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

Nonlinear H ∞ optimal control scheme for an underwater vehicle with regional function formulation

A conventional region control technique cannot meet the demands for an accurate tracking performance in view of its inability to accommodate highly nonlinear system dynamics, imprecise hydrodynamic coefficients, and external disturbances. In this paper, a robust technique is presented for an Autonomous Underwater Vehicle (AUV) with region tracking function. Within this control scheme, nonlinear H∞ and region based control schemes are used. A Lyapunov-like function is presented for stability analysis of the proposed control law. Numerical simulations are presented to demonstrate the performance of the proposed tracking control of the AUV. It is shown that the proposed control law is robust against parameter uncertainties, external disturbances, and nonlinearities and it leads to uniform ultimate boundedness of the region tracking error

A Robust Model Predictive Control Approach for Autonomous Underwater Vehicles Operating in a Constrained workspace

This paper presents a novel Nonlinear Model Predictive Control (NMPC) scheme for underwater robotic vehicles operating in a constrained workspace including static obstacles. The purpose of the controller is to guide the vehicle towards specific way points. Various limitations such as: obstacles, workspace boundary, thruster saturation and predefined desired upper bound of the vehicle velocity are captured as state and input constraints and are guaranteed during the control design. The proposed scheme incorporates the full dynamics of the vehicle in which the ocean currents are also involved. Hence, the control inputs calculated by the proposed scheme are formulated in a way that the vehicle will exploit the ocean currents, when these are in favor of the way-point tracking mission which results in reduced energy consumption by the thrusters. The performance of the proposed control strategy is experimentally verified using a $4$ Degrees of Freedom (DoF) underwater robotic vehicle inside a constrained test tank with obstacles.Comment: IEEE International Conference on Robotics and Automation (ICRA-2018), Accepte

Time-optimal trajectory and robust adaptive control for hybrid underwater glider

The undersea environment is generally still a mystery for the human race, although it has been with us for a long time. To explore under the sea, the underwater glider is the efficient equipment capable of sustainable operation for several months. For faster and longer duration performance, a new design of underwater glider (UG) shaping ray type is proposed. To have the shortest settling time, a new design of time-optimal trajectory (TOT) for controlling the states of the ray-type hybrid underwater glider (RHUG) is proposed. And for the stable flight control, a robust adaptive controller is designed for the RHUG with unknown parameters and environmental disturbances. The heading dynamics of the RHUG is presented with linear and quadratic damping. A closed form solution of the heading dynamics is realized for designing the time-optimal trajectory. The conventional and super-twisting sliding mode control will be constructed for tracking this trajectory. The tracking performance considering the disturbance effect will be discussed in simulations. For identification of unknown parameters of the system, the adaptive control is designed and implemented by the heading experiment. The RHUG uses the net buoyancy force for gliding under the water, so the depth control is essential. In this dissertation, a robust control algorithm with TOT will be carried out for the heaving motion using a hybrid actuation of the buoyancy engine and the propeller. The net buoyancy force with a constant rate is generated by the buoyancy engine for both descending and ascending motion. And the second actuator for the depth control is the propeller with quick response in producing thrusting force. To apply the robust control with TOT, the control input is designed for the buoyancy engine and thruster individually. And finally, the robust control with TOT using the buoyancy engine and thruster is simulated with consideration of external disturbances. When the RHUG is the underactuated system, a robust adaptive control is designed for the RHUG dynamics based on Lyapunov’s direct method using the backstepping and sliding mode control techniques. The performance of this controller is simulated for gliding motion and depth control with unknown parameters and bounded disturbances.Contents Contents i List of Tables iv List of Figures v Chapter 1. Introduction 1 1.1. Hybrid underwater glider 1 1.2. Time-optimal trajectory 4 1.3. Nonlinear control design 5 Chapter 2. Dynamics of RHUG 8 2.1 Dynamics of underwater vehicles 8 2.2 Design of RHUG platform 11 2.2.1 Hull design 11 2.2.2 Buoyancy engine and mass-shifter 12 2.2.3 Battery 13 2.2.4 Sensors 14 2.2.5 Assembly 16 2.3 Dynamics of RHUG 17 2.4 Hydrodynamic coefficients 19 2.5 Thruster modeling 21 2.6 Buoyancy engine modeling 22 2.7 Mass-shifter modeling 23 Chapter 3. Time-optimal trajectory with actuator saturation for heading control 25 3.1 Time-optimal trajectory 25 3.2 Heading motion 25 3.3 Analytic solution of heading dynamic equation 26 3.3.1 Right-hand direction 29 3.3.2 Left-hand direction 36 3.4 Time-optimal trajectory 42 3.5 Super-twisting sliding mode control 44 3.6 Computer simulation 46 3.6.1 Simulation 1 46 3.6.2 Simulation 2 47 3.6.3 Simulation 3 49 Chapter 4. Time-optimal trajectory for heaving motion control using buoyancy engine and propeller individually 51 4.1. Heave dynamics and TOT 51 4.2. Analytical solution of heave dynamics with buoyancy and thruster force individually 54 4.2.1 First segment with positive rate 54 4.2.2 Second segment with maximum input 55 4.2.3 Third segment with constant velocity 56 4.2.4 Fourth segment with negative rate 57 4.2.5 Fifth segment with minimum input 58 4.3. Time-optimal trajectory for depth motion 59 4.3.1 Find z1, w1 and w1 59 4.3.2 Find t2, z2, w2 and w2 61 4.3.3 Find w3, z4 and w4 62 4.3.4 Find z3, t3 and t4 63 4.3.5 Find α and t5 64 4.4. Sliding mode control for heave dynamics 64 4.5. Computer simulation 66 4.5.1. Simulation 1 66 4.5.2. Simulation 2 69 Chapter 5. Experimental study of direct adaptive control along TOT for heading motion 72 5.1. Motivation 72 5.2. Composition of RHUG 73 5.3. Robust adaptive control for heading dynamics 77 5.4. Computer simulation 79 5.5 Experiment 82 5.5.1 First experiment with k1=2.5,k2=30 82 5.5.2 Second experiment with k1=2,k2=30 83 5.5.3 Third experiment with k1=2,k2=50 85 Chapter 6. Robust adaptive control design for vertical motion 89 6.1. Dynamics of vertical plane 89 6.2. Adaptive sliding-mode control for pitch motion 91 6.3. Adaptive sliding-mode control for surge motion 93 6.4. LOS and PI depth-keeping guidance 95 6.5. Computer simulation 97 6.5.1 Simulation 1 97 6.5.2 Simulation 2 104 Chapter 7. Conclusion 111 Reference 113Docto

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