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Model Predictive Control of an Autonomous Semi-Submersible Vehicle for Depth Control

Amin Hatamy, Erfan | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55355 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Nejat Pishkenari, Hossein; Salarieh, Hassan
  7. Abstract:
  8. In the past few decades, with the advancement of technology, using autonomous robots has been receiving growing interest. Autonomous semi-submersible vehicles are a subset of autonomous underwater vehicles (AUV). This type of vehicle operates near the surface of the water and is semi-submerged. Nonlinear coupled dynamics, structural uncertainties, model parameters dependency to robot velocity, external disturbances, and model constraints are AUV’s workspace challenges. For this reason, depth control of these vehicles in the presence of environmental disturbances is crucial. However, MPC as one of the advanced control methods in the field of robotics is increasingly developing. This control method has several advantages over the classical methods, including the ability to apply system constraints explicitly to the problem. This thesis addresses the design of a depth controller for an autonomous semi-submersible vehicle using Model Predictive Control (MPC). To bring the simulation results closer to the realistic scenarios, we evaluate the control performance by applying output noise, perturbations, and dynamic model uncertainties. Furthermore, research results are compared to sliding mode controller as a robust control method. The research results in the appropriate performance of the model predictive controller. Using the model predictive controller reduces the reference path tracking error by 50 to 100%. Whereas, the computation time has values of 5% to 30% of the simulation time step. However, the sliding mode controller has the least amount of computation time due to the use of algebraic relations in extracting the optimal control input instead of numerical optimization
  9. Keywords:
  10. Model Predictive Control ; Optimization ; Sequential Quadratic Programming (SQP) ; Active Set Method ; Autonomous Underwater Vehicle ; Autonomous Semi-Submersible Vehicle ; Ocean Wave Model

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