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

Autonomous homing and docking tasks for an underwater vehicle

This paper briefly introduces a strategy for autonomous homing and docking tasks using an autonomous underwater vehicle. The control and guidance based path following for those tasks are described in this work. A standard sliding mode for controller design is briefly given. The method provides robust motion control efforts for an underwater vehicle’s decoupled system whilst minimising chattering effects. In a guidance system, the vector field based on a conventional artificial potential field method gives a desired trajectory with a use of existing information from sensors in the network. A well structured Line-of-Sight method is used for an AUV to follow the path. It provides guidance for an AUV to follow the predefined trajectory to a required position with the final desired orientation at the dock. Integration of a control and guidance system provides a complete system for this application. Simulation studies are illustrated in the paper

Experimental Verification of a Depth Controller using Model Predictive Control with Constraints onboard a Thruster Actuated AUV

In this work a depth and pitch controller for an autonomous underwater vehicle (AUV) is developed. This controller uses the model predictive control method to manoeuvre the vehicle whilst operating within the defined constraints of the AUV actuators. Experimental results are given for the AUV performing a step change in depth whilst maintaining zero pitch

Pose Detection and control of multiple unmanned underwater vehicles using optical feedback

This paper proposes pose detection and control algorithms in order to control the relative pose between two Unmanned Underwater Vehicles (UUVs) using optical feedback. The leader UUV is configured to have a light source at its crest which acts as a guiding beacon for the follower UUV which has a detector array at its bow. Pose detection algorithms are developed based on a classifier, such as the Spectral Angle Mapper (SAM), and chosen image parameters. An archive look-up table is constructed for varying combinations of 5-degree-of-freedom (DOF) motion (i.e., translation along all three coordinate axes as well as pitch and yaw rotations). Leader and follower vehicles are simulated for a case in which the leader is directed to specific waypoints in horizontal plane and the follower is required to maintain a fixed distance from the leader UUV. Proportional-Derivative (PD) control (without loss of generality) is applied to maintain stability of the UUVs to show proof of concept. Preliminary results indicate that the follower UUV is able to maintain its fixed distance relative to the leader UUV to within a reasonable accuracy

Improving the energy efficiency of autonomous underwater vehicles by learning to model disturbances

Energy efficiency is one of the main challenges for long-term autonomy of AUVs (Autonomous Underwater Vehicles). We propose a novel approach for improving the energy efficiency of AUV controllers based on the ability to learn which external disturbances can safely be ignored. The proposed learning approach uses adaptive oscillators that are able to learn online the frequency, amplitude and phase of zero-mean periodic external disturbances. Such disturbances occur naturally in open water due to waves, currents, and gravity, but also can be caused by the dynamics and hydrodynamics of the AUV itself. We formulate the theoretical basis of the approach, and demonstrate its abilities on a number of input signals. Further experimental evaluation is conducted using a dynamic model of the Girona 500 AUV in simulation on two important underwater scenarios: hovering and trajectory tracking. The proposed approach shows significant energy-saving capabilities while at the same time maintaining high controller gains. The approach is generic and applicable not only for AUV control, but also for other type of control where periodic disturbances exist and could be accounted for by the controller. © 2013 IEEE

State-Space Feedback Linearization for Depth Positioning of a Spherical URV

Variable ballast, a common mechanism in underwater vehichle, is utilized as vertical motion actuator of a spherical URV in order to control its depth positiong. Since the model of this system is nonlinear and controllable therefore state-space feedback linearization is utilized in this depth positioning. The idea of state-space feedback linearization is to algebraically transform all state variable of nonlinear systems dynamics into (fully or partly) linear ones, so that linear control techniques can be applied. This method can stabilize the equilibrium point of this system which is unstable in open loop system. From the control analysis and simulation results, it can be observed that the asymptotical stabilization is achieved by tracking the error. Hence, state-space feedback linearization can also be applied for tracking a trajectory of desired depth position. Keyword: variable ballast, spherical URV, feedback linearizatio

Dynamic Control of Mobile Multirobot Systems: The Cluster Space Formulation

The formation control technique called cluster space control promotes simplified specification and monitoring of the motion of mobile multirobot systems of limited size. Previous paper has established the conceptual foundation of this approach and has experimentally verified and validated its use for various systems implementing kinematic controllers. In this paper, we briefly review the definition of the cluster space framework and introduce a new cluster space dynamic model. This model represents the dynamics of the formation as a whole as a function of the dynamics of the member robots. Given this model, generalized cluster space forces can be applied to the formation, and a Jacobian transpose controller can be implemented to transform cluster space compensation forces into robot-level forces to be applied to the robots in the formation. Then, a nonlinear model-based partition controller is proposed. This controller cancels out the formation dynamics and effectively decouples the cluster space variables. Computer simulations and experimental results using three autonomous surface vessels and four land rovers show the effectiveness of the approach. Finally, sensitivity to errors in the estimation of cluster model parameters is analyzed.Fil: Mas, Ignacio Agustin. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kitts, Christopher. Santa Clara University; Estados Unido