This paper investigates the problem of real-time deception attack detection for unmanned aerial vehicle (UAV) under resource-constrained conditions. First, deception attacks are classified into trajectory hijacking attacks and constant bias attacks according to their dynamic characteristics. Then, we develop a physically interpretable two-level XGBoost feature construction that decouples transient anomaly detection from long-term bias identification. Furthermore, an event-triggered activation mechanism is developed to effectively reduce computational burden while maintaining reliable detection performance. The verification results of the test set show that for trajectory hijacking attacks, the detection accuracy of the proposed framework reaches 95.8 percent, and for constant deviation attacks, the detection accuracy of the proposed framework is 98.9 percent. Moreover, through analysis of complexity and cost, it is demonstrated that the proposed method is lightweight and highly efficient. Real-world UAV flight experiments further verify the effectiveness and practicality of the proposed method under real deception attacks, highlighting its suitability for real-time onboard deployment.

This study introduces iProDMP, a novel framework that integrates human behavioral preferences into exoskeleton learning through unified probabilistic movement modeling. The framework addresses three critical limitations: Dynamic movement primitives (DMPs)’ inability to represent preference uncertainty, inadequate trajectory generation beyond observation range in probabilistic movement primitives (ProMPs), and unreliable via-point modulation in hybrid approaches. Our solution features three innovations: First, we establish dynamic-probabilistic consistency conditions for unified DMP-ProMP frameworks, which enable stochastic modeling of human preferences while preserving attractor stability. Second, a novel scaling method decouples shape modulation from model hyperparameters, enabling flexible motion adaptation. Third, consistency-guaranteed expectation-maximization resolves parameter optimization within the unified framework. Experiments on lifting trajectory imitation demonstrate strong extrapolation beyond demonstration distributions, particularly for via-points outside training data, thereby validating adaptability to variable task conditions. In hip exoskeleton-assisted lifting tasks, our approach achieves a 28% improvement in assistance efficiency over conventional implementations.

In this paper, a fixed-time adaptive deferred constrained control strategy is proposed for a flexible single-link manipulator system with input saturation. Fuzzy Neural Networks are utilized to estimate the unknown dynamics of the flexible manipulator system as well as the errors caused by input saturation. To address output constraints imposed within a prescribed time period, a time-shift function and an adjusted barrier function are introduced. The system’s stability is rigorously proven using the direct Lyapunov method. Finally, numerical simulations and experimental results are presented to validate the effectiveness and superiority of the proposed control approach.

This article investigates the model analysis and control design of a nonuniform continuum arm system, addressing the challenges posed by its inherent nonlinearity, coupled dynamics, and large deformations. A distributed control strategy is proposed, leveraging in-domain static forces and velocity feedback to achieve controlled transitions between static variable curvature configurations. The developed model accounts for variable curvature bending configurations and provides interpretable actuation mechanisms based on cable-driven control torques. A Lyapunov-based stability analysis demonstrates the asymptotic stability of the system under the proposed control scheme. Numerical simulations, including a target capture scenario in narrow spaces, illustrate how the continuum arm can transition smoothly between different static shapes while maintaining stability. The results highlight the potential of the proposed approach for practical applications that require reliable configuration changes in confined or task-specific environments.

This study focuses on the reinforcement learning (RL)-based consensus tracking control of nonlinear multiagent robot systems (MARSs) with event triggering mechanism. Each agent of the MARSs is composed of a three-link rigid robot and a flexible payload, which can be assumed to be a Eulbernoulli beam. Based on the assumed mode method (AMM), the infinite distributed parameter model of the robot–payload system is approximated as a finite dimension model, and the dynamic performance of the robot system is controlled with the use of boundary control input. First, a RL control strategy based on actor–critic structure is adopted to maintain the consensus angles tracking of all agents while suppress the load vibration. Second, considering the communication bandwidth problem in practical applications, an event-triggered mechanism is utilized to reduce the transmission burden based on relative threshold strategy. Furthermore, the semi-global uniformly ultimately bounded (SGUUB) property of the closed-loop system is derived to guarantee the state errors can converge to the small neighborhoods of the origin. Finally, the effectiveness of the proposed control strategy is demonstrated by numerical simulations.

This article addresses the leader–follower consensus problem for a class of nonlinear multiagent systems (MASs) whose collective behavior is modeled by a diffusion partial differential equation (PDE). Existing control strategies for such systems often suffer from high communication overhead and a lack of robustness to unknown nonlinearities and disturbances. To overcome these limitations, we introduce a novel adaptive control scheme that integrates a dynamic event-triggered mechanism with a radial basis function neural network (RBFNN) approximator. The dynamic event trigger scheme significantly reduces communication burdens by aperiodically updating the control signal only at specific moments, while the RBFNN is employed to effectively compensate for the unknown boundary function and unmodeled disturbances. We provide a rigorous Lyapunov-based stability analysis to prove that the proposed controller guarantees stability of the closed-loop system. Numerical simulations demonstrate the efficacy of the proposed method, showing a substantial reduction in communication frequency while ensuring precise consensus tracking.

A neuro adaptive control for deploying a partial differential equation-based multi-agent system in 3D space with a non-collocated observer is proposed in this study. Since the full states of the system are unavailable in practice, an observer-based control is developed to ensure stability of the underlying closed-loop system. In addition, the system uncertainty is addressed by introducing a neural network control. By choosing appropriate system parameters, the desired control objectives can be achieved. The proposed strategy is simple to implement and its implementation condition is easily satisfied. Finally, the effectiveness of the designed method is verified by the simulation results. Note to Practitioners— In this paper, we introduce a neuro-adaptive control strategy for a multi-agent system based on partial differential equations in 3D space, using a non-collocated observer. This approach is particularly relevant for practitioners dealing with dynamic and uncertain environments in control systems. Neural networks are employed to manage system uncertainties, adapting to changing conditions, which is crucial in environments with variable system parameters. The non-collocated observer allows for state estimation, beneficial in situations where direct measurement is impractical. Ensuring the observer’s accuracy is key for effective control. Our strategy focuses on simplicity and ease of implementation, making it accessible for integration into existing systems. The observer-based control ensures the stability of the closed-loop system, a critical factor for consistent performance.

This paper investigates the attitude tracking control problem for spacecraft. A tanh-type self-learning control (TSLC) approach with variable learning intensity (VLI) is proposed, which avoids saturation while overcoming previous algorithms’ long response time disadvantage. Unlike the previously introduced VLI method, the enhanced TSLC does not tweak the learning intensity based on the previous controller output. Instead, it relates learning intensity to an intermediate variable directly related to the system state and tunes the learning intensity using a tanh-type function. Since the system state reflects the tracking error in real-time, the transformed tanh-type function has a higher decay rate than the exponential function, which not only significantly reduces the saturation response but also improves the response speed and achieves higher steady-state accuracy. Simulation proved TSLC’s superiority, considering adverse actuator factors such as dead zone, bias torque, and saturation. The proposed approach has also been validated on the Quanser helicopter platform, confirming its better performance.

Unsteady aerodynamics because of wing motion and underactuated caused by bionic driving mode are among the most notable challenges to accurate dynamic modeling and flight control for flapping-wing aircraft (FWAs). In this paper, we establish a comprehensive 6-degree-of-freedom (DoF) dynamic model to describe the multi-mode motion of a falcon-like FWA. The unsteady vortex lattice method (UVLM) is used to compute the time-varying aerodynamic forces on the main wing, accounting for the effects of flexible deformations and lateral dynamics. Based on a series of wind tunnel experiments, a data-driven model for the V-tail is developed using an artificial neural network (ANN) to capture the relationship between ruddervators’ deflections and aerodynamic forces. A simulation environment, constructed on the foundation of aerodynamic modeling and dynamic analysis, is validated with both open-loop and closed-loop flight data. The virtual system allows for rapid verification and optimization of the model and control parameters, which can enhance the flexibility and reliability of the development process of FWAs. Note to Practitioners—This work addresses the challenges of modeling and controlling FWAs, which are known for their exceptional agility but also pose significant difficulties due to unsteady aerodynamics and underactuated mechanisms. We develop a 6-DoF dynamic model and simulation environment for a falcon-like FWA, capturing multi-mode motion. Unlike dynamic models constrained to small perturbations around stable operating points, our approach provides a more comprehensive description of FWA dynamics across diverse flight modes. Validated with real flight data, this virtual environment is proven to be reliable and capable of supporting advanced control methods such as reinforcement learning and model predictive control. It offers a platform for optimizing FWA design and control, enhancing flexibility and reliability in practical applications while paving the way for further improvements in complex flight scenarios.

The acceptance of academic papers involves a complex peer-review process that requires substantial human and material resources and is susceptible to biases. With advancements in deep learning technologies, researchers have explored automated approaches for assessing paper acceptance. Existing automated academic paper rating methods primarily rely on the full content of papers to estimate acceptance probabilities. However, these methods are often inefficient and introduce redundant or irrelevant information. Additionally, while Bert can capture general semantic representations through pretraining on large-scale corpora, its performance on the automatic academic paper rating (AAPR) task remains suboptimal due to discrepancies between its pretraining corpus and academic texts. To address these issues, this study proposes LMCBert, a model that integrates large language models (LLMs) with momentum contrastive learning (MoCo). LMCBert utilizes LLMs to extract the core semantic content of papers, reducing redundancy and improving the understanding of academic texts. Furthermore, it incorporates MoCo to optimize Bert training, enhancing the differentiation of semantic representations and improving the accuracy of paper acceptance predictions. Empirical evaluations demonstrate that LMCBert achieves effective performance on the evaluation dataset, supporting the validity of the proposed approach. The code and data used in this article are publicly available at https://github.com/iioSnail/LMCBert.

This article investigates an adaptive fault-tolerant control (AFTC) problem for a quadrotor UAV subject to external disturbances, actuator faults and saturations, and system uncertainties. First, to ensure accurate estimation of external disturbances, the novel second-order fuzzy disturbance observers (SOFDOs) are proposed by combining fuzzy logic systems and projection functions. Unlike existing research, the proposed SOFDOs effectively solve the coupled problem between parameter estimations caused by actuator faults and disturbance estimations. Then, the AFTC scheme is designed based on the Lyapunov direct method to guarantee the stability of the closed-loop system for the quadrotor UAV. In addition, since the chosen control parameters are selected based on stability concerns without considering performance optimization, a modified particle swarm optimization algorithm is introduced to optimize the design parameters of the proposed scheme. At last, the effectiveness of the proposed parameter-optimization-based AFTC scheme is verified through numerical simulations.

In this study, a novel fault-tolerant broad fuzzy learning control scheme is proposed for a flexible single-link manipulator with input delay and output constraints. The broad fuzzy neural network is utilized to effectively approximate the time delay of the controller and compensate for the unknown nonlinear uncertainties. By applying the Lyapunov direct method and barrier Lyapunov function, the semi-global uniformly ultimately boundedness (SGUUB) of the system is demonstrated, which guarantees that all system states converge to zero within the specified limitation. Finally, the simulation and experiment results, compared with those of different neural networks, manifest the validity of the proposed control method.

In nature, fish have evolved sophisticated muscular systems that enable them to dynamically regulate their body movements for efficient and agile swimming, which has inspired the development of compact and fast flexibility regulation mechanisms in robotic fish. While existing robotic fish have primarily relied on passive flexible mechanisms and tunable stiffness mechanisms, these approaches often lack the dynamic adjustment capabilities that are characteristic of living fish. This article proposes a novel biomimetic flexible fishtail capable of dynamically controlling its deformation through artificial muscles made from macrofiber composite. In detail, the fishtail is equipped with a servo motor as the sole driving joint, while the artificial muscles regulate the deformation to indirectly adjust stiffness. A dynamic model considering both flexibility and hydrodynamics is established, and a partial differential equation observer is particularly developed to estimate the tail's full states. Subsequently, a deformation control framework incorporating a deep reinforcement learning strategy is constructed and successfully deployed on an embedded platform via lightweight design. Simulation and experimental results validate the accuracy and effectiveness of the dynamic model, observer, and control strategy. Especially, the proposed fishtail demonstrates the ability to enhance propulsion in fishlike swimming modes across various frequencies, ranging from 15% to 203%. When assembled into an untethered robotic prototype, deformation control allows the prototype's swimming speed to vary, achieving up to 42% slower or 37% faster speeds compared to passive compliance. Its rapid adjustability and adaptability to different frequencies represent significant advancements not widely reported in previous studies. The obtained results will offer some significant insights for flexible robotic systems to enhance their agility and interactivity.

In this article, we propose an adaptive neural network event-triggered control (ETC) to suppress the vibration of a high-rise building under uncertainty. This neural network efficiently handles unmodeled components in the system and approximates unknown nonlinear functions. An ETC mechanism with a relative threshold strategy is introduced, balancing the control effectiveness of the active mass damper (AMD) and extending operational lifespan. The ultimate boundedness of the system is verified using the Lyapunov direct method, ensuring convergence of vibration displacement and acceleration toward zero. The efficacy of this control scheme is demonstrated through detailed numerical simulations and experimental analyses.

With the burgeoning growth of the maritime economy, marine risers have emerged as reliable and convenient conduits for the transport of oil and natural gas. However, these risers are vulnerable to vibrational disturbances, which can adversely impact system performance and induce fatigue damage. Therefore, effective vibration control strategies are required to address this issue. This study introduces an innovative adaptive quantized fault-tolerant control strategy designed to attenuate vibrations in a three-dimensional (3-D) riser-vessel system against the effects of actuator faults, unknown control direction, and external disturbances. Different from previous findings, the suggested controller can directly counteract the nonlinear component stemming from actuator faults and handle the nonlinear decomposition inherent to the quantizer, without the necessity for upper-limit estimation. Furthermore, to tackle the input saturation, control laws are formulated using the hyperbolic tangent operator. Finally, the proposed controller’s effectiveness and robustness are validated through thorough Lyapunov analysis and numerical simulations, affirming the system’s uniformly bounded stability.

While a deadlock control problem in complex resource allocation systems (RASs) has been extensively studied in the literature, the corresponding results that are applicable to assembly systems are quite limited, both, in terms of structural analysis of deadlocks and deadlock resolution. Taking Petri nets (PNs) as a modeling and analysis tool, this article focuses on the deadlock control problem for a forward-conflict free net (FCFN), which allows for batch assembly and multiple resource allocations. First, a new structural characterization of deadlocks in FCFN is proposed through two structural objects: 1) circuit and 2)$\omega $-structure. The starting point for this is motivated by the fact that deadlocks in assembly systems stem not only from the circular wait of resources but also the parts waiting for their assembly with other parts. Subsequently, based on these two objects, a necessary and sufficient condition about FCFNs liveness is obtained: 1) an FCFN is live if and only if no circuit and 2)$\omega $-structure are saturated at any reachable marking. Finally, in order to prevent each such object from inducing deadlocks, a hierarchical search algorithm appropriate for real-time implementation is developed to avoid its saturation. The proposed algorithm is proven to be capable of ensuring the deadlock-free operation of FCFNs. Moreover, several examples are provided to demonstrate its effectiveness.

This study presents an adaptive event-triggered control methodology for flexible manipulator systems with prescribed performance and input backlash. To reduce the communication burden between the controllers and actuators, we consider a relative threshold event-triggered mechanism. Then, an adaptive inverse function is applied to eliminate the input backlash of the actuator, and a neural network is adopted to handle the system uncertainty. It is proven that the proposed control approach not only ensures the tracking error converges to a small region close to zero within the prescribed time but also significantly reduces overshoot by using Lyapunov’s direct method. Furthermore, the efficacy of the scheme proposed is demonstrated through numerical simulations and experiments.

This article presents a disturbance observer (DO)-based neural network (NN) control for a two-degree-of-freedom (2-DOF) helicopter system with input saturation, external disturbances, and output constraints. First, the uncertainties in the helicopter system are approximated using a radial basis function NN. Subsequently, a DO is used to approximate unknown compound disturbances, involving errors from NN estimation, input saturation, and external disturbances. To address the issue of output constraints imposed at a prescribed time period, a novel time-shift function and an adjusted barrier function are employed. Through the direct Lyapunov method, the boundedness of all control signals in the closed-loop system is verified. Finally, the effectiveness of the proposed control method is validated through numerical simulation results.

The development of robotic hand that can imitate human movements has always been an important research topic. In this paper, a linkage-driven underactuated three-finger hand is proposed to imitate the flexion/extension (f/e) and abduction/adduction (a/a) motions of human hand. The robotic hand has three identical underactuated fingers, each of which contains an underactuated planar linkage, a spherical four-bar mechanism, and a set of bevel gears. The spherical four-bar mechanism is designed to provide 2-degree-of-freedom actuation, driving the f/e and a/a motions of the proximal joint simultaneously. Based on screw theory, the kinematic model of the spherical mechanism is established, and the maximum available workspace index (MAW) of the spherical mechanism is proposed to evaluate the workspace with the same adduction and abduction angle ranges. The effects of the parameters of the spherical mechanism on the MAW and the transmission efficiency are obtained, and the parameters of the spherical mechanism are optimized. The optimization results show that the MAW of the spherical mechanism can be increased by up to 3.5 times. Finally, experiments are carried out to show the proposed robotic hand can perform simultaneous adaptive grasping and in-hand manipulation.Note to Practitioners—The robotic hand is of great significance for replacing workers in heavy, repetitive, and unsafe working environments. Uncertain environments in non-manufacturing fields, such as industries for pick-and-place, sorting, palletizing, assembly and other operations, marine development, and medical service, require that robotic hands can operate tasks like human hands. Inspired by the human hand with the f/e and a/a motions, a linkage-driven underactuated three-finger hand is proposed in this paper to imitate the functions of the human hand. Due to its underactuated characteristics and a/a function, the designed robotic hand is more dexterous and economical. The effects of the design parameters of the spherical mechanism on the MAW and the transmission efficiency are analyzed, and the parameters of the spherical mechanism are optimized to improve the performance of the robotic hand. This approach can be used in other industrial applications related to the robotic hand or the spherical mechanism.

We investigate the trajectory tracking problem of underactuated aerial vehicles with unknown mass in the presence of unknown non-vanishing disturbances using an event-triggered approach, while considering the constraint that the derivative of the reference trajectory is not available. In contrast to existing references where the derivative of the reference trajectory is needed, here we first introduce a high-gain observer to estimate the unknown derivative solely from the reference trajectory. A disturbance observer is designed to compensate for non-vanishing disturbances, such as wind, etc. Fuzzy logic systems are used to approximate the model uncertainty arising from the unknown mass of the vehicle, and then we derive a thrust command law that follows from a desired stabilizing force. Additionally, unlike traditional fixed and relative threshold strategies that rely solely on control signals, we develop a new time-varying eventtriggered mechanism linked to the performance of the controlled system, taking into account factors such as tracking errors, to develop angular velocity commands, enhancing tracking accuracy while efficiently conserving communication resources, especially in the absence of Zeno behavior. We present simulation results to demonstrate the efficacy of the proposed approach and validate the theoretical findings.

This study proposes a new distributed control method based on an adaptive fuzzy control for multiple collaborative autonomous underwater vehicles (AUVs) to track a desired formation shape within a fixed time. First, a formation control protocol based on a fixed-time backstepping sliding mode control is designed, in which the consensus cooperative tracking errors for each AUV will be formulated. Then, to compensate for the saturated control torques, an adaptive auxiliary variable is introduced. Finally, a fixed-time adaptive fuzzy logic control (FLC) is derived to approximate the unknown dynamics, in which the adaptive laws of the FLC is derived such that the adaptive signals and errors can be convergent within a fixed time. The fixed-time convergence is desired in practice because it provides an exciting property that the global convergence of the whole system is independent with the initial states of the AUVs. The computer simulation results for a consensus formation control of four AUVs show that the proposed formation control can provide higher tracking performance with lower and smoother control efforts.

Identification of dynamical systems from frequency response data (FRD) has extensively been studied and effective techniques have been developed. Given different FRD sets obtained from different systems and a fixed state space model structure, is it possible to find a constant parameter vector containing$(\mathbf {A},\mathbf {B},\mathbf {C},\mathbf {D})$quadruple's numerical content and a FRD-associated mask vector set that approximates the spectral information available in each FRD set? This article proposes a genetic algorithm based optimization approach to determine the real parameter vector$(\mathbf {A},\mathbf {B},\mathbf {C},\mathbf {D})$and the binary mask vector through a sequential optimization scheme. We study state space models for matching FRD from multiple systems. Results show that the proposed optimization approach solves the problem and compresses multiple dynamical models into a single masked one.

Modern temporal modeling methods, such as Transformer and its variants, have demonstrated remarkable capabilities in handling sequential data from specific domains like language and vision. Though achieving high performance with large-scale data, they often have redundant or unexplainable structures. When encountering some real-world datasets with limited observable variables that can be affected by many unknown factors, these methods may struggle to identify meaningful patterns and dependencies inherent in data, and thus, the modeling becomes unstable and unpredictable. To tackle this critical issue, in this article, we develop a novel algorithmic framework for inferring latent factors implied by the observed temporal data. The inferred factors are used to form multiple predictable and independent signal components that enable not only the reconstruction of future time series for accurate prediction but also sparse relation reasoning for long-term efficiency. To achieve this, we introduce three characteristics, i.e., predictability, sufficiency, and identifiability, and model these characteristics of latent factors via powerful deep latent dynamics models to infer the predictable signal components. Empirical results on multiple real datasets show the efficiency of our method for different kinds of time series forecasting tasks. Statistical analyses validate the predictability and interpretability of the learned latent factors.

Fault detection for distributed parameter systems (DPSs) generally requires the complete model information to be known so far. However, for numerous industrial applications, it is common that accurate first-principles physical models are extremely difficult to obtain. Hence, the applicability of traditional model-based methods is being restricted. To pave the way, an adaptive neural network (AdNN) is constructed to simultaneously estimate the state variable and the unknown nonlinearity for a class of partially known nonlinear DPSs. Moreover, considering that full-state measurement is unrealistic in applications, the proposed adaptive neural observer is based on a reduced-order model, which also increases the computation efficiency. Then, the residual generation and evaluation are conducted using the output estimation error of the proposed adaptive neural observer. Bearing the effects of the neglected fast dynamics in mind, a data-driven threshold generation scheme is proposed. Extensive experimental results are presented and analyzed to validate the effectiveness of the proposed method.

Bionic flapping-wing aircraft is a strongly coupled and underactuated system, and its dynamic modeling and intelligent control are still a major challenge. In this paper, we develop an 3-dimensional dynamic model for the flapping-wing aircraft designed by our team. The aerodynamic performance of the wing is analysed by the blade element method and a theoretical calculation model is obtained. Based on wind tunnel experiment, an aerodynamic model is identified for the V-Tail, the attitude control ruddervators. Further, we build a virtual simulation environment based on gym, which is verified by the outdoor flight data. This work provides the basis for intelligent control of flapping wing aircraft.

This article considers the design of an adaptive iterative learning controller for high-rise buildings with active mass dampers (AMDs). High-rise buildings in this article are seen as distributed parameter systems, in which the characteristics of every point in buildings should be considered. Two partial differential equations (PDEs) and several ordinary differential equations are used to describe the model of buildings. To achieve the control target that is to suppress the vibration induced by high winds, an adaptive iterative learning controller is proposed for the flexible building system with boundary disturbance. The convergency of the adaptive iterative learning control (AILC) approach is proven by serious theory analysis. In simulations and experiments, this article uses both the analysis of figures and quantitative analysis (root-mean-square values) to illustrate the efficiency of the AILC scheme.

An adaptive neural network (NN) control is proposed for an unknown two-degree of freedom (2-DOF) helicopter system with unknown backlash-like hysteresis and output constraint in this study. A radial basis function NN is adopted to estimate the unknown dynamics model of the helicopter, adaptive variables are employed to eliminate the effect of unknown backlash-like hysteresis present in the system, and a barrier Lyapunov function is designed to deal with the output constraint. Through the Lyapunov stability analysis, the closed-loop system is proven to be semiglobally and uniformly bounded, and the asymptotic attitude adjustment and tracking of the desired set point and trajectory are achieved. Finally, numerical simulation and experiments on a Quanser's experimental platform verify that the control method is appropriate and effective.

In this paper, we propose a control for a class of multi-agent systems described by diffusion partial differential equations to solve the leader-follower consensus problem. Each group of follower agents changes according to the leader group's formation, obtaining the desired formation by applying boundary control. We use the Lyapunov direct method to prove the system is uniformly ultimately bounded. Numerical simulation results finally show the validity of the proposed control.

In this article, we propose a redundant fault-tolerant and boundary constraint control in the framework of adaptive method, the neural network approximation, and the barrier Lyapunov function (BLF) with a relaxed initial condition. The actuator failures are compensated by a combination of adaptive methods and redundant actuators when some actuators suffer from partial or even total loss of effectiveness. The radial basis function’s neural network structure is introduced to strengthen the adaptivity in various orientations of the hose and other additionally unmodeled dynamics. To maintain the boundary deflection within a predefined open set after a constraint time, a novel asymmetrical and time-varying BLF is constructed by applying a shifting function to transform the original state into a new state with zero value initially. The performance of the developed adaptive control is demonstrated by numerical simulations. Note to Practitioners—This article is motivated by the limited performances in the existing control designs for flexible unmanned aerial refueling hose systems with failed actuators and boundary constraints. This article considers a redundant actuator case to solve time-varying and partially and totally failed actuator failures that are not settled by adaptive and Nussbaum-based single control. Unlike conventional barrier Lyapunov functions (BLFs), in this article, we resort to a shifting function and propose a novelly asymmetric and time-varying BLF, which is well defined initially and capable to address a deferred constraint control problem. The control design and stability analysis of the actuated system is a Lyapunov-based method rather than relying on semigroup theory or functional analysis, which makes the developed method more engineering orientated. The proposed control strategy is tested to illustrate performances in numerical simulations with the finite difference method.

This article discusses cooperative control of a mobile dual flexible manipulator system with asymmetric time-varying output constraints and actuator failures. The shift function and a barrier Lyapunov function (BLF) are used to guarantee output constraints when the initial states of the system violate the prescribed constraints. Moreover, an adaptive fault-tolerant control scheme is developed to deal with actuator failures, while suppressing system’s vibration and achieving cooperative operation. Finally, theoretic analysis proves the uniform bounded stability of the system and numerical simulation verifies the effectiveness of the proposed control method. Note to Practitioners—The purpose of this article is to develop a cooperative dual flexible manipulator system with performance limitations and actuator failures. The system can realize the task of stably grasping and moving a rigid object. The existing research on the grasping task of flexible manipulators only focuses on coordinated operation, which limits the application of the dual flexible manipulator system in practical engineering. To further study the problem, this article considers the output constraints and actuator failures of the dual flexible manipulator system in the actual process and proposes a cooperative fault-tolerant control framework. The control framework uses shift function and BLF to deal with output constraints and adopts adaptive technology to deal with actuator failures. In addition, the cooperative operation task of grasping object with dual flexible manipulators is realized. Simulation shows that this control strategy is feasible.

This article focuses on flexible single-link manipulators (FSLMs) under boundary control and in-domain control. The actuators of the system include the dc motor at the end of the joint and m piezoelectric controllers installed at the flexible link, which is regarded as an Euler-Bernoulli beam. The problem of the infinite number of actuator failures, including the partial loss of the effectiveness and total loss of effectiveness, is solved by the adaptive compensation method. By introducing the relative threshold strategy, the event-triggered control (ETC) scheme is proposed to achieve angle regulation and vibration suppression while reducing the communication burden between the controllers and the actuators. The Lyapunov direct method is utilized to prove that the system is uniformly ultimately bounded and both the angular tracking error and elastic displacement converge to a neighborhood of zero. Numerical simulation results are provided to demonstrate the effectiveness of the proposed control law.

This article proposes an adaptive fuzzy control approach with an event-triggered mechanism and spatial motion constraint for a hybrid spacecraft system. The spacecraft system is composed of a rigid body and a slender flexible panel, with coupled dynamics captured by three ordinary differential equations and two partial differential equations. The overall control objective lies in utilizing an event-triggered control input to regulate the angular velocities of the rigid body and stabilize the vibrations of the flexible panel under unknown input disturbances and prescribed spatial motion performance. We collectively address the posture regulation and disturbance rejection purposes by introducing a barrier Lyapunov function and a fuzzy logic system. The event-triggered solution only updates the control signals at some discrete-time instants, and hence the communication burden is reduced significantly. The potential effectiveness and thrifty efficiency of the developed control strategy are theoretically demonstrated and numerically verified.

In this study, we propose an adaptive fuzzy event-triggered control scheme for an autonomous aerial refueling hose system involving uncertainty, an event-triggered mechanism, and actuator failures. The unknown nonlinear function is approximated using the designed fuzzy logic systems. Through introduction of the adaptive compensation scheme, the problem of an infinite number of actuator failures, including partial and complete failures, is solved. In addition, the event-triggered control strategy is designed to achieve vibration suppression while decreasing the communication burden between the controllers and actuators. The stability of the closed-loop system is demonstrated via the Lyapunov direct method. Finally, simulation examples are presented to confirm the validity of the proposed control scheme.

As a kind of large flexible structure, high-rise buildings need to consider wind-resistant and anti-seismic problems for the safety of occupants and properties, especially in coastal areas. This article proposes an infinite dimensional model and an adaptive boundary control law for an active mass damper (AMD) on this question. The dynamic model of the high-rise building is a combination of some storeys which have flexible walls and rigid floors under a series of physical conditions. Then the adaptive boundary controller is acted on an AMD which is equipped on the top floor, in order to suppress the vibration of every floor and guarantee the comfort of residents. Moreover, simulations and experiments are carried out on a two-floor flexible building to illustrate the effectiveness of the proposed control strategy.

This article studies a single boundary regulator for the flexible tail of an autonomous robotic fish to implement complex oscillating body motions. The dynamic model of the flexible tail is derived by Hamilton’s principle, and conforms to the partial nonuniform Euler–Bernoulli beam with the uneven parameters. Then, a boundary control at the body–tail junction is proposed to manipulate the oscillation of the flexible tail, which is given in the form as a torque. The exponential stability of the error system is deduced by the integral Lyapunov synthesis. For further considering the boundary disturbance at the same point with control and the distributed disturbance, a disturbance observer is proposed. By appropriately choosing the designed parameters, the uniformly ultimate boundedness with disturbances is proved and the tracking error converges to a small neighborhood of 0. Finally, some simulations are presented to illustrate the effectiveness of the proposed control.

This article addresses fuzzy observer design for a nonlinear parabolic equation over an unit square domain$\Omega$in terms of the time delayed spatially averaged measurement, where the observer is composed of$m$-chain of subobservers. Due to 2-D domain, special emphases are made to the computational complexity. A Lyapunov argument is utilized to give constructive conditions ensuring the exponential stability of the resulting error system. The method used for the continuous-time fuzzy observer is applicable to the sampled-data implementation. Consistent simulation results that support the proposed theoretical statements are presented.

In this paper, we study the control problems of distributed parameter systems, and discuss the limitations of traditional control methods. In recent years, social factors have gradually become an essential parameter of system modeling. For complex distributed parameter systems, the accurate modeling becomes difficult. With the rapid development of the network and the technology of big data and cloud computing, based on the advanced control theory of large-scale computing, we introduce the idea of parallel control to the control of distributed parameter systems. Parallel control is a method to accomplish tasks through the interaction of virtual and actual. Its core is to model the complex distributed parameter system on artificial society or artificial system, then analyze and evaluate it by computational experiment, and finally control and manage the distributed parameter system by parallel execution. Data-driven control and computational control are used in this method, which is a control idea that adapts to the rapid development of society.