Improving AUV Stability and vSLAM Performance using a Combined Nonlinear Disturbance Observer and Predictive Control under Ocean Disturbances

Autonomous Underwater Vehicles (AUVs) play a crucial role in deep-sea exploration, but their stability is often compromised by Internal Solitary Waves (ISWs) and nonlinear disturbances in stratified waters. This study aims to evaluate the performance of two control algorithms, Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC), in mitigating ISW effects on AUV trajectory tracking. Simulations were conducted in Simulink (MATLAB), modeling AUV dynamics under ISW disturbances with intensities ranging from 0% to 100%. The results reveal that both PID and SMC algorithms experience significant performance degradation as ISW intensity increases, with Root Mean Square Error (RMSE) values rising exponentially between 50% and 75% disturbance levels. While SMC offers better resilience to nonlinear disturbances than PID, neither algorithm fully compensates for high ISW intensities. These findings highlight the limitations of conventional control strategies and underscore the need for more robust, adaptive algorithms for reliable deep-sea AUV operations. Future work will explore Nonlinear Model Predictive Control (NMPC) for improved stability in complex marine environments.

This paper employs nonlinear disturbance observer for robust Nonlinear Model Predictive Control (NMPC) of biped robots. The NMPC is used in order to imitate some properties of human walking, which is optimal and uses some basic goals and constraints, yielding safe and stable walking. Since there may be some uncertainties in the dynamics or parameters variations in the biped model, the controller robustness is also considered. However, the NMPC is a model based controller; this characteristic reduces the effectiveness of the NMPC based controlling. In order to overcome this shortcoming of the NMPC, the nonlinear disturbance observer (NDO) will be used to robustify the proposed controller against dynamic uncertainties in the biped robot and rejecting external disturbances. Simulation results reveal better performance of the nonlinear-disturbance-observer-based NMPC as compared to the previously reported NMPC controllers.

In this paper a nonlinear model predictive control strategy based on enhanced nonlinear disturbance observer is proposed to control the dynamic walking of biped robots on the smooth surface considering double support phase, single support phase, and impact. Optimal tracking of reference trajectories via optimal joint torque is established via a nonlinear predictive controller with well-defined cost functions and associated constraints. The implementation of a conventional disturbance observer encounters numerous challenges due to the joint acceleration requirements. The proposed nonlinear disturbance observer here, which only requires the position and angular velocity, helps to estimate the disturbances introduced on the robot and reduce the complications. The simulation results performed on the dynamic walking of a 5-DOF biped robot on flat surface shows the merits of the proposed method in tracking arbitrary trajectories despite the disturbances.

This paper presents a solution for the tracking control problem, for an unmanned ground vehicle (UGV), under the presence of skid-slip and external disturbances in an environment with static and moving obstacles. To achieve the proposed task, we have used a path-planner which is based on fast nonlinear model predictive control (NMPC); the planner generates feasible trajectories for the kinematic and dynamic controllers to drive the vehicle safely to the goal location. Additionally, the NMPC deals with dynamic and static obstacles in the environment. A kinematic controller (KC) is designed using evolutionary programming (EP), which tunes the gains of the KC. The velocity commands, generated by KC, are then fed to a dynamic controller, which jointly operates with a nonlinear disturbance observer (NDO) to prevent the effects of perturbations. Furthermore, pseudo priority queues (PPQ) based Dijkstra algorithm is combined with NMPC to propose optimal path to perform map-based practical simulation. Finally, simulation based experiments are performed to verify the technique. Results suggest that the proposed method can accurately work, in real-time under limited processing resources.

Robust nonlinear model predictive control for reference tracking of dynamic positioning ships based on nonlinear disturbance observer

Autonomous underwater vehicles (AUVs) are indispensable for ocean exploration; however, achieving accurate trajectory tracking control suffers from significant challenges due to internal model uncertainties and external disturbances, stemming from unmodeled hydrodynamics, parametric uncertainties, and time-varying environmental forces. Moreover, the presence of inherent velocity constraints may further restrict actuator performance, leading to increased tracking errors or even system instability. Taking into account these issues, this study proposes a tightly coupled nonlinear continuous sliding mode predictive control (NCSMPC) scheme to tackle the trajectory tracking problem for AUV. First, the Lyapunov-based nonlinear model predictive control (NMPC) kinematic controller is developed for the position loop to achieve finite-time tracking, which generates the constrained velocity signals to consistently achieve optimal tracking performance in accordance with the online optimization function. Meanwhile, an adaptive integral event-triggering mechanism (ETM) is introduced to reduce the computational burden by adaptively regulating the frequency of optimization updates. Then, an adaptive continuous sliding mode controller (ACSM) based on the adaptive super-twisting algorithm is proposed for the velocity loop. The designed dynamic controller can actively compensate for unmodeled hydrodynamics and time-varying disturbances without requiring prior knowledge of their bounds, thereby achieving finite-time velocity tracking with low oscillation. Guaranteed by Lyapunov stability, the proposed control framework integrates the optimization of NMPC with the robust performance of ACSM, thereby ensuring high-performance position-velocity closed-loop tracking. Finally, simulations and real-time experiments on a robot-operating-system (ROS)-based AUV model validate the effectiveness of the proposed controller.

The trajectory tracking control problem is addressed for autonomous underwater vehicle (AUV) in marine environment, with presence of the influence of the uncertain factors including ocean current disturbance, dynamic modeling uncertainty, and thrust model errors. To improve the trajectory tracking accuracy of AUV, an adaptive backstepping terminal sliding mode control based on recurrent neural networks (RNN) is proposed. Firstly, considering the inaccurate of thrust model of thruster, a Taylor’s polynomial is used to obtain the thrust model errors. And then, the dynamic modeling uncertainty and thrust model errors are combined into the system model uncertainty (SMU) of AUV; through the RNN, the SMU and ocean current disturbance are classified, approximated online. Finally, the weights of RNN and other control parameters are adjusted online based on the backstepping terminal sliding mode controller. In addition, a chattering-reduction method is proposed based on sigmoid function. In chattering-reduction method, the sigmoid function is used to realize the continuity of the sliding mode switching function, and the sliding mode switching gain is adjusted online based on the exponential form of the sliding mode function. Based on the Lyapunov theory and Barbalat’s lemma, it is theoretically proved that the AUV trajectory tracking error can quickly converge to zero in the finite time. This research proposes a trajectory tracking control method of AUV, which can effectively achieve high-precision trajectory tracking control of AUV under the influence of the uncertain factors. The feasibility and effectiveness of the proposed method is demonstrated with trajectory tracking simulations and pool-experiments of AUV.

The depth tracking issue of underactuated autonomous underwater vehicle (AUV) in vertical plane is addressed in this paper. Considering the complicated dynamics and kinematics model for underactuated AUV, a more simplified model is obtained based on assumptions. Then a nonlinear disturbance observer (NDO) is presented to estimate the external disturbance acting on AUV, and an adaptive terminal sliding mode control (ATSMC) based on NDO is applied to enhance the depth tracking performance of underactuated AUV considering both internal and external disturbance. Compared with the traditional sliding mode controller, the static error and chattering problem of the depth tracking process have been clearly improved by adopting NDO-based ATSMC. The stability of control system is proven to be guaranteed according to Lyapunov theory. In the end, simulation results imply that the proposed controller owns strong robustness and satisfied control effectiveness in comparison with the traditional controller.

The sound speed profile has an important impact on sound propagation and underwater acoustic detection, and the water temperature has the most significant impact on the sound speed, so it is critical to obtain high-precision full-depth temperature profiles. With the rapid development of marine mobile platforms, it is possible to obtain depth-fixed temperature data using surface velocimeter or autonomous underwater vehicles (AUVs). This paper uses the measured thermistor chain data to carry out numerical simulation, and discusses the feasibility of reconstructed full-depth temperature profiles using measured temperature of few discrete depths. The back propagation (BP) neural network is used to generate the nonlinear mapping relationship between the temperature in a few discrete depths and the first two empirical orthogonal function (EOF) coefficients. The experimental results show that the temperature at two specially selected depths can reflect the full-depth temperature profiles to a certain extent. However, the information about the water temperature at different depths is diverse and the thermocline contains the most information. As the depth-fixed data measured by the AUV increases, the inversion accuracy of the full-depth temperature profiles increases accordingly. Results shows that, even in ocean regions that have solitary internal waves, when the depth of the depth-fixed data is selected the same as the depth of the surface layer and the two extreme points of the second EOF, the root mean square error (RMSE) of almost all reconstructed temperature profiles in the test set is less than 0.2°C, and the mean RMSE is about 0.12°C.

In the marine environment, the motion characteristics of Autonomous Underwater Vehicles (AUVs) are influenced by unknown factors such as time-varying ocean currents, thereby amplifying the complexity involved in the design of path-following controllers. In this study, a backstepping sliding mode control method based on a current observer and nonlinear disturbance observer (NDO) has been developed, addressing the 3D path-following issue for AUVs operating in the ocean environment. Accounting for uncertainties like variable ocean currents, this research establishes the AUV’s kinematics and dynamics models and formulates the tracking error within the Frenet–Serret coordinate system. The kinematic controller is designed through the line-of-sight method and the backstepping method, and the dynamic controller is developed using the nonlinear disturbance observer and the integral sliding mode control method. Furthermore, an ocean current observer is developed for the real-time estimation of current velocities, thereby mitigating the effects of ocean currents on navigational performance. Theoretical analysis confirms the system’s asymptotic stability, while numerical simulation attests to the proposed method’s efficacy and robustness in 3D path following.

Hydrobatic autonomous underwater vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle avoidance, inspections, docking, and under‐ice operations. However, such AUVs are underactuated systems—this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of model predictive control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear model predictive control (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in robot operating system, a linear time varying MPC (LTV‐MPC) is derived from the nonlinear model to enable real‐time control. In simulations, NMPC and LTV‐MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and linear quadratic regulator based controllers in terms of rise‐time, overshoot, steady‐state error, and robustness. The LTV‐MPC shows satisfactory real‐time performance in experimental validation. The paper further also demonstrates experimentally that LTV‐MPC can be run real‐time on the AUV in performing hydrobatic maneouvers.

Autonomous underwater vehicles (AUVs) are primarily capable of withstanding prolonged periods in unstructured marine environments. These are subject to several uncertainties, including the effects of underwater currents or drift, topography, water pressure, and other factors. The inherent motion of the vehicle is influenced by buoyancy or mass error and other slowly varying hydrodynamic coefficients. Moreover, an AUV faces various types of undetected faults during its mission execution. The use of large-dimensional, rigorous onboard sensor data from real-time AUV operations, subject to the limited power capacity and the presence of other acoustic issues, makes it more challenging for the control community from both stability and positioning accuracy perspectives. Hence, an anti-disturbance stabilized control mechanism is intended to perform a variety of underwater tasks in various uncertain oceanic environments. A two-stage high-degree cubature information filter (TSHDCIF) is designed to effectively observe unmatched external disturbances, uncertain hydrodynamic parameters, and actuator faultiness. A nonlinear model predictive control (NMPC) scheme incorporated with the TSHDCIF observer is proposed in this work. Various realistic scenario-based result sets, including 1) effects on ocean currents, 2) variations in hydrodynamic parameters, and 3) loss of actuator effectiveness, are addressed in this study. The control solution is compared with the baseline NMPC scheme to examine its efficacy, including depth accuracy, control aggression, convergence analysis, and computation time per iteration. Based on the analysis, the TSHDCIF-NMPC scheme outperforms the traditional NMPC scheme by 5.49% in terms of RMSE (root mean square error) and 1.73% in terms of mean square deviation (MSD). The stability of the closed-loop system is explored using the Lyapunov theorem. Thus, it is recommended to use the TSHDCIF-NMPC scheme for systems such as AUV for better efficacy, as mentioned above.

In order to track the desired depth of underactuated autonomous underwater vehicle (AUV), a back-stepping controller based on nonlinear disturbance observer(NDO) is proposed. Firstly, the dynamics model of AUV in the vertical plane is simplified to be more practical for the construction of controller; secondly, the back-stepping controller of AUV's depth is designed along with NDO. Based on the Lyapunov's principle, the overall stability of the whole system is proved to be guaranteed. In the end, the results of the simulation indicate that the designed controller shows strong robustness to the external disturbance, and achieves satisfactory control performance.

An efficient position based visual sevroing control approach for Autonomous Underwater Vehicles (AUVs) by employing Non-linear Model Predictive Control (N-MPC) is designed and presented in this work. In the proposed scheme, a mechanism is incorporated within the vision-based controller that determines when the Visual Tracking Algorithm (VTA) should be activated and new control inputs should be calculated. More specifically, the control loop does not close periodically, i.e., between two consecutive activations (triggering instants), the control inputs calculated by the N-MPC at the previous triggering time instant are applied to the underwater robot in an open-loop mode. This results in a significantly smaller number of requested measurements from the vision tracking algorithm, as well as less frequent computations of the non-linear predictive control law. This results in a reduction in processing time as well as energy consumption and, therefore, increases the accuracy and autonomy of the Autonomous Underwater Vehicle. The latter is of paramount importance for persistent underwater inspection tasks. Moreover, the Field of View constraints (FoV), control input saturation, the kinematic limitations due to the underactuated degree of freedom in sway direction, and the effect of the model uncertainties as well as external disturbances have been considered during the control design. In addition, the stability and convergence of the closed-loop system has been guaranteed analytically. Finally, the efficiency and performance of the proposed vision-based control framework is demonstrated through a comparative real-time experimental study while using a small underwater vehicle.

Designing a backstepping sliding mode controller for an assistant human knee exoskeleton based on nonlinear disturbance observer