Position and Heading Control of an Autonomous udUnderwater Vehicle using Model Predictive Control

摘要

Autonomous Underwater Vehicle (AUV) is currently being used for scientific research, udcommercial and military underwater applications. AUV requires autonomous guidance and udcontrol systems to perform underwater applications. This Thesis is concerned with position udand heading control of AUV using Model Predictive Control. udPosition control is a typical motion control problem, which is concerned with the design of udcontrol laws that force a vehicle to reach and maintain a fixed position. The position control udof body fixed x-axis to a fixed point using MPC toolbox of MATLAB is done here. System is udmodelled Using INFANTE AUV hydrodynamic parameters. There is physical limitation on udthruster value. ud udHeading control is concerned with the design of control laws that force a vehicle to reach and udmaintain a fixed direction. There are physical limitations on control input (Rudder deflection) udin heading control also a high yaw rate can produce sway and roll motion, which makes it udnecessary to put constraint on yaw rate. The MPC have a clear advantage in case of control udand input constraints. To avoid constraint violation and feasibility issues of MPC for AUV udheading control Disturbance Compensating (DC) MPC scheme is used. The DC-MPC udscheme is used for ship motion control and gave better results so we are using the proposed udscheme to AUV heading control. udA 2 DOF AUV model is taken with yaw rate and rudder deflection constraints. Line of sight ud(LOS) guidance scheme is utilised to generate the reference heading, which is to be followed. udTwo types of disturbances are taken constant and sinusoidal. Then simulation has been done udfor standard MPC, M-MPC and DC-MPC. A (DC) MPC algorithm is used to satisfy the state udconstraints in presence of disturbance to get a better performance. udStandard MPC gives good result without disturbance. But in case of disturbance yaw udconstraint is violated. At many time steps the standard MPC has no solution for given yaw udrate constraint at those time steps the constraints have been removed. The M-MPC satisfies udthe constraints. The DC-MPC gives better result in comparison to standard MPC and udModified MPC. The steady state oscillations are less in DC-MPC as compared to M-MPC for udsinusoidal disturbances. udThe minimization of extra cost function in DC-MPC makes the result better than M-MPC. By udsolving the extra cost function we try to make response close to that of without disturbance. udThe only added complexity in DC-MPC is ni-dimensional optimization problem. Which is udvery less compared to Np*ni, complexity of M-MPC. Where ni is the dimension of control udinput and Np is value of prediction horizon. The feasibility of DC-MPC scheme largely uddepends on the magnitude of disturbance. If disturbance is too large then this scheme is not udfeasible. ud