Intelligent Active Force Control of an Underwater Remotely Operated Vehicle Using Evolutionary Computation Technique
Keywords:
Underwater remotely operated vehicle (UROV), proportional-integral-derivative (PID), active force control (AFC), evolutionary computation (EC), robustnessAbstract
The purpose of this study is to develop a robust control system on an Underwater Remotely Operated Vehicles (ROV). An intelligent Active Force Control (AFC) with Evolutionary Computation (EC) algorithms was implemented to a selected dynamic model of the UROV. System performance employing different hybrid control schemes that applied AFC and EC was observed and studied to evaluate review their performance and capability. The system performance of the six degree of freedom (6-DOF) UROV related to the surge, sway, heave, roll, pitch and yaw motions, respectively were observed to obtain the responses related to the steady-state error, overshoot and settling time as the criteria for evaluation. Conventional PID controller was first implemented on the system and tuned using a heuristic method before applying any disturbances and other control techniques. A comparative study between PID, PID-AFC and PID-AFC-EC was then conducted to determine the best control technique amongst them. The results showed that PID-AFC-EC is robust, has low steady state error and fast settling time even when disturbances were presence in and applied to the system.
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References
Azis F.A., Aras M.S.M., Rashid M.Z.A., Othman M.N. and Abdullah S.S., 2012. Problem Identification for Underwater Remotely Operated Vehicle (ROV): A Case Study, Procedia Engineering, 41: 554–560.
Yamamoto I., Morinaga A. and Ura K., 2019. Development of Remotely Operated Underwater Vehicle and Applications to the Sea, Procs of the International Offshore and Polar Engineering Conference, 1, 1637–1641.
Ishaque K., Abdullah S.S., Ayob S.M. and Salam Z., 2010. Single Input Fuzzy Logic Controller for Unmanned Underwater Vehicle, Journal of Intelligent and Robotic Systems: Theory and Applications, 59(1): 87–100.
Xu J. and Wang N., 2018. Optimization of ROV Control Based on Genetic Algorithm. Procs of 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO), Kobe.
Cui R., Chen L., Yang C. and Chen M., 2017. Extended State Observer-Based Integral Sliding Mode Control for an Underwater Robot With Unknown Disturbances and Uncertain Nonlinearities, IEEE Transactions on Industrial Electronics, 64(8): 6785–6795.
Soylu S., Proctor A.A., Podhorodeski R.P., Bradley C. and Buckham B.J., 2016. Precise Trajectory Control for an Inspection Class ROV, Ocean Engineering, 111: 508–523.
Bessa W.M., Dutra M.S. and Kreuzer E., 2010. An Adaptive Fuzzy Sliding Mode Controller for Remotely Operated Underwater Vehicles, Robotics and Autonomous Systems, 58(1): 16–26.
Javadi-Moghaddam J. and Bagheri A., 2010. An Adaptive Neuro-fuzzy Sliding Mode Based Genetic Algorithm Control System for Under Water Remotely Operated Vehicle, Expert Systems with Applications, 37(1): 647–660.
Liu S., Liu Y. and Wang N., 2017. Robust Adaptive Self-organizing Neuro-fuzzy Tracking Control of UUV with System Uncertainties and Unknown Dead-zone Nonlinearity, Nonlinear Dynamics, 89(2): 1397–1414.
Mailah M., Hewit J.R. and Meeran S., 1996. Active Force Control Applied to a Rigid Robot Arm, Jurnal Mekanikal, 2(2): 52–68.
Priyandoko G., Mailah M. and Jamaluddin H., 2009. Vehicle Active Suspension System using Skyhook Adaptive Neuro Active Force Control, Mechanical Systems and Signal Processing, 23(3): 855-868.
Mailah M. and Rahim N.I.A., 2000. Intelligent Active Force Control of a Robot Arm using Fuzzy Logic, Procs of TENCON 2000, 2: 291–296.
Bäck T. and Schwefel H-P., 1993. An Overview of Evolutionary Algorithms for Parameter Optimization, Evolutionary Computation, 1(1): 1–23.
Kiranyaz S., Ince T. and Gabbouj M., 2014. Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition, Adaptation, Learning, and Optimization book series (ALO, volume 15), Springer.
Jaen-Cuellar A.Y., Romero-Troncoso R.D.J., Morales-Velazquez L. and Osornio-Rios R.A., 2013. PID-controller Tuning Optimization with Genetic Algorithms in Servo Systems, International Journal of Advanced Robotic Systems, 10(9), https://doi.org/10.5772/56697.
Mirzal A., Yoshii S., Furukawa M., 2006. PID Parameters Optimization by Using Genetic Algorithm: A Study on Time-delay Systems, ISTECS Journal, 8: 34-43.
Chin C.S., Lau M.W.S., Low E. and Seet G.G.L., 2006. Software for Modelling and Simulation of a Remotely-Operated Vehicle (ROV), International Journal of Simulation Modelling, 5(3): 114–125.
Siciliano B. and Khatib O. (Eds.), 2016. Springer Handbook of Robotics, Springer.
Chin C.S., 2011. Systematic Modeling and Model-based Simulation of a Remotely Operated Vehicle using MATLAB and Simulink, International Journal of Modeling, Simulation, and Scientific Computing, 2(4): 481–511.
Chin C.S., 2017. ROV Design and Analysis (RDA) - Simulink. Retrieved from: https://www.mathworks.com/matlabcentral/fileexchange/19362-rov-design-and-analysis-rda-simulink. [Accessed: 18 April 2020].
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