Unknown Disturbances Research Articles

Fault‐tolerant control of unmanned marine vehicles with unknown parametric dynamics via integral sliding mode output feedback control

AbstractThis paper focuses on fault‐tolerant control (FTC) for unmanned marine vehicles (UMVs) subject to unknown parametric dynamics, thruster faults, and external disturbances. A novel FTC strategy based on the integral sliding mode output feedback method is proposed. Based on a high gain compensator and output information, a novel integral sliding mode fault‐tolerant controller is constructed to guarantee the dynamic positioning (DP) of UMVs. An attraction region that is related to the known positive constants with respect to dynamic uncertainties has been revealed for the first time. Finally, the closed‐loop stability can be guaranteed from the every initial time in despite of unknown parametric dynamics, thruster faults, and external disturbances. Simulation though a typical floating production ship model has verified the effectiveness of the proposed method.

A variable-barrier-function-based prescribed finite-time bounded-[formula omitted] reinforcement learning optimal tracking control strategy without dependence on initial condition

The prescribed finite-time bounded-H∞ optimal tracking control (OTC) problem is investigated for a class of nonlinear systems with unknown initial tracking condition and external disturbances in this paper. A novel variable barrier function is designed to develop a new approach of prescribed performance control (PPC) which is irrelevant to the initial condition of the constrained variable in the system. Meanwhile, the reinforcement learning (RL) method with the actor–critic architecture is used to optimize the control input of the system in order to obtain the least energy consumption. By means of a newly proposed prescribed finite-time performance function (PFTPF), a prescribed finite-time optimal tracking controller is obtained regardless of initial tracking condition of the system. At the same time, the bounded-H∞ control problem is considered for the external disturbances in the system. The designed controller not only ensures the boundedness of all signals in the closed-loop system, but also has a finite-time control performance and an H∞ anti-interference performance. Finally, the effectiveness and the superiority of this proposed method are verified by the simulations.

Event-Triggered Neural Adaptive Distributed Cooperative Control for the Multi-Tug Towing of Unactuated Offshore Platform with Uncertainties and Unknown Disturbances

An event-triggered neural adaptive cooperative control is proposed for the towing system (TS) with model parameter uncertainties and unknown disturbances. Different from ordinary multi-vessel formation control, the tugs and unactuated offshore platform in the TS are connected together by towlines, and the resultant tension of the towlines serves as the actual drag force for the platform. Initially, based on the radial basis function neural network (RBFNN), an adaptive RBFNN is designed to compensate unknown disturbances and model parameter uncertainties of the TS, and we use minimal learning parameter (MLP) algorithm to reduce the online learning parameters of adaptive RBFNN. Combined with dynamic surface technology and event-triggered control (ETC) mechanism, an event-triggered neural adaptive virtual controller is designed to obtain the desired drag force of the platform. According to the quadratic programming algorithm, the desired drag force is allocated as the desired tensions of towlines. Subsequently, the desired towline length and the desired position information of the tugs are obtained sequentially through the towline model and the position relationship between the tugs and the platform. Then, according to the desired positions of tugs, an event-triggered neural adaptive distributed cooperative controller is designed for achieving the multi-tug towing of the offshore platform. The ETC mechanism is introduced to reduce the communication burden within the TS and the execution frequency of the tugs’ thrusters. Finally, the stability of the closed-loop system is proven using the Lyapunov theory, and the ETC mechanism proves that no Zeno behavior occurs. The effectiveness of the ETC mechanism and the MLP-based adaptive RBFNN on the controllers of TS is verified through simulations and comparison analysis.

Finite‐time consensus for variable‐order fractional non‐linear multi‐agent systems under actuator faults and external disturbances

AbstractThe present investigation aims to address the leader‐following consensus issue for variable‐order fractional multi‐agent systems (VOFMASs) under actuator faults and unknown external disturbances. The Caputo definition for the variable‐order fractional (VOF) derivative is used to model the non‐linear dynamics of the leader and follower agents. Consequently, two lemmas are developed for the Caputo VOF derivative of the Lyapunov function. In the first case, it is assumed that the multi‐agent system (MAS) operates without actuator faults and an adaptive controller is proposed. With the aid of the developed lemmas, assurance is provided for the finite‐time bounded cooperative tracking of the VOFMAS despite the presence of unknown external disturbances. In the second case, a novel fault‐tolerant controller is designed for the finite‐time consensus of the MAS under two common kinds of actuator faults: loss of effectiveness fault and bias fault. Finally, the efficacy of the proposed controller is demonstrated through the presentation of results from three simulation examples.

High-dynamic steering control of the pump-controlled electro-hydraulic steering system for heavy vehicles with active disturbance suppression

Low-frequency response, strong nonlinearity and unknown disturbances are the main challenges faced by the engineering application of the pump-controlled electro-hydraulic steering systems (EHSSs) for heavy vehicles. To solve the above problems, an active disturbance suppression controller is proposed based on a variable rate reaching law (VRRL) and a terminal sliding mode observer (TSMO) in the framework of the sliding mode control. This controller is unique because, the VRRL introduces a nonlinear activation function, which enables smooth switching between multiple reaching rates, effectively improving the response speed and the disturbance rejection performance of the pump-controlled EHSS. Furthermore, an adaptive incomplete disturbance compensation control strategy is proposed by incorporating a TSMO to further improve the accuracy and robustness of the steering system, which employs a VRRL-based TSMO to realize accurate estimation of the lumped disturbance in finite time, and avoids controller overcompensation by selecting a preferred disturbance compensation coefficient. The stability analysis demonstrates that the proposed controller effectively suppresses the lumped disturbance and reduces the convergence domain of the steering system. A pump-controlled EHSS experimental bench is constructed, and a series of experiments are conducted with various steering frequencies and load conditions, which validate the ability of the proposed controller to achieve high-precision dynamic steering in all-terrain conditions of the pump-controlled EHSS for heavy vehicles.

Distributed finite-time attack detection and estimation of intelligent connected vehicle platoon

ABSTRACT This paper presents a distributed finite-time observer design approach for the problem of intelligent connected vehicles (ICVs) containing both attack signals and unknown disturbances. Firstly, an analysis is conducted on the two types of network attacks that ICVs may encounter, followed by the construction of a dynamic vehicle model and its transformation into a state space equation. Secondly, an intermediate variable is introduced to design a distributed attack estimation observer, which both ensures accurate estimation of the vehicle safety state and attack signal within a limited time and overcomes the constraints of traditional observers. Additionally, sufficient conditions for the finite-time boundedness of the observer error system with H ∞ performance are provided in the form of linear matrix inequalities. Furthermore, an adaptive threshold attack detection method is designed. Finally, the simulation results validate the efficiency of the design methods.

A novel fractional-order boundary layer fast terminal sliding mode controller for permanent magnet linear synchronous motor

This paper presents a novel fractional-order boundary layer fast terminal sliding mode (FBLFTSM) control method for high-precision tracking tasks of the permanent magnet linear synchronous motor (PMLSM). Specifically, a dynamic model of PMLSM with lumped uncertainty is established by considering the tracking task involving parameter variations, disturbance load, etc. Then, based on the dynamic model, a FBLFTSM control law is designed to guarantee higher tracking accuracy of the surface motion than the classical terminal sliding mode control even if the system suffers from unknown disturbance. Meanwhile, the fractional-order boundary layer control has the feature of “large error turns into large gain, small error turns into small gain,” which solves the contradiction between weak chattering and fast convergence in the integer-order boundary layer control and improves the dynamic performance of the system. Finally, the effectiveness of the control approach is verified by conducting tracking experiments on the cSPACE-based motor platform.

An adaptive approximation‐free control for leader‐follower tracking of multi‐agent systems with unknown nonlinearity and disturbance

AbstractDue to variable and complex work environments, nonlinearities, uncertainty and disturbances are inevitable in multi‐agent systems. Approximation‐free control can address these issues without involving approximators, such as fuzzy logic systems and neural networks. However, some issues like the singularity problem caused by the signals exceeding the preset boundary in changing work conditions still remain. This paper proposes an adaptive and reliable approximation‐free control, which comprises a novel singularity compensator and a modified transforming function. The proposed control scheme performs better in terms of convergence rate and overshoot, avoids issues relating to singularity, and has added flexibility in terms of parameter choice. The proposed control law adapts to changes in operating conditions and nonlinearities—the efficacy of which is demonstrated using simulations.

Fixed-time formation tracking for unmanned surface vehicles: A multi-layer neural networks approach

This paper investigates the distributed fixed-time formation problem of unmanned surface vehicles (USVs) in the presence of external disturbances, model uncertainties, input saturation and quantization constraints. To deal with the problem, a fixed-time sliding-mode control algorithm is proposed, where multi-layer neural networks (MNNs) are designed to approximate the unknown dynamics and composite disturbances of the system. The proposed MNNs combine the advantages of fuzzy neural networks (FNNs) and radial basis function neural networks (RBFNNs), exhibiting robust dynamic characteristics. Furthermore, the non-singular fast terminal sliding mode (NFTSM) is integrated into the fixed-time control framework to improve the robustness and speed of convergence for uncertain USV systems. Comparative simulations conducted with USVs demonstrate the superiority and effectiveness of the proposed algorithm.

Backstepping based intelligent control of tractor-trailer mobile manipulators with wheel slip consideration

In this research, a new hybrid backstepping control strategy based on a neural network is proposed for tractor-trailer mobile manipulators in the presence of unknown wheel slippage and disturbances. To minimize the negative impacts of wheel slippage, the desired velocities of the tractor’s wheels are computed with a proposed kinematic control model with an adaptive term. As the system’s dynamical model contains unavoidable uncertainties, model-based backstepping control technique is unable to effectively manage these systems. Hence, the proposed controller blends a radial basis function neural network with the merits of a dynamical model-based backstepping approach. The neural networks are employed to approximate the non-linear unknown smooth function. To minimize the impact of external disturbances, and network reconstruction error an adaptive term is added to the control law. The Lyapunov theorem and Barbalat’s lemma are employed to guarantee the stability of the control method. The tracking error is shown to be bounded and to rapidly converge to zero with the proposed method. To demonstrate the efficacy and validity of the control mechanism, comparison simulation results are presented.

Trajectory Tracking Control of an Autonomous Vessel in the Presence of Unknown Dynamics and Disturbances

We present a proportional–integral–derivative-based controller plus an adaptive slide surface to solve the trajectory tracking control problem for a fully actuated vessel with unknown parameters perturbed by slowly varying external unknown dynamics. The controller design assumes that the vessel moves at low speed and frequency, its physical parameters are unknown, and its state is measurable. The control approach ensures error tracking convergence toward a small vicinity at the origin. We conduct the corresponding stability analysis using the Lyapunov theory and saturation functions. We tested the controller through two numerical experiments—a turning ellipse maneuver and a rest-to-rest maneuver—where the vessel parameters were unknown, and we obtained satisfactory results.

Neuroadaptive‐based fixed‐time control for robotic manipulators with uniform prescribed performance under unknown disturbance

AbstractAchieving faster convergence, smaller transient overshoots, and higher steady‐state tracking accuracy is essential to improve the efficiency, robustness, and applicability of robotic manipulators. This article introduces an innovative adaptive fixed‐time uniform prescribed performance controller for the manipulator facing model uncertainties and unknown disturbances. Initially, by designing a modified prescribed performance function inspired by variable superposition, this study redefines the unified prescribed performance control problem into a simplified parameter selection problem. This approach allows for the incorporation of varied performance metrics within a singular control scheme, addressing both transient and steady‐state performances concurrently without shifting control frameworks. Then, to alleviate computational demands, an adaptive neural network employing a single‐parameter weight update technique compensates for uncertainties of the manipulator dynamic model. Additionally, a disturbance observer is designed to mitigate the impact of non‐parametric disturbances. Moreover, integrating fixed‐time theory with the Lyapunov stability analysis method guarantees the convergence of all error signals to a near‐zero compact neighborhood at a fixed time. Finally, the advantages and comprehensive performance of the proposed method are confirmed by numerical simulations and real‐world experiments.

Concurrent event‐triggered adaptive neural control for MASS under cross‐water scenarios

AbstractThis article discusses the control problem of marine autonomous surface ships (MASS) under cross‐water scenarios, that is, from open water to restricted water, where several practical facts, such as uncertain dynamic, unknown disturbance and actuator wear suppression, are taken into account. To resolve such a control design challenge, the predefined performance control (PPC)‐based and barrier Lyapunov function (BLF)‐based ideas are employed, and a prespecified performance function (PPF) is designed to implement the transformation of cross‐water design. Under the adaptive backstepping design framework, with aid of PPC‐based and BLF‐based design ideas, an adaptive neural control solution is developed for MASS under cross‐water scenarios. Furthermore, to reduce the actuator wear and tear caused by high‐frequency corresponding control commands and hull vibration, a new multichannel concurrent event‐triggered protocol (ETP) is constructed in the controller‐actuator (C‐A) channel. Finally, a concurrent event‐triggered adaptive neural control scheme is proposed for MASS under cross‐water scenarios. The theoretical analysis indicates that all signals in the control system are ultimately bounded, and the Zeno behavior is avoided. The simulation and comparison results verify the effectiveness and superiority of the developed control scheme.

Distributed Estimator-Based Containment Control for Multi-AUV Systems Subject to Input Saturation and Unknown Disturbance

This article addresses the containment control issue for multi-AUV systems with the intervention of both external disturbance and input saturation. Firstly, a distributed estimator is established for the sake of acquiring precise estimation information of the desired position and its derivative for each follower AUV in the system. Next, on the basis of the proposed distributed estimator, a virtual control law is designed for each follower AUV. Then, due to the difficulty in obtaining accurate information about the derivative of the virtual control law, a linear tracking differentiator is introduced. Additionally, a disturbance observer is employed to tackle the composite disturbance, which mainly contains the internal model uncertainties and external bounded disturbances. Meanwhile, the issue of input saturation is handled by constructing the auxiliary system. Furthermore, a containment control law is designed with the assistance of the introduced linear tracking differentiator, the established disturbance observer, and the constructed auxiliary system. Additionally, the Lyapunov stability theory is applied to analyze the stability of the multi-AUV system. Finally, simulation results are given to confirm the feasibility of the proposed containment control scheme.

Adaptive disturbance observer-based fast nonsingular terminal sliding mode control for quadrotors

In this paper, a sliding-mode disturbance observer (SMDO)-based finite-time control scheme is proposed for a quadrotor under uncertainties and external disturbances. The SMDO is used to estimate and compensate the unknown time-varying disturbance with minor chattering and rapid convergence. However, a priori bounded uncertainty may impose a priori constraint on the system state before obtaining closed-loop stability. To address this issue, an adaptive SMDO (ASMDO) with a nested adaptive structure is further introduced. As for controller design, a fast nonsingular integral terminal sliding mode controller (FNITSMC) is proposed to ensure finite-time convergence and zero tracking error for the quadrotor attitude control. Finally, effectiveness of proposed method is verified through simulations and experiments.

Disturbance Observer-Based Adaptive Fault Tolerant Control with Prescribed Performance of a Continuum Robot

This paper studies an adaptive fault tolerant control (AFTC) scheme for a continuum robot subjected to unknown actuator faults, dynamics uncertainties, unknown disturbances, and prescribed performance. Specifically, to deal with uncertainties, a function approximation technique (FAT) is employed to evaluate the unknown actuator faults and uncertain dynamics of the continuum robot. Then, a nonlinear disturbance observer (DO) is developed to estimate the unknown compounded disturbance, which contains the unknown disturbances and approximation errors of the FAT. Furthermore, the prescribed error bound is treated as a time-varying constraint, and the controller design method is based on an asymmetric barrier Lyapunov function (BLF), which is operated to strictly ensure the steady-state and transient performance of the continuum robot. Afterwards, the simulation results validate the effectiveness of the proposed AFTC in dealing with the unknown actuator faults, uncertainties, unknown disturbances, and prescribed performance. Finally, the effectiveness of the proposed AFTC scheme is verified through experiments.

Disturbance observer‐based matrix‐weighted consensus

AbstractIn this paper, we proposed several disturbance observer‐based matrix‐weighted consensus algorithms. A new disturbance observer is firstly designed for linear systems with unknown matched or mismatched disturbances representable as the multiplication of a known time‐varying matrix with a unknown constant vector. Under some assumptions on the boundedness and persistent excitation of the regression matrix, the disturbances can be estimated at an exponential rate. Then, a suitable compensation input is provided to compensate the unknown disturbances. Second, disturbance‐observer based consensus algorithms are proposed for matrix‐weighted networks of single‐ and double‐integrators with matched or mismatched disturbances. We show that both matched and mismatched disturbances can be estimated and actively compensated, and the consensus system uniformly globally asymptotically converges to a fixed point in the kernel of the matrix‐weighted Laplacian. Depending on the network connectivity, the system can asymptotically achieve a consensus or a cluster configuration. The disturbance‐observer based consensus design is further extended for a network of higher‐order integrators subjected to disturbances. Finally, simulation results are provided to support the mathematical analysis.

Attitude Control of a Mass-Actuated Fixed-Wing UAV Based on Adaptive Global Fast Terminal Sliding Mode Control

Compared with traditional control methods, moving mass control (MMC) enhances the aerodynamic efficiency and stealth performance of fixed-wing unmanned aerial vehicles (FWUAVs), thereby facilitating their broader application in military and civilian fields. Nevertheless, this approach increases system complexity, nonlinearity, and coupling characteristics. To address these challenges, a novel attitude controller is proposed using adaptive global fast terminal sliding mode (GFTSM) control. Firstly, a dynamic model is established based on aerodynamics, flight dynamics, and moving mass dynamics. Secondly, to improve transient and steady-state responses, prescribed performance control (PPC) is adopted, which enhances the controller’s adaptability for mass-actuated aircraft. Thirdly, a fixed-time extended state observer (FTESO) is utilized to solve the inertial coupling issue caused by mass block movement. Additionally, the performance of the entire control system is rigorously proven through the Lyapunov function. Finally, numerical simulations of the proposed controller are compared with those of PID and linear ADRC in three different conditions: ideal conditions, fixed aerodynamic parameters, and nonlinear aerodynamic parameter changes. The results indicate that the controller effectively compensates for the system’s uncertainty and unknown disturbances, ensuring rapid and accurate tracking of the desired commands.

Prescribed-time tracking control of Mars entry vehicle with output constraints

This paper studies the prescribed-time output-constrained tracking control problems for Mars entry vehicle systems. By using the properties of the time-varying high-gain function and output-constrained conditions, time-varying barrier Lyapunov functions are constructed. Based on such Lyapunov functions, time-varying adaptive controllers are designed to solve the considered problem. It is proved that the proposed controllers can drive the errors converge to zero in a prescribed time under unknown disturbances, and the control signals are bounded. Both the symmetric and asymmetric cases are considered. Finally, the effectiveness of the proposed methods is illustrated by several numerical simulations.

Lyapunov-based model predictive control for trajectory tracking of hovercraft with actuator constraints and external disturbances

In this study, a Lyapunov-based model predictive control (LMPC) method is developed to address the trajectory tracking problem of autonomous hovercrafts subjected to unknown complex disturbances from ocean environments and practical constraints of marine surface vessels, such as actuator increment limitations and saturation. Initially, the novel LMPC algorithm is applied to enhance tracking performance through rolling optimization and online optimization. Subsequently, a nonlinear disturbance observer is designed to estimate external disturbances caused by wind and dynamics uncertainties. Furthermore, to theoretically ensure control stability, contraction constraints are established using the nonlinear backstepping control method. This approach provides the essential conditions for the system's robustness and stability. Finally, the superiority and robustness of the designed LMPC trajectory tracking method are demonstrated through numerical simulations conducted in a marine environment.