Search Results (1,214)

For marine development in harsh sea states, floating-body salvage equipment serves as critical support infrastructure. Aiming at the challenges of nonlinear dead-zone, model uncertainty, and actuator failures in the wave compensation systems of such equipment, this paper proposes an intelligent fault-tolerant control method based on neural networks. First, the dead-zone nonlinearity of the hydraulic system is compensated using an inverse model approach. Then, neural networks are employed to online learn unmodeled dynamics, while adaptive laws are designed to handle partial actuator failures and Lyapunov theory is used to prove the global stability of the closed-loop system, effectively enhancing the robustness and fault-tolerance of the wave compensation system under complex sea conditions. Unlike existing studies that rely on accurate system models, the proposed method integrates data-driven learning with model-based compensation. This integration enables adaptive handling of wave disturbances, model uncertainties, and actuator faults, thereby overcoming the strong model dependence and complex observer design inherent in traditional sliding-mode fault-tolerant control. Simulation and experiment results show that the method ensures high-precision dynamic tracking and compensation performance under various sea conditions. Full article

With the development of aerospace technology, hypersonic flight vehicles are evolving towards larger size, lighter weight, and higher performance. Their cross-domain maneuverability and extreme flight environment led to the rigid–flexible coupling effect and became the core bottleneck restricting performance improvement, seriously affecting flight stability and control accuracy. This paper systematically reviews the research status in the field of control for high-speed rigid–flexible coupling aircraft and conducts a review focusing on two core aspects: dynamic modeling and control strategies. In terms of modeling, the modeling framework based on the average shafting, the nondeformed aircraft fixed-coordinate system, and the transient coordinate system is summarized. In addition, the dedicated modeling methods for key issues, such as elastic mode coupling and liquid sloshing in the fuel tank, are also presented. The research progress and challenges of multi-physical field (thermal–structure–control, fluid–structure–control) coupling modeling are analyzed. In terms of control strategies, the development and application of linear control, nonlinear control (robust control, sliding mode variable structure control), and intelligent control (model predictive control, neural network control, prescribed performance control) are elaborated. Meanwhile, it is pointed out that the current research has limitations, such as insufficient characterization of multi-physical field coupling, neglect of the closed-loop coupling characteristics of elastic vibration, and lack of adaptability to special working conditions. Finally, the relevant research directions are prospected according to the priority of “near-term engineering requirements–long-term frontier exploration”, providing Refs. for the breakthrough of the rigid–flexible coupling control technology of the new-generation high-speed aircraft. Full article

In recognising both the emerging industrial applications of multi-link robotic manipulators and the inherent challenges of modelling and controlling their highly complex nonlinear dynamics, this work proposes a completely model-free terminal sliding mode control (MFTSMC) design approach to reduce the sensitivity and complexity often associated with model-based routines. Consequently, the proposed design achieves strong robustness, simplicity, and good operation tuning by eliminating the need for system modelling and enabling direct operator–machine interaction. Simulink simulations on a 3-link case subjected to different disturbance conditions (free, low-frequency, high-frequency, and mixed) show rapid dynamic convergence, good tracking precision, and strong disturbance rejection. The system reaches the sliding surface within 0.07 s, maintains steady-state errors around ${10}^{-2}$, and achieves a smooth torque response with low energy costs. The benchmark results confirm the finite-time convergence and demonstrate that the proposed framework is practical and scalable for multi-DOF systems and has potential for underactuated manipulators. It should be noted that a generalised dynamic model for a planar n-link manipulator is presented in the study for (1) the ground truth of the manipulator in simulation (not for the MFTSMC design), (2) the model-based controller designs in comparison to the MFTSMC, and (3) understanding the dynamic characteristics. Full article

This paper investigates the global polynomial synchronization (GPS) problem for quaternion-valued neural networks (QVNNs) featuring proportional delay, parameter uncertainty, and external disturbance. A combined approach of sliding mode control (SMC) and a non-separation strategy is adopted to achieve this goal. First, an integral-type sliding surface is designed for the system. Then, by constructing a delay-free Lyapunov functional and leveraging the properties of the quaternion vector norm and inequality techniques, sufficient conditions are derived to achieve GPS for the sliding mode dynamics. Furthermore, both a SMC law and an adaptive SMC law are designed, with a reachability analysis confirming that the system trajectories reach the predefined sliding surface in finite time. Finally, numerical examples with graphical analysis are provided to verify the obtained results, along with their application in color image pattern storage and retrieval. Full article

Active Debris Removal (ADR) missions demand precise and rapid controllers that lower collision risks specifically in the capture phase of tumbling objects. Sliding Mode Control (SMC), in general, offers robustness against model uncertainties. However, traditional reaching laws often face slow convergence when the chaser is too far from the target state. In this paper, we address this particular limitation and present the first application of Power Rate Reaching Law Sliding Mode Control (PRRL-SMC) to the 6-DOF coupled dynamics of a CubeSat-based debris capture mission in both the pre-capture tracking and post-capture stabilization phases in the case of tumbling debris. To show the strength of our work, we evaluate the proposed controller against Proportional–Derivative (PD), Linear Quadratic Regulator (LQR), second-order SMC (SOSMC), and terminal SMC (TSMC) for the pre-capture tracking and post-capture stabilization phases. By numerical simulations we show that PRRL-SMC reduces convergence time extremely and achieves stable capture in 7.6 s. This time it is 24.6 s for LQR and 28.1 s for SOSMC. The controller also handles the abrupt inertia variations of the combined stack post-capture successfully. This is efficient for proximity operations because of their importance in timing and fuel conservation. Full article

Tunnel boring machine (TBM) hydraulic cylinders operate under pronounced start–stop shocks and load uncertainties, making it difficult to simultaneously achieve smooth start-up and high-precision tracking. This paper proposes a two-phase switching adaptive sliding mode control (ASMC) strategy for TBM hydraulic actuation. Phase I targets a soft start by introducing smooth gating and a ramped start-up mechanism into the sliding surface and equivalent control, thereby suppressing pressure spikes and displacement overshoot induced by oil compressibility and load transients. Phase II targets precise tracking, combining adaptive laws with a forgetting factor design to maintain robustness while reducing chattering and steady-state error. We construct a state-space model that incorporates oil compressibility, internal/external leakage, and pump/valve dynamics, and provide a Lyapunov-based stability analysis proving bounded stability and error convergence under external disturbances. Comparative simulations under representative TBM conditions show that, relative to conventional PID Controller and single ASMC Controller, the proposed method markedly reduces start-up pressure/velocity peaks, overshoot, and settling time, while preserving tracking accuracy and robustness over wide load variations. The results indicate that the strategy can achieve the unity of smooth start and high-precision trajectory of TBM hydraulic cylinder without additional sensing configuration, offering a practical path for high-performance control of TBM hydraulic actuators in complex operating environments. Full article

Quadrotor unmanned aerial vehicles demonstrate broad application prospects, yet existing research still lacks a comprehensive solution that simultaneously addresses efficiency, disturbance rejection, environmental adaptability, and precision in their control performance. To achieve prescribed-time convergence and prescribed tracking performance, this work proposes a composite control scheme that integrates prescribed-performance control, disturbance estimation, and terminal sliding-mode control. First, a prescribed-time adaptive composite disturbance observer is developed to estimate and compensate for system composite disturbances, and a stability analysis shows that the disturbance estimation error converges to a small neighborhood of the origin within a prescribed time. Second, the system is decomposed into position and attitude subsystems, enabling tailored hierarchical control-law design and analysis based on their distinct dynamics. For position control, a prescribed-performance control method is employed, incorporating a prescribed-time performance function that accommodates large initial deviations, thereby guaranteeing convergence of the position-tracking errors to a small neighborhood within a specified time. For attitude control, a prescribed-time terminal sliding-mode surface and corresponding control law are designed to eliminate singularities and ensure convergence of the attitude errors to a small neighborhood within a predetermined time. The stability of both subsystems is rigorously substantiated through theoretical analysis. Finally, comparative simulation results confirm the effectiveness and superiority of the proposed control strategy. Full article

The control design of three-phase AC/DC converters is particularly challenging, as their dynamic behavior is governed by complex nonlinear interactions and strong coupling among system variables, conventional Proportional–Integral (PI) controllers often suffer from sluggish transient responses and limited immunity to interference. To address these issues, Sliding-Mode Control (SMC) is widely adopted for its robustness against parameter uncertainties and rapid dynamic performance. However, the chattering phenomenon inherent in traditional SMC near the sliding surface remains a critical challenge. To improve the dynamic performance of sliding-mode control, this work introduces a redesigned exponential reaching law into the control framework. The proposed strategy is implemented in a voltage–current cascaded (double closed-loop) structure, where the improved reaching law is embedded in the outer DC-link voltage loop and the inner loop regulates the grid currents in the synchronous dq frame. By modifying the reaching dynamics, the proposed approach effectively weakens chattering phenomena while enabling faster convergence of the system states. Comprehensive validation was conducted using Matlab/Simulink simulations and experimental prototypes. The results demonstrate that, compared to PI control and traditional exponential reaching law-based SMC, the proposed strategy significantly mitigates chattering while delivering superior static stability and faster dynamic response. Full article

This study focuses on investigating the adaptive sliding mode control (SMC) problem for connected vehicles with input delays and unknown time-varying control coefficients. As a result of wear and tear of mechanical components, throttle response lags, and the internal data processing time of the controller, input delays widely exist in vehicle actuators. Since input delays may lead to instability of the vehicle platoon, an improved integral compensation mechanism (ICM) with the adjustment factor for input delays is developed to improve the platoon’s robustness. As the actuator efficiency, drive mechanism, and load of the vehicle may change during operation, the control coefficients of vehicle dynamics are usually unknown and time-varying. A novel adaptive updating mechanism utilizing a radial basis function neural network (RBFNN) is designed to deal with the unknown time-varying control coefficients, thereby improving the vehicle platoon’s tracking performance. By integrating the improved ICM and the RBFNN-based adaptive updating mechanism (RBFNN−AUM), an innovative distributed adaptive control scheme using sliding mode techniques is proposed to guarantee that the convergence of state errors to a predefined region and accomplish the vehicle platoon’s control objectives. Comparative numerical results confirm the effectiveness and superiority of the developed control strategy over existing method. Full article

With the rapid development of unmanned agricultural machinery technology, the accuracy and stability of agricultural machinery path tracking have become key challenges in achieving precision agriculture. To address the issues of insufficient accuracy and stability in path tracking for rice transplanters in paddy fields, this study proposes a composite control strategy that integrates the extended line-of-sight (LOS) guidance law with an adaptive fuzzy sliding mode control law. By establishing a two degree of freedom dynamic model of the rice transplanter, two extended state observers are designed to estimate the longitudinal and lateral velocities of the rice transplanter in real time. A dynamic compensation mechanism for the sideslip angle is introduced, significantly enhancing the adaptability of the traditional look-ahead guidance law to soil slippage. Furthermore, by combining the approximation capability of fuzzy systems with the adaptive adjustment method of sliding mode control gains, a front wheel steering control law is designed to suppress complex environmental disturbances. The global stability of the closed-loop system is rigorously verified using the Lyapunov theory. Simulation results show that compared to the traditional Stanley algorithm, the proposed method reduces the maximum lateral error by 38.3%, shortens the online time by 23.9%, and decreases the steady-state error by 15.5% in straight-line path tracking. In curved path tracking, the lateral and heading steady-state errors are reduced by 19.2% and 14.6%, respectively. Field experiments validate the effectiveness of this method in paddy fields, with the absolute lateral error stably controlled within 0.1 m, an average error of 0.04 m, and a variance of 0.0027 m². Full article

A fixed-time sliding mode controller based on an adaptive neural network is developed for the path tracking problem of robotic manipulators with model uncertainty and external nonlinear interference. Firstly, a fixed-time sliding surface and sliding mode reaching law are designed based on the dynamic model of the robotic manipulator, which ensures that the error signal converges along the sliding surface within a fixed time. The speed of the state approaching the sliding surface can be flexibly adjusted through the reaching law, and it has strong robustness to parameter perturbations and external disturbances. Then, the uncertainty of model parameters and external disturbances is regarded as composite interference, and an adaptive neural network is utilized to approximate the disturbance online for adaptive fitting. This does not require precise modelling, the control input jitter is reduced, the composite disturbance is compensated in real time, and the system tracking accuracy is improved. Subsequently, the fixed-time stability characteristics of the closed-loop system are demonstrated through Lyapunov stability theory. Finally, the effectiveness and robustness of the proposed control strategy are verified through simulation. Full article

This paper describes a composite control approach that improves the accuracy and dynamic performance of the control of a voice-coil-driven, two-dimensional fast steering mirror (FSM). Strong nonlinearity, perturbation of parameters, unmodeled dynamics and external disturbances typically compromise the performance of the FSM. The proposed controller combines a tracking differentiator (TD), linear extended state observer (LESO), and a double-integral global fast terminal-sliding mode control (DIGFTSMC). The TD corrects the reference command signal, and the LESO approximates and counteracts system disturbances. The sliding surface is then equipped with the double-integral operators and an improved adaptive reaching law (IARL) to enhance tracking accuracy, response speed and robustness. Prior to physical experiments, systematic numerical simulations were conducted for five control algorithms across four typical test scenarios, verifying the proposed controller’s feasibility and preliminary performance advantages. It is found through experimentation that the proposed controller lowers the time esterified by the step response adjustment by 81.0% and 48.4% more than the PID controller and the DIGFTSMC approach with no IARL, respectively, and the proposed controller enhances error control when tracking sinuoidal signals and multisinusoidal signals. Simulation results consistently align with experimental trends, confirming the proposed controller’s superior convergence speed, tracking precision, and disturbance rejection capability. Furthermore, it cuts the angular movement swing by an average of over 44% through dismissing needless vibration interruptions as compared to other sliding mode control techniques. Experimental results demonstrate that the proposed composite control approach significantly enhances the disturbance rejection, control accuracy, and dynamic tracking performance of the voice-coil-driven FSM system. Full article

Recently, there has been a rapid increase in the demand for magnetic levitation systems. Since they are utilized in many levitation-based systems, one such application is in magnetic levitated (Maglev) trains. Moreover, these systems are complicated to control due to their nonlinear characteristics, susceptibility to external disturbances, and model uncertainties. This article proposes an enhanced integral sliding mode control (ISMC) strategy with a robust optimal framework designed for electromagnetic levitation systems (EMLSs). Traditional sliding mode control (SMC) often suffers from a high-frequency phenomenon in the input, thereby necessitating the development of a more robust controller. This requirement is addressed through the implementation of a comprehensive integral robust optimal sliding mode control strategy. The proposed controller effectively mitigates the chattering phenomenon while simultaneously enhancing the system’s robustness against uncertainties. The robust optimal approach is specifically designed to handle the matched uncertainties inherent in the system dynamics, thereby facilitating an appropriate feedback control mechanism. The Hamilton–Jacobi–Bellman (HJB) equation is used to achieve the robust control design. This feedback control is integrated with the ISMC to execute the desired control action effectively. The simulation results highlight the effectiveness of the proposed control scheme, presenting a comparative analysis of performance indices, including integral time absolute error (ITAE), integral absolute error (IAE), integral squared error (ISE), and integral time squared error (ITSE). These indices collectively underscore the robustness of the control design. Full article

Control of elbow exoskeletons using muscular signals, although promising for the rehabilitation of millions of patients, has not yet been widely commercialized due to challenges in real-time intention estimation and management of dynamic uncertainties. From a practical perspective, millions of patients with stroke, spinal cord injury, or neuromuscular disorders annually require active rehabilitation, and elbow exoskeletons with precise and safe motion intention tracking capabilities can restore functional independence, reduce muscle atrophy, and lower treatment costs. In this research, an intelligent control framework was developed for an elbow joint exoskeleton, designed with the aim of precise and safe real-time tracking of the user’s motion intention. The proposed framework consists of two main stages: (a) real-time estimation of desired joint angle (as a proxy for movement intention) from High-Density Surface Electromyography (HD-sEMG) signals using an LSTM network and (b) implementation and comparison of three PID, impedance, and sliding mode controllers. A public EMG dataset including signals from 12 healthy individuals in four isometric tasks (flexion, extension, pronation, supination) and three effort levels (10, 30, 50 percent MVC) is utilized. After comprehensive preprocessing (Butterworth filter, 50 Hz notch, removal of faulty channels) and extraction of 13 time-domain features with 99 percent overlapping windows, the LSTM network with optimal architecture (128 units, Dropout, batch normalization) is trained. The model attained an RMSE of 0.630 Nm, R² of 0.965, and a Pearson correlation of 0.985 for the full dataset, indicating a 47% improvement in R² relative to traditional statistical approaches, where EMG is converted to desired angle via joint stiffness. An assessment of 12 motion–effort combinations reveals that the sliding mode controller consistently surpassed the alternatives, achieving the minimal tracking errors (average RMSE = 0.21 Nm, R² ≈ 0.96) and showing superior resilience across all tasks and effort levels. The impedance controller demonstrates superior performance in flexion/extension (average RMSE ≈ 0.22 Nm, R² > 0.94) but experiences moderate deterioration in pronation/supination under increased loads, while the classical PID controller shows significant errors (RMSE reaching 17.24 Nm, negative R² in multiple scenarios) and so it is inappropriate for direct myoelectric control. The proposed LSTM–sliding mode hybrid architecture shows exceptional accuracy, robustness, and transparency in real-time intention monitoring, demonstrating promising performance in offline simulation, with potential for real-time clinical applications pending hardware validation for advanced upper-limb exoskeletons in neurorehabilitation and assistive applications. Full article

To satisfy the stringent requirements of ultra-precision systems for high accuracy and rapid dynamic response, the chatter and system delay of voice coil motor (VCM) under operating conditions have become key bottlenecks restricting overall performance enhancement. To address these challenges, this paper proposes an adaptive convergence rate fractional-order sliding mode control strategy based on a Smith predictor (AFOSMC-SP). The strategy constructs a fractional-order sliding mode controller using an improved Oustaloup method, introduces a novel adaptive reaching law based on a saturation function with dynamic gain, and integrates a Smith predictor to compensate for the current-loop delay in the VCM system. This approach reduces algorithmic complexity and position-tracking error while enabling the adaptive adjustment of the system convergence rate to enhance robustness. In addition, the parameter boundary conditions and the global asymptotic stability of the closed-loop system are analyzed using Lyapunov stability theory. The simulation results show that, compared to conventional sliding mode control methods, the proposed AFOSMC-SP strategy provides superior parameter adaptability, higher tracking accuracy, and stronger suppression of external disturbances. Full article