Details
| Original language | English |
|---|---|
| Title of host publication | ICINCO 2018 - Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics |
| Editors | Kurosh Madani, Oleg Gusikhin |
| Pages | 240-247 |
| Number of pages | 8 |
| ISBN (electronic) | 9789897583216 |
| Publication status | Published - 2018 |
| Event | 15th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2018 - Porto, Portugal Duration: 29 Jul 2018 → 31 Jul 2018 |
Abstract
An auto-tuning method of PID controllers for robot manipulators using multi-objective optimization technique is proposed. Two approaches are introduced based on the multi-objective particle swarm optimization (MOPSO) and multi-objective cuckoo search (MOCS), respectively. The main goal of this work is to introduce a comparative study on the performance of both algorithms with respects to their applicability to the auto-tuning process. For this sake, necessary metrics are considered such as the hyperarea difference and the overall Pareto spread, among others. In order to generate a sufficient amount of statistical data, a simulation of the robot Puma 560 is implemented. Using a relatively accurate model of the robot dynamics, a PID controller is applied and an optimization problem is configured. Two objective functions are defined, namely the integral of absolute error and the variance of control action. In addition, two constraints are considered regarding the maximal position error and maximal motor torque. After defining the optimization problem, the two algorithms are implemented as auto-tuning methods of the controller gains. Execution of the tuning process is repeated 30 times to test the statistical power of the obtained results. After that, an experiment on a real robot is performed to gain an overview on the practical application of the proposed method. Finally, the performance of both algorithms are compared and conclusions about the efficiency of each one are made.
Keywords
- Automatic Tuning, Cuckoo Search, Multi-Objective Optimization, Particle Swarm Optimization, PID Control, Robot Manipulators
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Engineering(all)
- Control and Systems Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
ICINCO 2018 - Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics. ed. / Kurosh Madani; Oleg Gusikhin. 2018. p. 240-247.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A comparative study on the performance of MOPSO and MOCS as auto-tuning methods of PID controllers for robot manipulators
AU - Zidan, Ahmed
AU - Tappe, Svenja
AU - Ortmaier, Tobias
PY - 2018
Y1 - 2018
N2 - An auto-tuning method of PID controllers for robot manipulators using multi-objective optimization technique is proposed. Two approaches are introduced based on the multi-objective particle swarm optimization (MOPSO) and multi-objective cuckoo search (MOCS), respectively. The main goal of this work is to introduce a comparative study on the performance of both algorithms with respects to their applicability to the auto-tuning process. For this sake, necessary metrics are considered such as the hyperarea difference and the overall Pareto spread, among others. In order to generate a sufficient amount of statistical data, a simulation of the robot Puma 560 is implemented. Using a relatively accurate model of the robot dynamics, a PID controller is applied and an optimization problem is configured. Two objective functions are defined, namely the integral of absolute error and the variance of control action. In addition, two constraints are considered regarding the maximal position error and maximal motor torque. After defining the optimization problem, the two algorithms are implemented as auto-tuning methods of the controller gains. Execution of the tuning process is repeated 30 times to test the statistical power of the obtained results. After that, an experiment on a real robot is performed to gain an overview on the practical application of the proposed method. Finally, the performance of both algorithms are compared and conclusions about the efficiency of each one are made.
AB - An auto-tuning method of PID controllers for robot manipulators using multi-objective optimization technique is proposed. Two approaches are introduced based on the multi-objective particle swarm optimization (MOPSO) and multi-objective cuckoo search (MOCS), respectively. The main goal of this work is to introduce a comparative study on the performance of both algorithms with respects to their applicability to the auto-tuning process. For this sake, necessary metrics are considered such as the hyperarea difference and the overall Pareto spread, among others. In order to generate a sufficient amount of statistical data, a simulation of the robot Puma 560 is implemented. Using a relatively accurate model of the robot dynamics, a PID controller is applied and an optimization problem is configured. Two objective functions are defined, namely the integral of absolute error and the variance of control action. In addition, two constraints are considered regarding the maximal position error and maximal motor torque. After defining the optimization problem, the two algorithms are implemented as auto-tuning methods of the controller gains. Execution of the tuning process is repeated 30 times to test the statistical power of the obtained results. After that, an experiment on a real robot is performed to gain an overview on the practical application of the proposed method. Finally, the performance of both algorithms are compared and conclusions about the efficiency of each one are made.
KW - Automatic Tuning
KW - Cuckoo Search
KW - Multi-Objective Optimization
KW - Particle Swarm Optimization
KW - PID Control
KW - Robot Manipulators
UR - http://www.scopus.com/inward/record.url?scp=85071604041&partnerID=8YFLogxK
U2 - 10.5220/0006899802400247
DO - 10.5220/0006899802400247
M3 - Conference contribution
AN - SCOPUS:85071604041
SP - 240
EP - 247
BT - ICINCO 2018 - Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics
A2 - Madani, Kurosh
A2 - Gusikhin, Oleg
T2 - 15th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2018
Y2 - 29 July 2018 through 31 July 2018
ER -