Robust Genetic Algorithm and Fuzzy Inference Mechanism Embedded in a Sliding-Mode Controller for an Uncertain Underwater Robot

• Published in: IEEE/ASME transactions on mechatronics Vol. 23; no. 2; pp. 655 - 666
• Main Authors: Chin, Cheng Siong, Lin, Wei Peng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational fluid dynamics; Computational modeling; Computer simulation; Fuzzy control; Fuzzy logic; Fuzzy systems; genetic algorithm (GA); Genetic algorithms; hydrodynamic parameters; Hydrodynamics; Inference; Mathematical models; Numerical models; Numerical stability; Parameter sensitivity; Parameter uncertainty; Proportional integral derivative; remotely operated vehicle (ROV); Remotely operated vehicles; Sliding mode control; sliding-mode control (SMC); Uncertainty; Underwater robots
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2018.2806389

The integration of inaccurate remotely operated vehicle (ROV) model data obtained by computational fluid dynamics for control is presented. Since the ROV is highly nonlinear and uncertain, a sliding-mode control (SMC) system using a direction-based genetic algorithm (GA) and fuzzy inference mechanism is proposed. The GA influences the right evolutionary step and direction of the SMC parameters subjected to uncertainties in the evolutionary process. The effectiveness of reducing the sensitivity of the proposed control scheme to model parameters and external disturbance is verified by simulations and sea trial. The results demonstrate that the proposed controller performed better with less chattering in position responses than SMC without GA-fuzzy optimization, fuzzy logic controller, and proportional-integral derivative.