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@@ -11,16 +11,65 @@ course at UCM, with topics such as:
 - Intelligent agents
 
 
-## Práctica 1: Modelling, Identification and Control 
+## PRACTICE 1: Modelling, Identification and Control 
 
 By means of some calculation computer program, 
 identify a system whose input and output data are known using
 Matlab's cftool or basic fitting. 
 
-## Práctica 2: PID Controller
+Proyect: [here](https://github.com/DanielaCordova/Artificial-Intelligence-Applied-to-Control-Systems-Projects/tree/main/Practica1#readme)
 
-Project aimed at the construction of 
-different PID controllers based on different systems to control.
+## PRACTICE 2: PID Controller
+
+The goal of this project was to construct PID controllers and learn how they work:
+
+Using Matlab/Simulink, identify an open-loop system and use the model 
+parameters to tune a closed-loop PID controller. Use the “system” simulink block, supplied inside the “plant” simulink model (plant.zip).
+
+Proyect: [here](https://github.com/DanielaCordova/Artificial-Intelligence-Applied-to-Control-Systems-Projects/tree/main/practica2#readme)
+
+## PRACTICE 3: Expert Control Systems
+
+Project aimed at the construction of a PID controller through an expert system:
+
+Automatically improve the qualitative tuning of the parameters of a
+PID controller through an expert system whose inference engine is formed
+by a set of rules obtained from experience.
+
+Proyect: [here](https://github.com/DanielaCordova/Artificial-Intelligence-Applied-to-Control-Systems-Projects/tree/main/practica3#readme)
+
+## PRACTICE 4: Fuzzy Control
+
+Project aimed at the construction of a Fuzzy control system.
+
+
+## PRACTICE 5: Neuro Control
+This project is aimed at creating a neural network that controls a robot arm
+
+**PART 1:** 
+
+a) Design a neural network using the Matlab/Simulink *nntool* tool that performs the XOR function. 
+Observe the influence of the network parameters on the error convergence. Try various learning strategies. 
+
+**PART 2:** 
+
+In this practice we are going to make use of a set of neural networks to move a robot arm. 
+We will use the arm model shown in Figure 1 as a platform to obtain arm movement data, and to train and validate the constructed networks. 
+
+This arm has five joints:
+1. Base: The arm can rotate around its base.
+1. Shoulder: Allows vertical rotation of the entire arm.
+1. Elbow: allows all the parts of the arm to rotate vertically from that joint onwards.
+1. Wrist (vertical joint): allows the gripper to rotate vertically.
+1. Wrist (horizontal joint): allows the clamp to rotate left and right.
+
+All the joints have been simulated without stops, which means that they can freely rotate 360º. 
+In addition, the model does not take into account the collisions of its moving parts with each other. 
+Therefore, the model can perform movements that would be impossible for a real arm.
+
+## PRACTICE 6: PID Parameters Optimization by Using Genetic Algorithm 
+
+The goal of the project was to develop a PID controller through genetic algorithms.
 
 ## License