Unknown Environmental Disturbances Research Articles

Adaptive path following control for underactuated surface vessels considering actuator saturation dynamics

ABSTRACT This article studies the path-following problem of underactuated surface vessels with saturated actuator dynamics and unmeasured linear velocities subjected to model uncertainties and unknown environmental disturbances. Firstly, a predictor is designed to estimate linear velocities and an improved predictor-based line-of-sight guidance law is designed to generate a reference heading angle, where the unknown arbitrary sideslip angle is compensated. Secondly, robust adaptive control laws are devised, where auxiliary dynamic systems are introduced to address input saturation and adaptive technique is employed to offset environmental disturbances and model uncertainties. Furthermore, to tackle computational complexity in control design, tracking differentiators are employed. Subsequently, the stability analysis of a closed-loop system is demonstrated by the Lyapunov theory. At last, simulations are applied to verify the validity and superiority of the presented strategy.

Prescribed-time active fault-tolerant control of dynamic positioning vessels based on improved iterative learning observer

Considering the dynamic positioning (DP) vessel control system with actuator faults and unknown environment disturbances, a fault-tolerant control scheme based on improved iterative learning observer (ILO) is proposed in this article. Firstly, the kinematics and dynamics models of DP vessels are established containing the actuator faults and environmental disturbances. Secondly, an improved ILO is designed to handle the unknown faults and disturbances concurrently with higher estimation precision by introducing the state information of the previous moment. Further, a prescribed-time active fault-tolerant control scheme is presented on the basis of the improved ILO and prescribed-time stability. And the stability of proposed ILO and DP fault-tolerant control system are analyzed based on Lyapunov theory. Finally, the effectiveness and advantages of the presented ILO and active fault-tolerant control scheme are illustrated by numerical simulations and comparisons.

Disturbance-Observer-Based Adaptive Prescribed Performance Formation Tracking Control for Multiple Underactuated Surface Vehicles

This study proposes a new disturbance-observer-based adaptive distributed formation control scheme for multiple underactuated surface vehicles (USVs) subject to unknown synthesized disturbances under prescribed performance constraints. A modified sliding mode differentiator (MSMD) is applied as a nonlinear disturbance observer to estimate unknown synthesized disturbances, which contain unknown environmental disturbances and system modelling uncertainties, thus enhancing the robustness of the system. Based on this, we impose the time-varying performance constraints on the position tracking error between the neighboring USVs. A novel differentiable error transformation equation is embedded in the prescribed performance control, and an adaptive prescribed performance controller is constructed by employing the backstepping method to ensure that the position tracking error remains within the prescribed transient and steady performance, and each USV realizes collision-free formation motion. Furthermore, a novel second-order nonlinear differentiator is introduced to extract the derivative information of the virtual control law. Finally, the numerical simulation results verify the effectiveness of the proposed control scheme.

Predefined-time prescribed performance second-order sliding mode path following control for underactuated marine surface vehicles using self-structuring NN

This paper proposes a predefined time-prescribed performance second-order sliding mode path following controller for underactuated marine surface vehicles (MSVs) with unknown external environmental disturbances based on predefined time predictor line-of-sight (PTPLOS) guidance. First, a predefined time predictor is designed to address the problem of time-varying sideslip angles, and then a guidance law is designed. Second, a prescribed performance function with predefined times is proposed, which can not only set the rate of convergence and the final convergence range but also set an upper bound on the convergence time. Third, the predefined-time controller is designed in combination with second-order sliding mode control. Then, a self-structuring neural network (SSNN) is developed to approximate external disturbances and uncertainties, and this approach can adaptively adjust the number of neurons, reducing the computational burden while maintaining high approximation accuracy. After that, the closed-loop system is proven to be predefined-time stable by using of the Lyapunov stability theory. Finally, the control method proposed in this paper is validated through numerical simulations, demonstrating its effectiveness.

Backstepping-based adaptive control of underactuated AUV subject to unknown dynamics and zero tracking errors

This paper introduces a zero-error tracking control solution for underactuated autonomous underwater vehicle (AUV) facing unknown dynamic and environmental disturbances. The proposed approach integrates the backstepping method with adaptive techniques and neural networks to effectively address the challenges posed by uncertainties and external disturbances, thereby enhancing the robustness and adaptability of AUV. Subsequently, a boundary estimation method is employed to dynamically update a single adaptive parameter online, leading to a significant reduction in computational workload. To achieve superior asymptotic tracking performance, a different coordinate transformation of fusion scalar function is designed. Moreover, a rigorous analysis of the proposed control scheme is conducted using the Lyapunov-based method, establishing that the tracking error converges asymptotically to zero. Simulation experiments are executed to illustrate the efficacy of the proposed control scheme.

Adaptive neural synergetic heading control for USVs with unknown dynamics and disturbances

This paper delves into the challenge of heading control for unmanned surface vehicles (USVs) in conditions with unknown dynamics and environmental disturbances. Adaptive neural synergetic controllers are introduced for the purpose of effectively steering the USV, ensuring precise heading control to track and maintain the desired angle, even in the presence of environmental disturbances such as sea winds and waves. Firstly, different forms of macro variables and evaluation constraints are introduced to design the synergetic controllers. Then, to estimate the unknown dynamics and environmental disturbances, a single input single output RBF neural network is developed. Besides, to ensure tracking at the desired angle, an adaptive neural synergetic law is derived from the Lyapunov stability theorem. Finally, the simulation results compare the control effect of heading controllers with different macro variables and evaluation constraints and verify the effectiveness of the proposed control strategy. The feasibility of the adaptive neural synergetic controller is verified through the USV experiment.

An adaptive internal model control approach for unmanned surface vehicle based on bidirectional long short-term memory neural network: Implementation and field testing

This paper investigates a practical adaptive internal model control (IMC) for an unmanned surface vehicle (USV) with unknown time-varying nonlinear model parameters and environmental disturbances. Firstly, an internal model of the USV is presented using the Bidirectional Long Short-Term Memory (BiLSTM) neural network. Then, an adaptive neural IMC controller is designed for the trajectory tracking of USV by using the IMC method. The internal model and the controller are updated by an error threshold algorithm. The proposed control scheme comprises of a trajectory guidance module via the Line-of-Sight (LOS) guidance method and a tracking control module designed by IMC theory. Under the proposed control scheme, the development processes of the vehicle platform and the control algorithms are described, and accurate tracking control can be achieved. Finally, the results of simulation and field experiments are presented and discussed to validate the effectiveness of the proposed control scheme.

UDE-Based Distributed Formation Control for MSVs With Collision Avoidance and Connectivity Preservation

Limited computational resource is one of features of the embedded system including unmanned marine surface vehicle (MSV). Under this practical constraint, we aim to develop a distributed formation control algorithm for a group of uncertain MSVs, whose computational burden is low. The uncertainty and disturbance estimator (UDE) is employed to compensate for the modeling uncertainties, the unknown environmental disturbances, and the unavailable derivative of the virtual control inputs, such that the formation errors converge asymptotically to zero. Meanwhile, the prescribed performance control technique is applied to ensure that the convergence rates of the formation errors are faster than predefined values. Additionally, collision avoidance and connectivity preservation among the neighboring vehicles are guaranteed, while the obstacle avoidance is also ensured. As no parameter update law is required to calculate, the computational burden is reduced significantly. The effectiveness of the proposed UDE-based formation control algorithm is validated through comparative simulation.

PSO-Based Predictive PID-Backstepping Controller Design for the Course-Keeping of Ships

Ship course-keeping control is of great significance to both navigation efficiency and safety. Nevertheless, the complex navigational conditions, unknown time-varying environmental disturbances, and complex dynamic characteristics of ships pose great difficulties for ship course-keeping. Thus, a PSO-based predictive PID-backstepping (P-PB) controller is proposed in this paper to realize the efficient and rapid course-keeping of ships. The proposed controller takes the ship’s target course, current course, yawing speed, as well as predictive motion parameters into consideration. In the design of the proposed controller, the PID controller is improved by introducing predictive control. Then, the improved controller is combined with a backstepping controller to balance the efficiency and stability of the control. Subsequently, the parameters in the proposed course-keeping controller are optimized by utilizing Particle Swarm Optimization (PSO), which can adaptively adjust the value of parameters in various scenarios, and thus further increase its efficiency. Finally, the improved controller is validated by carrying out simulation tests in various scenarios. The results show that it improves the course-keeping error and time-response specification by 4.19% and 9.71% on average, respectively, which can efficiently achieve the course-keeping of ships under various scenarios.

The Non-Singular Terminal Sliding Mode Control of Underactuated Unmanned Surface Vessels Using Biologically Inspired Neural Network

Underactuated Unmanned Surface Vessels (USVs) are widely used in civil and military fields due to their small size and high flexibility, and trajectory tracking control is a critical research area for underactuated USVs. This paper proposes a trajectory tracking control strategy using the Biologically Inspired Neural Network (BINN) for USVs to improve tracking speed and accuracy. A virtual control law is designed to obtain the required virtual velocity for trajectory tracking control, in which the velocity error is calibrated to ensure that the position error converges to zero. To observe and compensate for unknown and complex environmental disturbances such as wind, waves, and currents, a nonlinear extended state observer (NESO) is designed. Then, a controller based on Non-singular Terminal Sliding Mode (NTSM) is designed to resolve the problems of singular value and controller chattering and to improve the controller response speed. A BINN is introduced to simplify the process of differentiation, reduce the input values of the initial state, and solve the problem of thruster input saturation. Finally, the Lyapunov stability theory is utilized to analyze the stability of the proposed algorithm. The simulation results show that the proposed algorithm has a higher trajectory tracking accuracy and speed than traditional methods.

Cooperative Maritime Search of Multi-Ship Based on Improved Robust Line-of-Sight Guidance

In this paper, an improved robust line-of-sight (RLOS) guidance-based fuzzy sliding mode controller is presented to control underactuated ships to conduct the cooperative maritime search operation under the presented improved creeping line search method. First, considering that the ship cannot perform turning with corners, an improved creeping line search method is presented by integrating the Bezier method into the traditional creeping line search method to smooth the transition points with corners and employing the cubic spline interpolation method to generate continuous reference paths. Second, an improved RLOS guidance method is presented for the first time by exploring the idea of robust adaptive control to mitigate the chattering effect of the RLOS guidance. Third, the fuzzy logic system with approximate ability is integrated into the design of sliding mode controller to handle unknown nonlinear model dynamics and environmental disturbances. Finally, an improved RLOS guidance-based fuzzy sliding mode controller is presented. The closed-loop stability is guaranteed by the Lyapunov theorem. Comparative simulations are conducted to illustrate the advantages and verify the effectiveness of the presented method.

Adaptive dynamic positioning control of an offshore wind turbine installation vessel subjected to thruster dynamics and input constraints

This paper suggests an adaptive positioning control method for a DP-equipped offshore wind turbine installation vessel. The method combines a finite-time disturbance observer (FTDO) with adaptive command filtered backstepping (CFB) to handle issues related to thruster dynamics, input saturation, model parameter uncertainties, and unknown environmental disturbances. By analyzing the environmental loads acting on the pile legs, a mathematical model for the installation vessel is established. To counteract the unknown disturbance actively, a FTDO is utilized. Additionally, an auxiliary dynamic system (ADS) is used to mitigate the thruster input saturation issue. Then an adaptive positioning controller is designed based on the FTDO and ADS, and Lyapunov stability theory is used to prove that all signals in the control system remain uniformly ultimately bounded. Numerical simulations are conducted to verify the effectiveness of the proposed control approaches.

A weather optimal heading-based incremental feedback control for dynamic positioning of ships

In this brief, the weather optimal heading-based incremental feedback control algorithm is built for dynamic positioning (DP) of ships in the presence of model uncertainties and unknown environmental disturbances induced by wind, waves and currents. The proposed control algorithm has two parts, i.e., the weather optimal heading portion and the control portion. An improved ZPC-W approach with the deviation of the lateral control force is designed in order to enhance the convergence rate of the desired weather optimal heading and avoid overshoot as well. In addition, based on the iterative sliding mode function, a concise incremental feedback control scheme is designed with the pitch ratio of the thruster as the control input. It is measurable in the practical plant and has nothing to do with ship models with good stability and strong robustness. Lastly, simulation studies are performed to illustrate the performance and the superiorities of the proposed control algorithm.

Adaptive asymptotic tracking control of autonomous underwater vehicles based on Bernstein polynomial approximation

In this article, an adaptive asymptotic tracking control approach is first proposed for the autonomous underwater vehicle (AUV) on the horizontal plane by using the Bernstein polynomial approximation. Bernstein polynomials aim to compensate for the synthetical uncertainties including unknown model dynamics and environmental disturbances. However, they cannot be directly used due to the unknown polynomial coefficients. To avoid this trouble, the adaptive backstepping technique is applied to estimation. Considering that the adaptive backstepping can only procure the bounded steady tracking errors and suffer from the redundancy of adaptive parameters, we further integrate the σ-modification and the minimum learning parameter (MLP) technique with the Bernstein polynomial approximation, so as to guarantee the high-precision tracking performance of AUVs with the asymptotic convergent tracking errors and less computation burden. The direct Lyapunov’s method and the Barbalat’s lemma are introduced for proving the stability of the control system. Finally, simulation reveals the advantage of the proposed scheme.

Energy-based trajectory tracking control of under-actuated unmanned surface vessels

In this paper, a novel energy-based trajectory tracking control strategy for under-actuated unmanned surface vessels (USVs) in the presence of unknown environmental disturbances is presented. The port-Hamiltonian framework is utilized to propose a passivity-based control model in body-fixed coordinates of the USVs. An adaptive disturbance estimation method is detailed and used to accurately estimate the environmental disturbances affecting USV motion. Furthermore, a passive and Hamiltonian structure-preserving controller is employed to achieve the desired trajectory of the USV system, and the stability of the desired target dynamic system is rigorously proven. The effectiveness of the proposed controller is demonstrated through simulations and experiments on a USV experimental platform, showcasing its capability of trajectory tracking performance and mitigating the effects of disturbances.

A Deep Reinforcement Learning-Based Path-Following Control Scheme for an Uncertain Under-Actuated Autonomous Marine Vehicle

In this article, a deep reinforcement learning-based path-following control scheme is established for an under-actuated autonomous marine vehicle (AMV) in the presence of model uncertainties and unknown marine environment disturbances is presented. By virtue of light-of-sight guidance, a surge-heading joint guidance method is developed within the kinematic level, thereby enabling the AMV to follow the desired path accurately. Within the dynamic level, model uncertainties and time-varying environment disturbances are taken into account, and the reinforcement learning control method using the twin-delay deep deterministic policy gradient (TD3) is developed for the under-actuated vehicle, where path-following actions are generated via the state space and hybrid rewards. Additionally, actor-critic networks are developed using the long-short time memory (LSTM) network, and the vehicle can successfully make a decision by the aid of historical states, thus enhancing the convergence rate of dynamic controllers. Simulation results and comprehensive comparisons on a prototype AMV demonstrate the remarkable effectiveness and superiority of the proposed LSTM-TD3-based path-following control scheme.

Distributed Event-Triggered Fixed-Time Leader–Follower Formation Tracking Control of Multiple Underwater Vehicles Based on an Adaptive Fixed-Time Observer

This paper focuses on the fixed-time leader–follower formation control of multiple underwater vehicles (MUVs) in the presence of external disturbances. First, an adaptive fixed-time disturbance observer (AFxDO) is developed to deal with unknown time-varying environmental disturbances. The developed AFxDO guarantees the fixed-time convergence property of the disturbance observation error and no prior information on the external disturbances or their derivatives is required. Then, with the aid of the developed AFxDO, a distributed event-triggered fixed-time backstepping controller was developed to achieve the leader–follower formation tracking control of MUVs. To solve the “explosion of complexity” problem inherent in the conventional backstepping, a nonlinear filter is introduced to obtain the derivative of the virtual control law. Furthermore, to reduce the communication burden, the event-triggered mechanism is integrated into the formation tracking controller. The stability analysis shows that the closed-loop MUV system is practical fixed-time stable. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

Adaptive Finite-Time Trajectory Tracking Control for Coaxial HAUVs Facing Uncertainties and Unknown Environmental Disturbances

In this paper, the problems of system design, dynamic modeling, and trajectory tracking control of coaxial hybrid aerial underwater vehicles (HAUVs) with time-varying model parameters and composite marine environment disturbances are investigated. It is clear that a stable transition between different media remains a challenge in the practical implementation of amphibious tasks. For HAUVs, accurate dynamic modeling to describe complex dynamic variations during vehicle takeoff from underwater to air is a huge challenge. Meanwhile, due to the rapid changes in model parameters and the external environment, vehicles are likely to fall into the sea during the cross-domain process. An integrated continuous dynamic model considering hydrodynamic changes is established by introducing a linear switching coefficient during the process of trans-medium motion. A nonsingular fast terminal sliding-mode control (NFTSMC) algorithm combined with adaptive technology is used to design the position and attitude of the controller. With no previous knowledge of external interferences and lumped uncertainties of the HAUV, the adaptive NFTSMC (ANFTSMC) algorithm achieves the control objectives; at the same time, the inherent chattering problems of sliding mode control (SMC) are weakened. The finite-time stability of the global system is proven strictly using a series of mathematical derivations based on Lyapunov theory. The effect of the controller applied is analyzed through a series of simulations with representative working conditions. The results show that the proposed ANFTSMC can realize a “seamless” air–water trans-medium process, which proves the superiority and robustness of the proposed control algorithm.

Adaptive backstepping fast terminal sliding mode control of dynamic positioning ships with uncertainty and unknown disturbances

Considering the complexity of the operating environment of marine ships, higher demands are placed on the positioning accuracy and positioning performance of Dynamic Positioning (DP) systems. This paper designs an Adaptive Backstepping Fast Terminal Sliding Mode Control (ABFTSMC) to solve the problems of low positioning accuracy, slow convergence, and poor anti-interference performance in ship positioning due to uncertainty of dynamic model parameters and unknown time-varying environmental disturbances. A Fast Terminal Sliding Mode Control (FTSMC) is combined with backstepping techniques to ensure the robustness of the system to uncertainties and disturbances. Meanwhile, an adaptive law is used to estimate the unknown uncertainty terms. Moreover, the Lyapunov stability criterion is used to prove the convergence and stability of the designed controller. Multiple simulation results show that ABFTSMC has better control performance compared with Adaptive Backstepping Control (ABC) and Adaptive Backstepping Sliding Mode Control (ABSMC). All the comparisons could fully verify the effectiveness and superiority of the proposed control method.

Attitude tracking backstepping control for small unmanned helicopters based on adaptive neural network under unknown environment disturbances

To solve the attitude tracking control problem of small unmanned helicopters under unknown bounded disturbances, an attitude tracking backstepping controller based on adaptive radial basis function (RBF) neural network disturbance compensation is proposed in this paper. The unknown disturbance is estimated online and in real time through RBF neural network with a novel gradient descent weight update rate. A novel attitude tracking backstepping controller based on virtual control variables is designed, and the system stability is analyzed using the Lyapunov method. The experimental verification of the backstepping controller is carried out on the three-degrees-of-freedom helicopter. The experiments show the effectiveness and advancedness of the proposed adaptive neural network backstepping controller in suppressing unknown external disturbances compared with the robust adaptive integral backstepping.