer. These added hidden layers increase the accuracy performance of the neural network design. Each neuron performs calculations on the received input and passes the result to the next layer, allowing the network to learn complex patterns from the data [24]. Also, a perceptron simulates a neuron with a set of inputs, each of these having a particular weight, such that the inputs with higher weights have a significant impact on the neural network. The neuron computes and produces an output based on the weighted inputs. Each neuron receives n inputs (features), sums them, applies a transformation (activation) and generates the output. A second parameter called bias is used to adjust the output based on the input weights; thus the model fits the data in the best possible way. An activation function converts the inputs into the output produced using a threshold. Briefly, the neural network architecture serves as the support for the understanding and processing of diverse data types, and generative models unlock the ability to create new data samples that resemble the training data. In the literature [24], it is mentioned that deep learning is \"among the fastest-growing areas in computational science, employing complex multilayered networks to model high level patterns in data\". The state-of-the-art of deep learning techniques has been enhanced in several fields, including \"visual object recognition, speech recognition, genomics, and discovery of drugs, along with plenty of others\". Among the well-knowndeep learning algorithms discussed [24] are backpropagation models, autoencoders, variational autoencoders, restricted Boltzmann machines, deep belief networks, convolutional natural networks, forward neural networks, recurrent neural networks, LSTM, generative adversarial networks, transformer deep learning structure, embedding from language models, and bidirectional encoder representations from transformers. A brief overview of the steps involved in using deep learning is expressed in the following sequence:  
\[1.Data\ Preparation\to 2.Building\ NN\to 3.Training\ NN\to 4\frac{Evaluation}{Tuning}\to 5\frac{Prediction}{Generation}\to 6.Iterative\ Improvement\]  
These steps can be seen in the MATLAB code lines of both the RL DLNN algorithms developed in the present research, Appendix B.1, Algorithm A1, and Algorithm A2 respectively. In the first step, the data are collected and preprocessed (cleaning by removing errors or inconsidences, normalization, and splitting, such as for example, 70% into training data and 30% testing data). The data format must be compatible with the NN. The architecture of the NN is defined in the second step. In the third step, the training process is launched for fine-tuning of the network's weights and biases in order to minimize the difference between the predicted output and the true output. This is followed by step 4, evaluation and validation, required for a rigorous evaluation of performance accuracy, followed by a test validation to gather valuable information about how well the NN generalizes to unseen data.