Sliding-mode predictive control for constrained hovercraft: A short-horizon framework with global stability and external disturbances

Bayesian optimization-enhanced adaptive sliding mode control for 15 MW floating wind turbines: Energy capture and economic analysis

A super-twisting non-singular terminal sliding mode control strategy based on particle swarm optimization for underwater two-link manipulator trajectory tracking

Reliable Nonsingularity Adaptive fixed-time sliding mode control under input saturation for an uncertain robotic manipulator

A chattering-free fuzzy adaptive sliding mode controller based on a nonlinear two-point kinetics model for load-tracking of nuclear reactor

Reinforcement learning-driven nonsingular fast terminal sliding mode control for pipe crack sealing manipulator

Adaptive sliding mode control scheme for satellite detumbling using flexible rod with improved dynamic model

Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance

By considering the disturbance caused by ice floes in polar regions, the trajectory tracking control problem for uncertain unmanned surface vessels (USVs) is investigated in this paper. USVs for trajectory tracking missions in polar regions are required to not only overcome common disturbances and perturbations such as model uncertainties and environmental disturbances caused by winds, waves and currents, but it must also consider the stochastic resistance generated by ice floes. However, studies on the stochastic model of ice floes resistance on USVs are insufficient, making it difficult to a design tracking controller. This paper proposes a discrete integral sliding-mode control (DISMC) with a disturbance observer based on Gaussian process regression (GPR) technique, which could steer uncertain USVs to track predefined trajectories under disturbance without knowing its upper bound. Compared to the existing methods for USV control, (1) to the best of our knowledge, this study is among the first to address the trajectory tracking control problem of USVs in ice-floe sea conditions; (2) a novel fully data-driven disturbance observer is proposed that approximates the mean and autocorrelation function of the lumped uncertainties without requiring prior knowledge about the stochastic ice resistance; and (3) a novel DISMC given the autocorrelation function of uncertainties instead of the uncertain upper bound is proposed and proved to be stable with a probability of 1. The proposed method offers a significant approach for controlling USVs in ice-covered sea areas.

The performance of Photovoltaic (PV) power generation systems, using traditional Vector Current Controllers based on Proportional-Integral Action (CVC-PI), exhibits marked non-linearity and is strongly influenced by meteorological variations. Due to its robustness in the face of these challenges, Sliding Mode Control (SMC) has been widely used in this field, but SMC has certain inconveniences, notably the phenomenon of chattering and ripples in active (P) and reactive (Q) power. These limitations remain partially unresolved, even with the use of Second-order SMC (SMC-2). To address these limitations, this work proposes a robust, non-linear control based on the Third-order SMC in Dual Mode conversion (DM-SMC-3). To demonstrate the effectiveness of the proposed DM-SMC-3 control, a comparative study was carried out between DM-SMC-3, SMC-2, SMC and CVC-PI in terms of chattering reduction, dynamic response time, overshoot, P and Q power ripples and the Total Harmonic Distortion (THD) of the current injected into the electrical grid. The DM-SMC-3 is also used to guarantee Maximum Power Point Tracking (MPPT). The system is designed and simulated using Matlab/Simulink software. The simulation results demonstrated the significant contribution of the DM-SMC-3, with a 99% reduction in overshoot compared to other controllers. THD was reduced to 0.58%. The average reference tracking efficiency was 99.68%, and 99.41% under the temperature and irradiation conditions used. What’s more, the DM-SMC-3 takes just 35ms to reach the Maximum Power Point (MPP). It also reduced P and Q power ripples, demonstrating the robustness of the proposed controller in the face of chattering. • Development of Third Order Sliding Mode Controller (SMC3) for Improving the PV System. • Developed SMC3 method reduces current THD and chattering effects of classical SMC. • Comparison of the performance of the SMC3 with that of the other three controllers. • Validation of SMC3 robustness in face of changes in radiation and temperature. • Validation of SMC3’s robustness in the case of partial shading and a grid fault.

Autonomous Underwater Vehicles (AUVs) face challenges in maintaining stability during visual Simultaneous Localization and Mapping (vSLAM) operations, particularly when affected by internal solitary waves (ISWs). This study presents a novel integrated control strategy that combines a Nonlinear Disturbance Observer (NDO) and Nonlinear Model Predictive Control (NMPC), specifically adapted to address the nonlinear and unpredictable nature of ISW disturbances in underwater environments. Unlike previous NDO-NMPC implementations in other domains, this framework incorporates dynamic modeling of ISWs and underwater-specific tuning mechanisms to maintain robust vSLAM performance. To the best of our knowledge, this is the first study integrating NDO with NMPC for AUV-vSLAM under ISW disturbances. The proposed method is evaluated using a custom AUV model integrated with the ORB-SLAM2 framework, tested through Software-in-the-Loop (SITL) simulations under various ISW intensities. Results show that the NDO-NMPC algorithm outperforms traditional PID, Sliding Mode Control (SMC), and standalone NMPC controllers in terms of stability, trajectory tracking, and mapping accuracy. This approach reduces the impact of ISWs, improves the number of visual feature points for mapping, and achieves lower Root Mean Square Error (RMSE) in position and velocity. This work offers a robust solution for improving AUV navigation and mapping in dynamic underwater environments, with potential applications in autonomous underwater exploration and surveying.

Bioinspired flexible propulsion offers a promising approach for improving the efficiency and maneuverability of underwater robots. Inspired by the undulatory locomotion of median and/or paired fin organisms, this article presents a flapping propulsion system based on flexible pectoral fins fabricated using hydrogel materials. Coordinated actuation of multiple fin rays generates continuous traveling-wave deformation of the pectoral fins. To address the difficulty of establishing accurate hydrodynamic models for flexible flapping propulsion, a path following control framework combining offline data-driven modeling and online adaptive control is developed. Experimental measurements are used to establish the mapping between flapping motion parameters and hydrodynamic forces, which is implemented as a lookup-table-based feedforward compensation. An adaptive super twisting sliding mode control strategy is further incorporated to enhance robustness against external disturbances and measurement noise. Numerical simulations and pool experiments demonstrate the stability and reliability of the proposed approach for flapping-based path following. This work provides practical insights into the design, modeling, and robust control of flexible flapping propulsion systems.

Fuzzy logic sliding mode controller based solar PV fed UPQC for improvement of dynamic performance and power quality enhancement in distribution power system.

Ball balancers are nonlinear, electromechanical, multivariable, open-loop unstable systems widely used in research laboratories, aerospace, military, and automotive industries to evaluate control mechanism effectiveness. The inherent difficulty in precisely managing ball position, combined with actuator saturation and system sensitivity to disturbances, makes trajectory tracking a persistent challenge. Conventional controllers often exhibit oscillatory responses with steady-state errors exceeding acceptable limits. Sliding mode control (SMC) offers robustness against model uncertainties; however, chattering finite-frequency, finite-amplitude oscillations near the sliding surface caused by switching imperfections, time delays, and actuator dynamics remain a significant limitation. This study addresses chattering through explicit vibration model compensation integrated into the SMC design for a 2-DOF ball balancer system using a visual servoing approach. A double-loop control architecture is implemented, where the inner loop handles servo angular position control and the outer loop manages ball position tracking through visual servoing feedback. The sliding mode controller is designed with a power rate reaching law, synthesizing two control laws: one with explicit vibration model compensation incorporating damping and stiffness terms, and one without. Experimental validation confirmed that SMC with compensation achieved significantly reduced steady-state error (0.034 mm vs. 0.386 mm) and lower overshoot (3.95% vs. 13.81%) compared to the uncompensated variant, with chattering amplitude reduced by approximately 72%.

Thermoelectric generators (TEGs) enable compact waste-heat energy harvesting but require high-gain DC–DC conversion due to their low-output voltage for DC microgrid interfacing. This work proposes a novel TEG-supplied two-stage architecture consisting of a perturb-and-observe (P&O)-based MPPT boost converter followed by a modified Z-source converter regulated through an advanced model predictive control (MPC) framework. The modified Z-source topology enables high-voltage gain without extreme duty ratios and mitigates switching losses by eliminating diode reverse-recovery effects via synchronous operation. To enhance dynamic performance, the advanced MPC strategy incorporating an adaptive ripple-based weighting mechanism is applied to the modified Z-source converter and benchmarked against MPC and sliding mode control (SMC). Simulation results under multiple disturbance scenarios, including hot-side and cold-side temperature variations, multi-condition disturbances, coupling-factor variation, and measurement noise, demonstrate that the proposed system maintains stable 400 V regulation at a 100 W output level. In contrast, MPC exhibits switching frequency deviations that increase switching losses during transient operation, while SMC suffers from significant voltage deviations under source variations. The proposed strategy maintains tight voltage regulation with nearly fixed-frequency operation around 50 kHz, providing a new perspective for TEG researchers while supporting sustainable waste-heat energy utilization.

Abstract To address the control inaccuracy caused by varying load inertia in a rotary magazine, a joint state‐parameter estimation strategy is proposed. Based on the dual‐mass model considering transmission stiffness, extended state observers (ESOs) were respectively constructed on both the motor side and the load side to reconstruct the unmeasured states and reduce sensor reliance. Design an integral cost function with a forgetting factor to balance parameter tracking and noise suppression. Utilize the auxiliary matrix transformation to achieve a parameter error‐driven adaptive law, which can estimate parameters such as load and transmission stiffness online, and the limitation of persistent excitation conditions is weakened. The simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbances in the rotary magazine. Under five load conditions, positioning accuracy remains stable at 0.001 rad. Compared to PI control, tracking errors are reduced by 20% to 60%, with significantly attenuated oscillations relative to sliding mode control. These findings validate the algorithm's disturbance rejection capability in multi‐load inertia scenarios.

The Two-Layered Vertical Cable-Driven Parallel Robot for Machining Controlled by Adaptive Fuzzy Sliding Mode Controller

High−Altitude wind is a critical factor affecting the recovery safety of reusable rockets, significantly altering aerodynamic loads, flight attitudes, and trajectories—especially during the aerodynamic deceleration phase (engine shutdown) of reentry, posing severe challenges to high-precision guidance and stable control. Currently, accurate advance prediction of landing site wind fields is difficult with poor real-time performance, necessitating a real-time estimation and prediction method independent of additional measurement equipment. This study addresses this gap by proposing a deep learning-based approach for wind field estimation and prediction, using directly measurable attitude angles and apparent acceleration deviations of the rocket as inputs to train a dedicated deep neural network. Furthermore, to solve the attitude control problem of Reusable Launch Vehicles (RLVs) during recovery, a non-recursive simplified high-order sliding mode control method with online wind disturbance compensation is designed to achieve finite-time convergence. First, a dynamic model for the attitude control of RLVs during recovery is established; second, based on homogeneity theory, a non-recursive simplified homogeneous high-order sliding mode controller is developed to realize finite-time tracking control during RLV recovery with uncertainties, effectively suppressing the chattering inherent in sliding mode control; finally, simulation results verify the effectiveness and engineering feasibility of the proposed method. The combined approach significantly reduces wind-induced disturbance torque and required control torque, enhancing the adaptability and control robustness of vertically recoverable rockets to wind fields.

ABSTRACT To solve the problems of excessive overshoot, insufficient precision, and susceptibility to external disturbances in the speed control of permanent magnet synchronous motors (PMSM), a novel sensorless control method is proposed herein. First, a new exponential convergence law was designed and a new sliding mode speed controller was constructed based on this law. An optimisation algorithm combining genetic algorithms and particle swarm optimisation (GAPSO) is proposed to optimise the parameters of the sliding-mode speed controller. Second, inspired by the Cholesky triangular decomposition, the symmetric strong tracking extended Kalman filter (SSTEKF) algorithm is derived from the strong tracking extended Kalman filter (STEKF) algorithm. The experimental results show that the GAPSO method for optimising the sliding mode controller parameters reduces the overshoot and accelerates the convergence speed, improving the control effectiveness of the PMSM, particularly under sudden load changes. Compared with the STEKF algorithm, the improved SSTEKF algorithm has higher accuracy in estimating the rotor speed and position, with the error in estimating the rotor speed reduced from 0.3 to 0.2 and the error in estimating the rotor position reduced from 0.01 to 0.005. Even with sudden load changes, a better observation performance can be achieved.

Abstract In this study, a robust control strategy for greenhouse climate regulation is presented using an integral sliding mode controller (ISMC) tuned via fuzzy logic. To validate the effectiveness of our approach, a greenhouse model was used under varying conditions, with key system parameters altered by up to 30% to simulate real-world disturbances and uncertainties. The fuzzy logic-based tuning mechanism dynamically adjusted ISMC parameters, thereby enhancing its adaptability and robustness. The results demonstrate that the proposed controller successfully maintains stable performance with a 1-min rise time, 2-min settling time, and 20% overshoot, despite 30% parameter variations, effectively overcoming disturbances. Our work highlights the fuzzy-tuned ISMC’s ability to handle system uncertainties, providing a reliable solution for robust greenhouse climate control.