The predictive functional control and the management of constraints in GUANAY II autonomous underwater vehicle actuators

Autonomous underwater vehicle control has been a topic of research in the last decades. The challenges addressed vary depending on each research group's interests. In this paper, we focus on the predictive functional control (PFC), which is a control strategy that is easy to understand, install, tune, and optimize. PFC is being developed and applied in industrial applications, such as distillation, reactors, and furnaces. This paper presents the rst application of the PFC in autonomous underwater vehicles, as well as the simulation results of PFC, fuzzy, and gain scheduling controllers. Through simulations and navigation tests at sea, which successfully validate the performance of PFC strategy in motion control of autonomous underwater vehicles, PFC performance is compared with other control techniques such as fuzzy and gain scheduling control. The experimental tests presented here offer effective results concerning control objectives in high and intermediate levels of control. In high-level point, stabilization and path following scenarios are proven. In the intermediate levels, the results show that position and speed behaviors are improved using the PFC controller, which offers the smoothest behavior. The simulation depicting predictive functional control was the most effective regarding constraints management and control rate change in the Guanay II underwater vehicle actuator. The industry has not embraced the development of control theories for industrial systems because of the high investment in experts required to implement each technique successfully. However, this paper on the functional predictive control strategy evidences its easy implementation in several applications, making it a viable option for the industry given the short time needed to learn, implement, and operate, decreasing impact on the business and increasing immediacy.Peer ReviewedPostprint (author's final draft

A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean

The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mission. The lower layer is in charge of generating optimal trajectories based on sequence of tasks and dynamicity of operating terrain. The mission planner is able to reactively re-arrange the tasks based on mission/terrain updates while the low level planner is capable of coping unexpected changes of the terrain by correcting the old path and re-generating a new trajectory. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The computational engine of the mentioned framework is based on the biogeography based optimization (BBO) algorithm that is capable of providing efficient solutions. To evaluate the performance of the proposed framework, firstly, a realistic model of undersea environment is provided based on realistic map data, and then several scenarios, treated as real experiments, are designed through the simulation study. Additionally, to show the robustness and reliability of the framework, Monte-Carlo simulation is carried out and statistical analysis is performed. The results of simulations indicate the significant potential of the two-level hierarchical mission planning system in mission success and its applicability for real-time implementation

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

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

A Hybrid Systems Model Predictive Control Framework for AUV Motion Control

A computationally efficient architecture to control formations of Autonomous Underwater Vehicles (AUVs) is presented and discussed in this article. The proposed control structure enables the articulation of resources optimization with state feedback control while keeping the onboard computational burden very low. These properties are critical for AUVs systems as they operate in contexts of scarce resources and high uncertainty or variability. The hybrid nature of the controller enables different modes of operation, notably, in dealing with unanticipated obstacles. Optimization and feedback control are brought in by a novel Model Control Predictive (MPC) scheme constructed in such a way that time-invariant information is used as much as possible in a priori off-line computation

Guidance and control of an autonomous underwater vehicle

Merged with duplicate record 10026.1/856 on 07.03.2017 by CS (TIS)A cooperative project between the Universities of Plymouth and Cranfield was aimed at designing and developing an autonomous underwater vehicle named Hammerhead. The work presented herein is to formulate an advance guidance and control system and to implement it in the Hammerhead. This involves the description of Hammerhead hardware from a control system perspective. In addition to the control system, an intelligent navigation scheme and a state of the art vision system is also developed. However, the development of these submodules is out of the scope of this thesis. To model an underwater vehicle, the traditional way is to acquire painstaking mathematical models based on laws of physics and then simplify and linearise the models to some operating point. One of the principal novelties of this research is the use of system identification techniques on actual vehicle data obtained from full scale in water experiments. Two new guidance mechanisms have also been formulated for cruising type vehicles. The first is a modification of the proportional navigation guidance for missiles whilst the other is a hybrid law which is a combination of several guidance strategies employed during different phases of the Right. In addition to the modelling process and guidance systems, a number of robust control methodologies have been conceived for Hammerhead. A discrete time linear quadratic Gaussian with loop transfer recovery based autopilot is formulated and integrated with the conventional and more advance guidance laws proposed. A model predictive controller (MPC) has also been devised which is constructed using artificial intelligence techniques such as genetic algorithms (GA) and fuzzy logic. A GA is employed as an online optimization routine whilst fuzzy logic has been exploited as an objective function in an MPC framework. The GA-MPC autopilot has been implemented in Hammerhead in real time and results demonstrate excellent robustness despite the presence of disturbances and ever present modelling uncertainty. To the author's knowledge, this is the first successful application of a GA in real time optimization for controller tuning in the marine sector and thus the thesis makes an extremely novel and useful contribution to control system design in general. The controllers are also integrated with the proposed guidance laws and is also considered to be an invaluable contribution to knowledge. Moreover, the autopilots are used in conjunction with a vision based altitude information sensor and simulation results demonstrate the efficacy of the controllers to cope with uncertain altitude demands.J&S MARINE LTD., QINETIQ, SUBSEA 7 AND SOUTH WEST WATER PL