DOI: 10.21950/j95ifs
title: SoftArmControl: Data and results of the identification and control of a soft robotic arm

description: Different csv files are available that include information about the pose (yaw and pitch angles) of the soft arm while varying the position of its motors. Three datasets are specifically available: experimental data used for the identification of the soft arm model; experimental data from the use of different controllers for the soft arm to reach a pose; and experimental data from the use of different controllers for the soft arm to follow a trajectory.&lt;/br&gt;
Apart from the datasets, different codes have been also generated in Matlab and C++ to manage the datasets and obtain the results shown in the associated paper, mainly the following results: soft arm model identification results; control results when using a PI controller for the robot to reach a pose and follow a trajectory; control results when using a fractional-order controller for the robot to reach a pose and follow a trajectory.
description: Description of the project: Perform the soft robotic arm model identification and design PI and fractional order controllers for a robust performance of the arm. With these controllers, the soft arm performance is analyzed experimentally.
Notes:The description of the soft robotic arm where the experiments are performed, the method for identifying the soft arm model through experimental data, the design and implementation of the controllers proposed and the description of the trajectories to test the arm performance can be found in:
Relaño, C., Muñoz, J., Monje, C. A., Martínez, S., & González, D. (2022). Modeling and Control of a Soft Robotic Arm Based on a Fractional Order Control Approach. Fractal and Fractional, 7(1), 8. https://doi.org/10.3390/fractalfract7010008
Methodology:
- To obtain the soft arm model identification dataset, different input actions are performed on the arm in open loop and the results of the soft arm outputs are saved as described in "main_vel_identification.cpp". The resulting identification datasets are included in "Identification_data.zip". 
- To obtain the soft arm model and design the PI and fractional-order controllers using the dataset "Identificaction_data.zip", the following Matlab files are used: "ident_plant.m" for model identification; "ident_controller_PID.m" for PI controller design; "isomFOCy.m" and "isomFOCp.m" for fractional-oder controller design for yaw and pitch angles, respectively. 
- To analyze the behavior of the soft arm with the controllers designed, real experiments are performed with the soft arm using the following C++ code: "main_vel_controller.cpp" to collect data from PI control; "main_vel_focontroller.cpp" to collect data from fractional-oder control; "vel_trajectory.cpp" to collect data of the robot when tracking/following a trajectory. The resulting data are stored in datasets "Control_data.zip" and "Trayectory_data.zip".
- To visualize the dataset results in Matlab, the following files are used: "Plot_vel_control.m" for plotting control charts; "show_control_velocidad_data.m" for plotting control results; "read_control_velocidad_data.m" for reading control results; "show_Trayectory.m" for plotting trajectory data.
description: Matlab
description: Any C++ IDE (such as QtCreator)