Search Results (593)

Large signal perturbations, such as abrupt load changes and sudden voltage surges, pose significant challenges to the stable operation of DC microgrids. Various control strategies have been explored to mitigate these disturbances. Among them, terminal sliding mode control (TSMC) offers a rapid dynamic response, but its inherent chattering and limited disturbance rejection capabilities hinder its effectiveness in DC microgrid applications. To address these issues, this paper proposes a novel adaptive smooth gain terminal sliding mode control (ASTSMC) strategy for the outer voltage loops of DC-DC converters. In addition, a reduced-order error-based extended state observer (REESO) is integrated to enhance disturbance estimation, mitigate the effects of disturbances, and improve the computational efficiency. The proposed ASTSMC-REESO approach effectively tackles the inherent chattering issues of TSMC, achieving superior voltage control performance under severe disturbances. Experimental results demonstrate that the proposed strategy significantly improves the performance of voltage regulation in DC microgrids. Full article

For the three-phase voltage-source inverters (VSIs), load disturbances and parameter uncertainties degrade the quality of output voltages, potentially leading to system instability. To improve steady-state precision and disturbance rejection, this paper suggests a finite-time backstepping control (FTBC) strategy that incorporates a fixed-time sliding mode disturbance observer (FTSMDO). Firstly, this paper establishes a new dynamic model of the three-phase VSI considering load disturbances, parameter uncertainty and cross-coupling effect. Subsequently, a fixed-time disturbance observer is then developed to precisely estimate the uncertain disturbances, with its convergence time not reliant on the system’s initial conditions. Concurrently, a finite-time differentiator is developed to achieve the desired signals, thereby sidestepping the “explosion of complexity” problem. A finite-time controller is constructed to obtain stable three-phase output voltages. Theoretical and test analysis demonstrate the proposed method is effective. Compared with the PI control, the proposed strategy improves dynamic performance and enhances disturbance-rejection capability under time-varying load disturbances. Full article

Steering angle control in self-driving cars is usually organized in layers combining trajectory planning, path tracking, and low-level actuator control. The steering controller converts the planned path into a desired steering angle and then ensures accurate tracking by the electric power steering (EPS). However, automotive electric power steering (AEPS) systems face many problems caused by model uncertainties, disturbances, and unknown system dynamics. In this paper, a robust finite-time control strategy based on an adaptive backstepping scheme is proposed to handle these problems. First, radial basis function neural networks (NNs) are designed to approximate the unknown system dynamics. Then, an adaptive sliding-mode disturbance observer (ASMDO) is introduced to address the impacts of the lumped disturbance. Enhanced control performance for the AEPS system is implemented using a combination of the above technologies. Numerical simulations and a hardware-in-the-loop (HIL) experimental verification are performed to demonstrate the significant improvement in performance achieved using the proposed strategy. Full article

To address trajectory tracking of cable-driven continuum robots (CDCRs) in the presence of obstacles, this paper proposes an integrated control framework that combines online trajectory replanning, obstacle avoidance, and tracking control. The control system consists of two modules. The first is a trajectory replanning controller developed on an improved model predictive control (IMPC) framework. The second is a trajectory-tracking controller that integrates an adaptive disturbance observer with a fast non-singular terminal sliding mode control (ADO-FNTSMC) strategy. The IMPC trajectory replanning controller updates the trajectory of the CDCRs to avoid collisions with obstacles. In the ADO-FNTSMC strategy, the adaptive disturbance observer (ADO) compensates for uncertain dynamic factors, including parametric uncertainties, unmodeled dynamics, and external disturbances, thereby enhancing the system’s robustness and improving trajectory tracking accuracy. Meanwhile, the fast non-singular terminal sliding mode control (FNTSMC) guarantees fast, stable, and accurate trajectory tracking. The average tracking errors for IMPC-ADO-FNTSMC, MPC-FNTSMC, and MPC-SMC are 1.185 cm, 1.540 cm, and 1.855 cm, with corresponding standard deviations of 0.035 cm, 0.057 cm, and 0.078 cm in the experimental results. Compared with MPC-FNTSMC and MPC-SMC, the IMPC-ADO-FNTSMC controller reduces average tracking errors by 29.96% and 56.54%. Simulation and experimental results demonstrate that the designed two-module controller (IMPC-ADO-FNTSMC) achieves fast, stable, and accurate trajectory tracking in the presence of obstacles and uncertain dynamic conditions. Full article

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

In this work, we develop a disturbance suppression-oriented fuzzy sliding mode secured sampled-data controller for third-order parabolic partial differential equations that ought to cope with nonlinearities, hybrid cyber attacks, and modeled disturbances. This endeavor is mainly driven by formulating an observer model with a T–S fuzzy mode of execution that retrieves the latent state variables of the perceived system. Progressing onward, the disturbance observers are formulated to estimate the modeled disturbances emerging from the exogenous systems. In due course, the information received from the system and disturbance estimators, coupled with the sliding surface, is compiled to fabricate the developed controller. Furthermore, in the realm of security, hybrid cyber attacks are scrutinized through the use of stochastic variables that abide by the Bernoulli distributed white sequence, which combat their unpredictability. Proceeding further in this framework, a set of linear matrix inequality conditions is established that relies on the Lyapunov stability theory. Precisely, the refined looped Lyapunov–Krasovskii functional paradigm, which reflects in the sampling period that is intricately split into non-uniform intervals by leveraging a fractional-order parameter, is deployed. In line with this pursuit, a strictly $({\Phi }_1,{\Phi }_2,{\Phi }_3)-\varrho$ dissipative framework is crafted with the intent to curb norm-bounded disturbances. A simulation-backed numerical example is unveiled in the closing segment to underscore the potency and efficacy of the developed control design technique. Full article

Exoskeleton knee-assistance (EKA) systems are wearable robotic technologies designed to rehabilitate and improve impaired mobility of the lower limbs. Clinical exercises are conducted on disabled patients based on physically demanding tasks which are prescribed by expert physicians. In order to carry out good tracking of the desired tasks, efficient controllers must be designed. In this study, a novel control framework is introduced to improve the robustness characteristics and tracking precision of EKA systems. The control approach integrates a super-twisting sliding mode controller (STSMC) with a nonlinear disturbance observer (NDO) to ensure robust and precise tracking of the knee joint trajectory. An evaluation of the proposed system is conducted through numerical simulations under the influence of external disturbances. The findings reveal considerable enhancements to trajectory tracking accuracy and disturbance rejection when compared to conventional STSMCs and sliding mode perturbation observer (SMPO)-based STSMCs. 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

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

To enhance line capacity in high-speed railways without new infrastructure, virtual coupling train sets (VCTSs) enable reduced inter-train distances via real-time communication and cooperative control. However, unknown disturbances and model uncertainties challenge VCTS performance, often causing chattering, slow convergence, and poor disturbance rejection. This paper proposes a novel finite-time extended state observer-based nonsingular terminal sliding mode (FTESO-NTSM) control strategy. The method integrates a nonsingular terminal sliding mode surface with a hyperbolic tangent-based reaching law to ensure fast convergence and chattering suppression, while a finite-time extended state observer estimates and compensates for lumped disturbances in real time. Lyapunov analysis rigorously proves finite-time stability. Numerical simulations under different initial statuses are conducted to validate the effectiveness of the proposed method. The results show that the maximum observation error achieves 0.0087 kN. The speed chattering magnitudes reach 0.00087 km/h, 0.0017 km/h, 0.0026 km/h, and 0.0034 km/h for the leading train and three followers, respectively. Furthermore, the convergence time of the followers is 56 s, 130 s, and 76 s, respectively. The results highlight that the proposed method can significantly improve line capacity and transportation efficiency. 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

Unmanned airships are highly sensitive to parametric uncertainty, persistent wind disturbances, and sensor noise, all of which compromise reliable path-following. Classical control schemes often suffer from chattering and fail to handle index discontinuities on closed-loop paths due to the lack of mechanisms and cannot simultaneously provide formal guarantees on state constraint satisfaction. We address these challenges by developing a unified, constraint-aware guidance and control framework for path-following in uncertain environments. The architecture integrates an extended state observer (ESO) to estimate and compensate lumped disturbances, a barrier Lyapunov function (BLF) to enforce state constraints on tracking errors, and a super-twisting terminal sliding-mode (ST-TSMC) control law to achieve finite-time convergence with continuous, low-chatter control inputs. A constructive Lyapunov-based synthesis is presented to derive the control law and to prove that all tracking errors remain within prescribed error bounds. At the guidance level, a nonlinear curvature-aware line-of-sight (CALOS) strategy with an index-increment mechanism mitigates jump phenomena at loop-closure and segment-transition points on closed yet discontinuous paths. The overall framework is evaluated against representative baseline methods under combined wind and parametric perturbations. Numerical results indicate improved path-following accuracy, smoother control signals, and strict enforcement of state constraints, yielding a disturbance-resilient path-following solution for the cruise of an unmanned airship. Full article

To achieve high-precision trajectory tracking for multi-joint robotic manipulators in the presence of model uncertainties, external disturbances, and strong coupling effects, this paper proposes a nonsingular fast terminal sliding mode control (NFTSMC) scheme incorporating an extended Kalman filter-based disturbance observer. First, the Kalman filter is combined with an extended state observer to perform the real-time observation of both internal and external disturbances in the system, accurately estimating system uncertainty and external disturbances. This approach reduces noise interference while significantly improving the correction accuracy of position and tracking errors. Second, an improved nonsingular fast terminal sliding mode controller with an optimized convergence law is introduced to ensure stability during the tracking process, effectively mitigate oscillation phenomena, and accelerate the system’s convergence speed. Finally, the convergence of the proposed method is analyzed by constructing an appropriate Lyapunov function. Simulation and experimental results strongly validate the superior performance of the proposed control strategy, demonstrating that the system can achieve high-precision trajectory tracking under the complex coupled effects of a six-axis robotic manipulator, and exhibits significant advantages in terms of accuracy and robustness. Full article

This study presents a comprehensive performance analysis of four different control strategies, Proportional–Integral (PI), classical Sliding Mode Control (SMC), Super-Twisting SMC (ST-SMC), and Exponential Reaching Law SMC (ERL-SMC), applied to the speed regulation of a Hall-effect sensored Brushless DC (BLDC) motor. A mathematically detailed BLDC motor model, three-phase inverter structure with safe commutation logic, and a high-frequency PWM switching scheme were implemented in the MATLAB/Simulink-2024a environment to provide a realistic simulation framework. The control strategies were evaluated under multiple test scenarios, including variations in supply voltage, mechanical load disturbances, reference speed transitions, and steady-state operation. The comparative results reveal that the classical SMC and PI controllers suffer from significant oscillations, overshoot, and limited disturbance rejection capability, especially during voltage and load transients. The ST-SMC algorithm improves robustness and reduces the chattering effect inherent to first-order SMC but still exhibits noticeable oscillations near the sliding surface. In contrast, the proposed ERL-SMC controller demonstrates superior performance across all scenarios, achieving the lowest steady-state ripple, the shortest settling time, and the most stable transition response while significantly mitigating chattering. These results indicate that ERL-SMC is the most effective and reliable control strategy among the evaluated methods for BLDC speed regulation, which requires high dynamic response and disturbance robustness. The findings of this study contribute to the advancement of SMC-based BLDC motor control, providing a solid foundation for future research that integrates observer-based schemes, adaptive tuning, or real-time hardware implementation. Full article

Integral Sliding Mode Control (ISMC) is widely employed in motor position control systems due to its robustness against uncertainties. However, its control performance is critically dependent on the selection of the switching gain. Although Disturbance Observer-Based Control (DOBC) is commonly adopted as an effective alternative for uncertainty compensation, it may exhibit limitations when high gains are required, potentially leading to system instability. To address these issues, this study proposes a Radial Basis Function Neural Network (RBF-NN)-based supervisory learning approach designed to minimize switching gain requirements. The effectiveness of the proposed scheme is validated through comparative simulations and laboratory experiments, specifically under scenarios involving system parameter uncertainties and sinusoidal disturbances with unknown offsets. Both simulation and experimental results demonstrate the superior performance of the proposed RBF-NN approach in terms of switching gain reduction and tracking error norms compared to a conventional ISMC and a DOBC-based cascade P–PI controller. Full article