## Design and Experimental Realization of Adaptive Control Schemes for an Autonomous Underwater Vehicle

Material type: TextLanguage: English Publisher: 2017Description: 106 pSubject(s): Engineering and Technology | Electrical Engineering | Power ElectronicsOnline resources: Click here to access online Dissertation note: Thesis Ph.D National Institute of Technology, Rourkela Summary: Research on Autonomous Underwater Vehicle(AUV) has attracted increased attention of control engineering community in the recent years due to its many interesting applications such as in Defense organisations for underwater mine detection, region surveillance, oceanography studies, oil/gas industries for inspection of underwater pipelines and other marine related industries. However, for the realization of these applications, effective motion control algorithms need to be developed. These motion control algorithms require mathematical representation of AUV which comprises of hydrodynamic damping, Coriolis terms, mass and inertia terms etc. To obtain dynamics of an AUV, different analytical and empirical methods are reported in the literature such as tow tank test, Computational Fluid Dynamics (CFD) analysis and on-line system identification. Among these methods, tow-tank test and CFD analysis provide white-box identified model of the AUV dynamics. Thus, the control design using these methods are found to be ineffective in situation of change in payloads of an AUV or parametric variations in AUV dynamics. On the other hand, control design using on-line identification, the dynamics of AUV can be obtained at every sampling time and thus the aforesaid parametric variations in AUV dynamics can be handled effectively. In this thesis, adaptive control strategies are developed using the parameters of AUV obtained through on-line system identification. The proposed algorithms are verified first through simulation and then through experimentation on the prototype AUV. Among various motion control algorithms, waypoint tracking has more practical significance for oceanographic surveys and many other applications. In order to implement, waypoint motion control schemes, Line-of-Sight (LoS) guidance law can be used which is computationally less expensive. In this thesis, adaptive control schemes are developed to implement LoS guidance for an AUV for practical realization of the control algorithm. Further, in order to realize the proposed control algorithms, a prototype AUV is developed in the laboratory. The developed AUV is a torpedo-shaped in order to experience low drag force, underactuated AUV with a single thruster for forward motion and control planes for angular motion. Firstly, the AUV structure such as nose profile, tail profile, hull section and control planes are designed and developed. Secondly, the hardware configuration of the AUV such as sensors, actuators, computational unit, communication module etc. are appropriately selected. Finally, a software framework called Robot Operating System (ROS) is used for seamless integration of various sensors, actuators with the computational unit. ROS is a software platform which provides right platform for the implementation of the control algorithms using the sensor data to achieve autonomous capability of the AUV. In order to develop adaptive control strategies, the unknown dynamics of the AUV is identified using polynomial-based Nonlinear Autoregressive Moving Average eXogenous (NARMAX) model structure. The parameters of this NARMAX model structure are identified online using Recursive Extended Least Square (RELS) method. Then an adaptive controller is developed for realization of the LoS guidance law for an AUV. Using the kinematic equation and the desired path parameters, a Lyapunov based backstepping controller is designed to obtain the reference velocities for the dynamics. Subsequently, a self-tuning PID controller is designed for the AUV to track these reference velocities. Using an inverse optimal control technique, the gains of the selftuning PID controller are tuned on-line. Although, this algorithm is computationally less expensive but there lie issues such as actuator constraints and state constraints which need to be resolved in view of practical realization of the control law. It is also observed that the proposed NARMAX structure of the AUV consists of redundant regressor terms. To alleviate the aforesaid limitations of the Inverse optimal self-tuning control scheme, a constrained adaptive control scheme is proposed that employs a minimum representation of the NARMAX structure (MR-NARMAX) for capturing AUV dynamics. The regressors of the MR-NARMAX structure are identified using Forward Regressor Orthogonal Least Square algorithm. Further, the parameters of this MRNARMAX model structure of the AUV are identified at every sampling time using RELS algorithm. Using the desired path parameters and the identified dynamics, an error objective function is defined which is to be minimized. The minimization problem where the objective function with the state and actuator constraints is formulated as a convex optimization problem. This optimization problem is solved using quadratic programming technique. The proposed MR-NARMAX based adaptive control is verified in the simulation and then on the prototype AUV. From the obtained results it is observed that this algorithm provides successful tracking of the desired heading. But, the proposed control algorithm is computational expensive, as an optimization problem is to be solved at each sampling instant. In order to reduce the computational time, an explicit model predictive control strategy is developed using the concept of multi-parametric programming. A Lyapunov based backstepping controller is designed to generate desired yaw velocity in order to steer the AUV towards the desired path. This explicit model predictive controller is designed using the identified NARMAX model for tracking the desired yaw velocity. The proposed explicit MPC algorithm is implemented first in simulation and then in the prototype AUV. From the simulation and experimental results, it is found that this controller has less computation time and also it considers both the state and actuator constraints whilst exhibiting good tracking performance.Item type | Current location | Collection | Call number | Status | Date due | Barcode |
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Thesis (Ph.D/M.Tech R) | BP Central Library Thesis Section | Reference | Not for loan | T669 |

Thesis Ph.D National Institute of Technology, Rourkela

Research on Autonomous Underwater Vehicle(AUV) has attracted increased attention of control engineering community in the recent years due to its many interesting applications such as in Defense organisations for underwater mine detection, region surveillance, oceanography studies, oil/gas industries for inspection of underwater pipelines and other marine related industries. However, for the realization of these applications, effective motion control algorithms need to be developed. These motion control algorithms require mathematical representation of AUV which comprises of hydrodynamic damping, Coriolis terms, mass and inertia terms etc. To obtain dynamics of an AUV, different analytical and empirical methods are reported in the literature such as tow tank test, Computational Fluid Dynamics (CFD) analysis and on-line system identification. Among these methods, tow-tank test and CFD analysis provide white-box identified model of the AUV dynamics. Thus, the control design using these methods are found to be ineffective in situation of change in payloads of an AUV or parametric variations in AUV dynamics. On the other hand, control design using on-line identification, the dynamics of AUV can be obtained at every sampling time and thus the aforesaid parametric variations in AUV dynamics can be handled effectively. In this thesis, adaptive control strategies are developed using the parameters of AUV obtained through on-line system identification. The proposed algorithms are verified first through simulation and then through experimentation on the prototype AUV. Among various motion control algorithms, waypoint tracking has more practical significance for oceanographic surveys and many other applications. In order to implement, waypoint motion control schemes, Line-of-Sight (LoS) guidance law can be used which is computationally less expensive. In this thesis, adaptive control schemes are developed to implement LoS guidance for an AUV for practical realization of the control algorithm.

Further, in order to realize the proposed control algorithms, a prototype AUV is developed in the laboratory. The developed AUV is a torpedo-shaped in order to experience low drag force, underactuated AUV with a single thruster for forward motion and control planes for angular motion. Firstly, the AUV structure such as nose profile, tail profile, hull section and control planes are designed and developed. Secondly, the hardware configuration of the AUV such as sensors, actuators, computational unit, communication module etc. are appropriately selected. Finally, a software framework called Robot Operating System (ROS) is used for seamless integration of various sensors, actuators with the computational unit. ROS is a software platform which provides right platform for the implementation of the control algorithms using the sensor data to achieve autonomous capability of the AUV. In order to develop adaptive control strategies, the unknown dynamics of the AUV is identified using polynomial-based Nonlinear Autoregressive Moving Average eXogenous (NARMAX) model structure. The parameters of this NARMAX model structure are identified online using Recursive Extended Least Square (RELS) method. Then an adaptive controller is developed for realization of the LoS guidance law for an AUV. Using the kinematic equation and the desired path parameters, a Lyapunov based backstepping controller is designed to obtain the reference velocities for the dynamics. Subsequently, a self-tuning PID controller is designed for the AUV to track these reference velocities. Using an inverse optimal control technique, the gains of the selftuning PID controller are tuned on-line. Although, this algorithm is computationally less expensive but there lie issues such as actuator constraints and state constraints which need to be resolved in view of practical realization of the control law. It is also observed that the proposed NARMAX structure of the AUV consists of redundant regressor terms. To alleviate the aforesaid limitations of the Inverse optimal self-tuning control scheme, a constrained adaptive control scheme is proposed that employs a minimum representation of the NARMAX structure (MR-NARMAX) for capturing AUV dynamics. The regressors of the MR-NARMAX structure are identified using Forward Regressor Orthogonal Least Square algorithm. Further, the parameters of this MRNARMAX model structure of the AUV are identified at every sampling time using RELS algorithm. Using the desired path parameters and the identified dynamics, an error objective function is defined which is to be minimized. The minimization problem where the objective function with the state and actuator constraints is formulated as a convex optimization problem. This optimization problem is solved using quadratic programming technique. The proposed MR-NARMAX based adaptive control is verified in the simulation and then on the prototype AUV. From the obtained results it is observed that this algorithm provides successful tracking of the desired heading. But, the proposed control algorithm is computational expensive, as an optimization problem is to be solved at each sampling instant. In order to reduce the computational time, an explicit model predictive control strategy is developed using the concept of multi-parametric programming. A Lyapunov based backstepping controller is designed to generate desired yaw velocity in order to steer the AUV towards the desired path. This explicit model predictive controller is designed using the identified NARMAX model for tracking the desired yaw velocity. The proposed explicit MPC algorithm is implemented first in simulation and then in the prototype AUV. From the simulation and experimental results, it is found that this controller has less computation time and also it considers both the state and actuator constraints whilst exhibiting good tracking performance.

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