Robust optimal design of FOPID controller for five bar linkage robot in a Cyber-Physical System: A new simulation-optimization approach

• Published in: PloS one Vol. 15; no. 11; p. e0242613
• Main Authors: Parnianifard, Amir, Zemouche, Ali, Chancharoen, Ratchatin, Imran, Muhammad Ali, Wuttisittikulkij, Lunchakorn
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.11.2020; Public Library of Science (PLoS)
• Subjects: Algorithms; Automatic Control Engineering; Background noise; Collaboration; Computational efficiency; Computer and Information Sciences; Computer applications; Computer engineering; Computer Science; Computer simulation; Computing costs; Control systems; Control systems design; Control theory; Controllers; Cyber-physical systems; Design and construction; Design optimization; Electrical engineering; Engineering and Technology; Equipment and supplies; Feedback; Feedback control systems; Feedback loops; Gaussian process; Global optimization; Heuristic methods; Industrial robots; Mathematical programming; Methods; Models, Theoretical; Multiple objective analysis; Noise; Optimization; Optimization theory; Particle swarm optimization; Physical Sciences; Proportional integral derivative; Reliability aspects; Research and Analysis Methods; Robot arms; Robotics; Robots; Robust control; Sensors; Simulation; Swarm intelligence; Systems design; Variability; Wireless communications; Thailand; Bangkok Thailand
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0242613

This paper aims to further increase the reliability of optimal results by setting the simulation conditions to be as close as possible to the real or actual operation to create a Cyber-Physical System (CPS) view for the installation of the Fractional-Order PID (FOPID) controller. For this purpose, we consider two different sources of variability in such a CPS control model. The first source refers to the changeability of a target of the control model (multiple setpoints) because of environmental noise factors and the second source refers to an anomaly in sensors that is raised in a feedback loop. We develop a new approach to optimize two objective functions under uncertainty including signal energy control and response error control while obtaining the robustness among the source of variability with the lowest computational cost. A new hybrid surrogate-metaheuristic approach is developed using Particle Swarm Optimization (PSO) to update the Gaussian Process (GP) surrogate for a sequential improvement of the robust optimal result. The application of efficient global optimization is extended to estimate surrogate prediction error with less computational cost using a jackknife leave-one-out estimator. This paper examines the challenges of such a robust multi-objective optimization for FOPID control of a five-bar linkage robot manipulator. The results show the applicability and effectiveness of our proposed method in obtaining robustness and reliability in a CPS control system by tackling required computational efforts.