Adaptive sliding mode fractional‐order control of autonomous underwater vehicles

This paper studies the three‐dimensional (3‐D) dynamic trajectory tracking control of an autonomous underwater vehicle (AUV). As AUV is a typical nonlinear system, each degree of freedom is strongly coupled, so the traditional control method based on the nominal model of AUV cannot guarantee the accuracy of the control system. To solve this problem, we first propose a prediction model based on a radial basis function neural network (RBF‐NN). The nonlinearity of AUV is learned and modeled offline by RBF‐NN based on previous data. This model can reflect the time sequence state and control variables of AUV. Secondly, to avoid the overfitting problem in network training based on the traditional gradient descent method, a new adaptive chaotic sparrow search algorithm (ACSSA) is proposed to optimize the network parameters, to improve the full approximation ability of RBF‐NN to nonlinear systems. To eliminate the steady‐state error caused by external interference during AUV trajectory tracking, a nonlinear optimizer is designed by updating the deviation of the NN model output layer. In each sampling period, the predictive control law is calculated online according to the deviation between the predicted value and the actual value. In addition, the stability analysis based on the Lyapunov method proves the asymptotic stability of the controller. Finally, the 3‐D dynamic trajectory tracking the performance of AUV under different external disturbances is verified by MATLAB/Simulink, and the results show that the proposed controller is more efficient and robust than the standard model predictive controller (MPC) controller and the standard NN model predictive controller (NNPC).

An efficient hybrid approach for trajectory tracking control of autonomous underwater vehicles

Learning‐based robust control methodologies under information constraints

This paper describes research on remote monitoring and control of autonomous underwater vehicles (AUVs). This work is part of a larger effort to create autonomous ocean sampling networks (AOSN), a new concept for collecting synoptic oceanographic data. AOSN is based on the operation of small, low cost autonomous underwater vehicles (AUVs) within an array of moorings that provide communication and power-supply nodes. Critical to the realization of AOSN will be the ability to control these assets and monitor the data collection process remotely. During the time in which our AOSN partners have been developing acoustic communications, our laboratory has performed a variety of remote monitoring and control experiments to test different types of network distributed control software. Experimental results are presented for runs in the Charles River in which a radio modem towed on a float was used to monitor and control the vehicle from a workstation on the Internet. In addition, we have used our acoustic navigation system to send simple command messages to an Odyssey vehicle, achieving simultaneous control of multiple AUVs operating in a high current environment in the Haro Strait.

SUMMARYMost of the previous works on the motion control of autonomous underwater vehicles (AUVs) assume that (i) the vehicle actuators are able to tolerate every level of the control signals, and (ii) the vehicle is equipped with the velocity sensors in all degrees of freedom. These assumptions are not desirable in practice. Toward this end, this paper addresses the trajectory tracking control of the underactuated AUVs with the limited torque, without the velocity measurements and under environmental disturbances in a three-dimensional space. At first, a variable transformation is introduced which helps us to derive a second-order dynamic model for underactuated AUVs. Then, a saturated tracking controller is proposed by employing the saturation functions to bound the closed-loop error variables. This technique reduces the risk of the actuators saturation by decreasing the amplitude of the generated control signals. In addition, a nonlinear saturated observer is introduced to remove the velocity sensors from the control system. The proposed controller copes with the uncertain vehicle parameters, and constant or time-varying environmental disturbances induced by the waves and ocean currents. Lyapunov's direct method is used to show the semi-global uniform ultimate boundedness of the tracking and state estimation errors. Finally, some simulation results illustrate the effectiveness of the proposed controller.

This paper deals with adaptive control of a nonlinear autonomous underwater vehicle (AUV) in the presence of actuator saturation. Despite the importance of actuator saturation as a practical involvement in the control of autonomous vehicles, it has been considered less in control of AUVs. Therefore, the adaptive $$\mu $$ -modification control method is utilized in this paper for pitch channel autopilot of the REMUS AUV. The designed adaptive $$\mu $$ -modified controller has been applied to the nonlinear six degrees of freedom model of the vehicle. Coefficients of the model are assumed unknown to be coped with an adaptive controller. Performance of the designed controller is compared with that of a direct Model reference adaptive control (MRAC) in six degrees of freedom through simulations. Problems of the MRAC in the presence of input constraints are shown. Shortcomings of conventional adaptive methods in the presence of input constraints are studied. It is finally deduced that how the adaptive controller with $$\mu $$ -modification can perform suitable performance in the presence of the actuator saturation.

Most studies so far on trajectory tracking control of autonomous underwater vehicle (AUV) have assumed that the Euler angles are exactly known. However, the AUV inevitably suffers from external environmental disturbances which are driven by wind, density, and temperature gradients. The attitude transducers cannot derive accurate attitude information of the AUV. Additionally, the uncertain hydrodynamic parameters affect the stability of the system. Consequently, it is unknown whether tracking performance of the AUV can be guaranteed. In order to overcome these drawbacks, in this paper, a finite-time controller is developed by using the nonsingular fast terminal sliding mode control technique. A robust differentiator is proposed to estimate the external disturbances and uncertain parts. Simulations are performed to show that with the proposed control laws, the AUV converges to the desired trajectory even in the presence of external disturbances and system uncertainty.

Cooperative control of multiple autonomous underwater vehicles (AUVs) plays an important role on marine scientific investigation and marine development. The formation of multi-AUV can significantly enhance applications on the marine sampling, imaging, surveillance and communications. Compared to the formation control of multi-robot, the formation control of multi-AUV is particularly difficult, especially on controlling attitude and direction of AUV; what is more, the communication method among AUVs is acoustic. When communication distance increases, the communication qualities deteriorate quickly; this mainly makes time-delay, signal attenuation and distortion. Although formation control of multiple AUVs obtains a wide range of attention in recent years, the fruits on formation control problem are less than ones on land multi-robot problems. For example, Fiorelli conducted a collaborative and adaptive sampling research of multi-AUV at the Monterey Bay [; Yu and Ura carried out the cable-based modular fast-moving and obstacle-avoidance experiments, and presented an interconnected multi-AUV system with three-dimension sensors. On the aspect of formation control framework [2-, [ proposed a four-layer cooperative control strategy based on hierarchical structure; [ proposed a hierarchical control framework based on hybrid model. In addition, Yang converted a nonholonomic system to a chain one and designed a controller to implement a leader-follower formation for multiple AUVs in [. The formation control for multiple autonomous underwater vehicles is rather different than the control methods for other vehicles, because the formation control for AUVs is of its characteristics, such as the large-scale distribution in space. The finite-time consensus controller designing based on finite-time control and consensus problem has important theoretical and practical significance. The decentralized controller methods for the autonomous underwater vehicle are applied more and more, but they ignore the coupling relationship between them. Another method is that an AUV is modeling as an agent, but this method ignores attitude characteristics of AUVs (pitch, roll and yaw). In this paper, we consider the cooperative control problem in three dimensional spaces. Finite-time formation for Autonomous Underwater Vehicles (AUVs) with constraints on communication range is investigated. We proposed a two-layer finite-time consensus control law, to avoid leading to collapse on formation because of failure leader, all AUVs are arrayed in the same level and each AUV can obtain global formation information. Finally, the simulation results show the effectiveness of the control strategy.

In this paper, a novel dynamic surface sliding mode control method is proposed for three-dimensional trajectory tracking control of autonomous underwater vehicle (AUV) in the presence of model errors. To enhance the robustness, the sliding mode control approach is modified by employing dynamic surface control (DSC). The radial basis function neural network (RBFNN) approximation technique is used for approximating model errors, furthermore the norm of the ideal weighting vector in neural network system is considered as the estimation parameter, such that only one parameter is adjusted. The proposed controller guarantees uniform ultimate boundedness (UUB) of all the signals in the closed-loop system via Lyapunov stability analysis, while the tracking errors converge to a small neighborhood of the desired trajectory. Finally, simulation studies are given to illustrate the performance of the proposed algorithm.

This article focuses on the motion control of autonomous underwater vehicles in the ocean environment by a robust adaptive controller in which there is no regressor matrix. Due to the different atmospheric conditions in the ocean environment, the hydrodynamic coefficients of autonomous underwater vehicles cannot be exactly available and there are many uncertainties in the dynamic model. This prevents the traditional controllers to overcome these difficulties immediately. Hence, developing the adaptive controllers for the autonomous underwater vehicles encountered with uncertainties is required to provide appropriate performance. In the conventional adaptive control system, it is assumed that the autonomous underwater vehicle dynamic model can be linearly written into regressor form. Since the dynamics of the underwater vehicles is very complex, the derivation of the regressor matrix is very tedious. To overcome these problems, a regressor-free adaptive controller is proposed for the autonomous underwater vehicles in the general form of the equations of motion. In this approach, the controller is derived by the inverse dynamic method. Also, by utilizing known basis functions weighted by constant unknown coefficients, the uncertainties of the control law are estimated. The adaptation laws are derived based on Lyapunov stability theorem. The validity of the proposed method is verified by some simulation experiments. The simulation results show that the proposed approach can improve the robustness of adaptive controller to the dynamic model uncertainties and the external disturbance.

AUV position tracking and trajectory control based on fast-deployed deep reinforcement learning method

This paper presents a design method for output-feedback path-following control of under-actuated autonomous underwater vehicles moving in a vertical plane without using surge, heave, and pitch velocities. Specifically, an extended state observer (ESO) is developed to recover the unmeasured velocities as well as to estimate total uncertainty induced by internal model uncertainty and external disturbance. At the kinematic level, a commanded guidance law is developed based on a vertical line-of-sight guidance scheme and the observed velocities. To optimize guidance signals, optimization-based reference governors are formulated as bound-constrained quadratic programming problems for computing optimal reference signals. Two globally convergent recurrent neural networks called projection neural networks are used to solve the optimization problems in real-time. Based on the optimal reference signals and ESO, a kinetic control law with disturbance rejection capability is constructed at the kinetic level. It is proved that all error signals in the closed-loop system are uniformly and ultimately bounded. Simulation results substantiate the efficacy of the proposed method for output-feedback path-following of under-actuated autonomous underwater vehicles.

Adaptive event-triggered coordination control of unknown autonomous underwater vehicles under communication link faults

This paper investigates the problem of trajectory tracking control of underactuated autonomous underwater vehicles (AUVs) with predefined time convergence. To this end, a predefined time nonlinear disturbance observer (PTNDO) is designed with the aim of accurately estimating unknown external disturbances. This observer is capable of capturing the dynamic influences on the AUV in real time to support the subsequent control strategy. By setting the time parameters reasonably, the convergence time of the system can be set in advance, thus ensuring that the desired tracking effect is achieved within a specific time. In addition, a predefined time backstepping controller (PTBC) is designed for the problem of AUV trajectory tracking. Theoretical analysis shows that the upper limit of its convergence time is only determined by a constant, which simplifies the parameter tuning process and enhances the practicability compared with the existing finite/fixed time control methods. The effectiveness and superiority of the proposed method are verified through further simulation studies, which show that the control strategy has good trajectory tracking performance.

Set point tracking control of autonomous underwater vehicle (AUV) via robust model predictive control (RMPC) is considered. Input-constrained RMPC with integral action, which has been developed in our previous work, is used to control the AUV in this study. In order to derive a RMPC control rule, non-linear dynamics of AUV with six degree of freedom is linearized at certain operating points. So, horizontal and vertical plane dynamics of system are represented by linear models which have polytopic uncertainties. Since the derived control rule will be used in real time, the computation time should be reduced. To overcome this computational time problem and get rid of trial–error step of Algorithm 1, a new algorithm is proposed here. The simulations are carried out using the control rule based on this algorithm and these results are presented.