Motion Control Of Autonomous Underwater Vehicles Research Articles

Parameter identification and model prediction path following control of underactuated AUV: Methodology and experimental verification

This paper explores the integrated optimal control scheme from the identification of underactuated autonomous underwater vehicle (AUV) system dynamics model parameters to the application of Model Predictive Control (MPC) technology in underactuated AUV motion control, and presents the simulation and field test results. First, the AUV kinematics and dynamics models are introduced to represent the horizontal plane motion characteristics. Based on Computational Fluid Dynamics (CFD) numerical simulation and overset mesh technology, AUV plane motion is simulated to determine the unknown hydrodynamic coefficients in the model, and the accuracy of the mathematical model is validated by comparing the maneuverability simulation and field test results. After that, an adaptive line-of-sight (ALOS) guiding algorithm with exponentially variable forward distance is utilized in the guidance layer to reconcile the rapidity and stability of tracking. The path following problem is formulated as an MPC optimization problem that takes into account multiple realistic constraints and then converted into a standard convex quadratic programming structure, permitting for online real-time optimization. Simulation and field test results indicate that the control algorithm proposed in this paper provides a more effective control strategy than the traditional LOS+PID controller in tracking effect and robustness.

A general motion control framework for an autonomous underwater vehicle through deep reinforcement learning and disturbance observers

This paper investigates the application of deep reinforcement learning (RL) in the motion control for an autonomous underwater vehicle (AUV), and proposes a novel general motion control framework which separates training and deployment. Firstly, the state space, action space, and reward function are customized under the condition of ensuring generality for various motion control tasks. Next, in order to efficiently learn the optimal motion control policy in the case that the AUV model is imprecise and there are unknown external disturbances, a virtual AUV model composed of the known and determined items of an actual AUV is put forward and a simulation training method is developed on this basis. Then, in the given deployment method, three independent extended state observers (ESOs) are designed to deal with the unknown items in different directions, and the final controller is obtained by compensating the estimated value of ESOs into the output of the optimal motion control policy obtained through simulation training. Finally, soft actor-critic is chosen as deep RL algorithm of the framework, and the generality and effectiveness of the proposed method are verified in four different AUV motion control tasks.

An Improved S-Plane Controller for High-Speed Multi-Purpose AUVs with Situational Static Loads

The classic S-plane control method combines PD structure with fuzzy control theory, with the advantages of a simple control structure and fewer parameters to be adjusted. It has been proved as a practical method in an autonomous underwater vehicle (AUV) motion control at low and medium speeds, but it takes no account of the situational static load and varying hydrodynamic forces which influence the control quality and even result in a “dolphin effect” at the time of high-speed movement. For this reason, an improved S-plane controller is designed based on the sliding mode variable structure, sliding mode surface, and control items in order to respond to the situational static load and high-speed movement. The improved S-plane controller is verified by Lyapunov stability analysis. The thrust allocation strategies are also discussed with constraints introduced in accordance with task requirements. In order to verify the practicability and effectiveness of the improved S-plane controller, both simulation experiments and field trials of AUV motion control, long-range cruise, and path point following were carried out. The results have demonstrated the superiority of the improved S-plane controller over the classic S-plane controller.

Motion control and path optimization of intelligent AUV using fuzzy adaptive PID and improved genetic algorithm.

This study discusses the application of fuzzy adaptive PID and improved genetic algorithm (IGA) in motion control and path optimization of autonomous underwater vehicle (AUV). The fuzzy adaptive PID method is selected because it is considered to be a strongly nonlinear and coupled system. First, this study creates the basic coordinate system of the AUV, and then analyzes the spatial force from the AUV to obtain the control model of the heading angle, climb angle, and depth. Next, the knowledge of fuzzy adaptive PID and IGA technology on AVU are investigated, then fuzzy adaptive PID controllers and path optimization are established, and experimental simulations are carried out to compare and analyze the simulation results. The research results show that controllers and IGA can be used for the motion control and path optimization of AUV. The advantages of fuzzy adaptive PID control are less overload, enhanced system stability, and more suitable for motion control and path optimization of AUV.

A general motion controller based on deep reinforcement learning for an autonomous underwater vehicle with unknown disturbances

This paper studies the application of deep Reinforcement Learning (RL) in the motion control of an underactuated autonomous underwater vehicle (AUV) with unknown disturbances. Firstly, a general state space, action space and reward function are designed for motion control problems rather than each specific motion control task, which ensures the generality of our method. Furthermore, a virtual AUV model with partial random disturbances is established, and on this basis, a simulation training method is developed to solve the problems of extremely high risk and extremely low efficiency caused by training in actual experiments. Then, in order to directly deploy the optimal control policy obtained through simulation training to an actual AUV, we employ Extended State Observers (ESOs) to estimate the unknown disturbances in five degrees of freedom, and give a deployment method using the estimated values as the disturbance state vector and compensation vector. Combining the above training method and deployment method, a novel general motion controller is proposed. Finally, four different AUV motion control simulations are carried out, and the results confirm the generality and effectiveness of our proposed controller.

H∞ robust control design for coupled Ciscrea AUV model

Objective The multi input multi output (MIMO) robust controller was proposed to realize the trajectory tracking and the yaw control for the four-DoF Ciscrea Autonomous Underwater Vehicle (AUV) model. Methods The parameter uncertainty of the inertia matrix and quadratic damping affection was formulated as an uncertain structure by the perturbation method, the general system was derived by linear fraction transformation (LFT). The H-infinity synthesis method was proposed to solve the MIMO robust controller for AUV general system, and the structure singular value analysis method was applied to compute the robust stability margin. To validate the robust yaw controller, the paper simulated AUV trajectory tracking as well as the yaw control scenario with MATLAB. The tracking performance was compared between interference and non-interference control condition. Results The upper bound and lower bound of the structure singular value was analyzed for the stable robust controller, and the elimination of the perturbation on AUV output showed effective anti-jamming performance. Conclusion The proposed control method is available for parameter uncertainty and nonlinearity issues on the AUV model. This paper provides a specific application for AUV motion and attitude control problem in the complicated ocean environment.

Technology of cybernetic models’ creating for the synthesis and refinement of autonomous underwater vehicle motion control system controllers

The technology of cybernetic models’ creating for the synthesis and refinement of algorithms for controlling the autonomous underwater vehicle’s (AUV’s) motion is considered. The paper gives a description of the following sequence of actions performed at each stage of the cybernetic model development: dynamic model type and parameters identification, controller type selection, construction and analysis of the considered control loop block diagram, determination of the controller’s optimal parameters. The synthesis of heading, pitch and depth stabilization regulators for streamlined shape AUV during propulsion engines controlling is performed. The results of numerical modeling and research of the various factors influence on the quality of control are presented.

Nonlinear diving stability and control for an AUV via singular perturbation

This paper presents control design and nonlinear stability analysis for path-following of an underactuated autonomous underwater vehicle (AUV), with dynamics restricted to the longitudinal plane. For this AUV motion control system, the speed of translational dynamics is much slower than that of orientation dynamics. Such characteristic allows for a mathematical setup which can be naturally treated by standard singular perturbation theory. Based on this analysis, a simple control law is designed with time scale decomposition method, which is able to stabilize an AUV to desired diving path by selecting desired collective pitch rate and pitch angle. Its nonlinear stability analysis is performed by constructing Lyapunov functions, in term of singular perturbation method. The paper also proves that the control law is robust in the presence of bounded disturbances and model uncertainties, using the small-gain theorem. Furthermore, simulation results are presented to illustrate the control performance.

Saturation Based Nonlinear FOPD Motion Control Algorithm Design for Autonomous Underwater Vehicle

The application of Autonomous Underwater Vehicle (AUV) is expanding rapidly, which drives the urgent need of its autonomy improvement. Motion control system is one of the keys to improve the control and decision-making ability of AUVs. In this paper, a saturation based nonlinear fractional-order PD (FOPD) controller is proposed for AUV motion control. The proposed controller is can achieve better dynamic performance as well as robustness compared with traditional PID type controller. It also has the advantages of simple structure, easy adjustment and easy implementation. The stability of the AUV motion control system with the proposed controller is analyzed through Lyapunov method. Moreover, the controlled performance can also be adjusted to satisfy different control requirements. The outperformed dynamic control performance of AUV yaw and depth systems with the proposed controller is shown by the set-point regulation and trajectory tracking simulation examples.

Design Boundary Layer Thickness and Switching Gain in SMC Algorithm for AUV Motion Control

SummaryDesigning the boundary layer thickness and switching gain in the nonlinear part of sliding mode controller (SMC) is one of the main subjects in SMC design that needs human experience, knowledge on the amplitude of disturbances, and information about the bounds of system uncertainties. In this paper, to reduce the trial-and-error effort by the designer(s) two different fitness functions in the horizontal and vertical planes are presented and a heuristic method is used for their optimization. The optimal switching gain in the proposed approach properly compensates the unmodeled dynamics, model uncertainty, and external disturbances. Chattering phenomenon in control signals and noise measurement effect are reduced by the optimal selection of boundary layer thickness. This proposed method is applied to an autonomous underwater vehicle (AUV) and evaluated through the real-time and cost-effective manner. The execution code is implemented on a single-board computer (SBC) through the xPC Target and is evaluated by the processor-in-the-loop (PIL) test. The results of the PIL test in the two different test cases indicate that the chattering phenomenon and amplitude of control signal applied to the actuators are reduced in comparison with the three conventional approaches in the AUV motion control.

Flocking Control of Multiple AUVs Based on Fuzzy Potential Functions

This paper presents the formulation of a flocking control algorithm for a group of autonomous underwater vehicles (AUVs). A leader–follower control strategy is employed to flock a group of AUVs along a predefined desired path. In this approach, leader AUVs are assumed to have global knowledge of the desired trajectory and the follower AUVs are not provided with this information. For keeping all the AUVs connected in a group, a flocking center is estimated. This flocking center is a virtual point whose position at any instant of time can be predicted by using a consensus algorithm. The controllers for the leader and follower AUVs are developed by implementing mathematical and fuzzy artificial potential functions. A group of four AUVs is considered for analyzing the efficacy of the developed control algorithm. Simulations are carried out both in obstacle-free and obstacle-rich environments. From the obtained results, it is observed that the proposed fuzzy flocking control algorithm provides effective cooperative motion control of multiple AUVs along the desired paths and avoid obstacles successively. It is also observed that the flocking controller developed based on fuzzy artificial potential function outperforms the controller with mathematical potential functions despite uncertainties owing to external disturbances.

Model-Free Recurrent Reinforcement Learning for AUV Horizontal Control

In this paper, aiming at the problems of 2-DOF horizontal motion control with high precision for autonomous underwater vehicle(AUV) trajectory tracking tasks, deep reinforcement learning controllers are applied to these conditions. These control problems are considered as a POMDP (Partially Observable Markov Decision Process). Model-free reinforcement learning(RL) algorithms for continuous control mission based on Deterministic Policy Gradient(DPG) allows robots learn from received delayed rewards when interacting with environments. Recurrent neural networks LSTM (Long Short-Term Memory) are involved into the reinforcement learning algorithm. Through this deep reinforcement learning algorithm, AUVs learn from sequences of dynamic information. The horizontal trajectory tracking tasks are described by LOS method and the motion control are idealized as a SISO model. Tanh-estimators are presented as data normalization. Moreover, AUV horizontal trajectory tracking and motion control simulation results demonstrate this algorithm gets better accuracy compared with the PID method and other non-recurrent methods. Efforts show the efficiency and effectiveness of the improved deep reinforcement learning algorithm.

Design of novel sliding-mode controller for high-velocity AUV with consideration of residual dead load

This work focuses on motion control of high-velocity autonomous underwater vehicle (AUV). Conventional methods are effective solutions to motion control of low-and-medium-velocity AUV. Usually not taken into consideration in the control model, the residual dead load and damping force which vary with the AUV’s velocity tend to result in difficulties in motion control or even failure in convergence in the case of high-velocity movement. With full consideration given to the influence of residual dead load and changing damping force upon AUV motion control, a novel sliding-mode controller (SMC) is proposed in this work. The stability analysis of the proposed controller is carried out on the basis of Lyapunov function. The sea trials results proved the superiority of the sliding-mode controller over sigmoid-function-based controller (SFC). The novel controller demonstrated its effectiveness by achieving admirable control results in the case of high-velocity movement.

Synthesis of a PID-controller of a trim robust control system of an autonomous underwater vehicle

Autonomous underwater vehicles are often used for performing scientific, emergency or other types of missions under harsh conditions and environments, which can have non-stable, variable parameters. So, the problem of developing autonomous underwater vehicle motion control systems, capable of operating properly in random environments, is highly relevant. The paper is dedicated to the synthesis of a PID-controller of a trim robust control system, capable of keeping an underwater vehicle stable during a translation at different angles of attack. In order to synthesize the PID-controller, two problems were solved: a new method of synthesizing a robust controller was developed and a mathematical model of an underwater vehicle motion process was derived. The newly developed mathematical model structure is simpler than others due to acceptance of some of the system parameters as interval ones. The synthesis method is based on a system poles allocation approach and allows providing the necessary transient process quality in a considered system.

A backstepping approach for the formation control of multiple autonomous underwater vehicles using a leader–follower strategy

ABSTRACTThis paper presents a Lyapunov-based backstepping approach for developing cooperative motion control for multiple autonomous underwater vehicles (AUVs). The cooperative motion is achieved using a leader–follower (LF) formation strategy in the presence of discrete data transmission between the leader AUV and the follower AUVs. To address the problem of sampled data transmission between the leader and the followers, an estimator is designed for each follower to estimate the states of the leader AUV. The proposed backstepping control algorithm enables the follower AUVs to successfully follow the desired virtual frames provided by the leader AUV. Thus, a formation shape is maintained by the followers while traversing the desired path. The simulation results suggest that the proposed Lyapunov-based backstepping controller is effective for achieving successful cooperative motion control of multiple AUVs in the presence of discrete data transmission.

Smooth transition of AUV motion control: From fully-actuated to under-actuated configuration

This paper addresses the problem of steering autonomous underwater vehicle (AUV) along a desired horizontal path throughout the full-range low-speed and high-speed profiles, experiencing both fully-actuated and under-actuated configurations. First, a nonlinear controller adopting Lyapunov’s direct method and backstepping technique is proposed for under-actuated AUV, based on the Line-of-Sight guidance built in a moving Frenet–Serret frame. And then, the controller is adapted to fully-actuated AUV except that the control computation for the evolution of the side-slip angle is different from the case of under-actuated one. Hence, both the fully-actuated and under-actuated configurations are under the same control framework, which enables a smooth continuous transition between two configurations in a synthesized controller. Finally, simulation results illustrate the performance of the proposed control design, where the varied control efforts in the sway direction clearly show the transitions from fully-actuated to under-actuated configuration.

Identification Method of Hydrodynamic Parameters of Autonomous Underwater Vehicle Based on Genetic Algorithm

With a view to the difficulty of the traditional hydrodynamic parameters experiments and theoretical calculation methods for autonomous underwater vehicle(AUV) and the drawback of optimum results tending to local optimization of the ever identification methods,a genetic algorithm based hydrodynamic parameters identification method of AUV is presented. This method has better robustness and can find the globally optimal point without initial values of parameters. It can provide a dynamic model for motion control,condition prediction and control system exploitation of AUV. Aimed at an open-frame AUV prototype named by CRanger-01,the genetic algorithm is used to identify the horizontal plane hydrodynamic parameters based on the analysis of the dynamic modeling. Then nineteen horizontal plane hydrodynamic parameters of CRanger-01 are identified,compared with the actual values,the identification errors are in the allowable range. By the simulation of the dynamic model,it demonstrates that the method can identify the hydrodynamic parameters of AUV effectively and can provide a reference for engineering practice.