Search Results (158)

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

This work investigates resilient control for uncertain nonlinear systems subject to unknown and unpredictable sensor delays. Conventional observer-based delay-compensation methods typically require known delay bounds or measurable timing information, which limits their applicability to strongly nonlinear dynamics. To address this issue, a resilient adaptive fuzzy observer-based sliding control (AFOSMC) framework is developed. A generalized nonlinear plant model is considered, and an adaptive fuzzy observer is constructed to estimate unmeasured states while explicitly decomposing the delayed measurement residual into estimation and delay components. A sliding-mode controller integrated with fuzzy approximation ensures robust tracking in the presence of modeling uncertainties and delay-induced distortions. A delay-dependent Lyapunov function with an integral term is derived, yielding explicit conditions that guarantee uniform ultimate boundedness (UUB) of all closed-loop signals. The proposed approach provides a unified and delay-resilient solution for nonlinear observer–controller co-design under unpredictable sensing delays. Simulations on a Duffing oscillator with a $0.15$ s sensing delay show that the proposed AFOSMC model achieves a total tracking RMSE of $3.6\times {10}^{-2}$, whereas a baseline sliding-mode controller without delay compensation becomes unstable after delay activation. Full article

This paper introduces a novel control framework for prescribed-time synchronization of higher-order nonlinear multi-agent systems (MAS) subject to parametric uncertainties and external disturbances. The proposed method integrates a fuzzy neural network (FNN) with a robust non-singular terminal sliding mode controller (NTSMC) to ensure leader–follower consensus within a user-defined time horizon, regardless of the initial conditions. The FNN is employed to approximate unknown nonlinearities online, while an adaptive update law ensures accurate compensation for uncertainty. A terminal sliding manifold is designed to enforce finite-time convergence, and Lyapunov-based analysis rigorously proves prescribed-time stability and boundedness of all closed-loop signals. Simulation studies on a leader–follower MAS with four nonlinear agents under directed communication topology demonstrate the superiority of the proposed approach over conventional sliding mode control, achieving faster convergence, enhanced robustness, and improved adaptability against system uncertainties and external perturbations. Full article

This study addresses the challenge of designing an event-triggered observer for neural network-enhanced sliding mode control in nonlinear Takagi–Sugeno fuzzy Markov jump systems, where premise variables are not directly measurable. Firstly, for the purpose of state observer design, a dynamic event-triggered mechanism integrated with a neural network-based compensator is developed. Secondly, through the construction of an integral sliding surface, the dynamic behaviors of both the sliding mode and the error system are formulated, incorporating estimated premise parameters. Thirdly, rigorous stochastic stabilization criteria are established, incorporating $H_{\infty }$ disturbance attenuation with a specified level $\gamma$, while accounting for transition rates with general uncertainty characteristics. Subsequently, a fuzzy adaptive sliding mode control scheme is synthesized to ensure finite-time convergence of the system states to the predefined sliding surface. Finally, the effectiveness of the proposed control strategy is thoroughly validated through high-fidelity numerical simulations on a practical example. Full article

Permanent Magnet Synchronous Motors (PMSMs) exhibit inherent symmetry in their electromagnetic structure yet behave as nonlinear and strongly coupled systems that are susceptible to internal parameter perturbations and external disturbances, posing challenges to effective control under dynamic operating conditions. To address these issues, this paper proposes a sliding mode control strategy for PMSMs that integrates a Novel Adaptive Reaching Law (NARL) and an Improved Terminal Fuzzy Sliding Mode Disturbance Observer (IFTSMDO), denoted as SMC-NARL-IFTSMDO. The NARL is designed with a state-dependent dynamic gain adjustment mechanism and terminal attractive factor characteristics: it increases the gain to ensure fast convergence when the system state is far from the sliding mode surface, and adaptively attenuates the gain to suppress chattering when approaching the sliding mode surface, thereby balancing the contradiction between convergence speed and chattering in traditional sliding mode control. The IFTSMDO constructs a composite sliding mode surface incorporating error derivatives, terminal power terms, and saturation functions, which enhances the sensitivity of disturbance estimation in the small-error stage, avoids high-frequency chattering caused by sign functions, and provides accurate feedforward compensation for the speed loop controller to improve the system’s anti-disturbance capability. Additionally, the asymptotic stability of the proposed control strategy is strictly proven using the Lyapunov stability theory, laying a solid theoretical foundation for its application. Experiments are conducted on a TMS320F28379D DSP-based platform, and quantitative results show that compared with the traditional sliding mode control (SMC-TRL), the proposed strategy reduces the no-load startup response time by 60%, the steady-state speed fluctuation by 60%, and the speed fluctuation under load disturbance by 81.5%, fully demonstrating its superiority in dynamic response and anti-disturbance performance. Full article

This study proposes a fractional-order adaptive super-twisting sliding mode control (FO-ASTSMC) strategy to mitigate the difficulties arising from nonlinearity, uncertain parameters, and substantial external interferences during path-following operations of a hydraulic excavator working device. The developed approach merges a high-order sliding mode differentiator aimed at state observation, a fresh fractional-order sliding manifold that embeds a memory component for bolstering transient performance and equilibrium accuracy, together with an adaptable super-twisting coefficient. This adaptive gain eliminates the requirement for prior awareness of disturbance limits, all the while mitigating chattering effects and bolstering system robustness. Utilizing Lyapunov theory, the finite-time stability of the overall closed-loop framework has been thoroughly demonstrated. For controller verification, joint simulations employing AMESim and Simulink platforms were performed, pitting its efficacy against both terminal sliding mode control (TSMC) and adaptive fuzzy sliding mode control (AFSMC). In nominal scenarios, the FO-ASTSMC method yielded the lowest root mean square error (RMSE) along with maximum error (MAXE) across boom, arm, and bucket articulations, registering mean decreases of $60\%$ in RMSE and $58.2\%$ in MAXE when benchmarked against AFSMC, alongside $41.8\%$ in RMSE and $43.6\%$ in MAXE versus TSMC. Facing sudden variations in loading, it exhibited enhanced robustness, achieving reductions of $64.2\%$ in RMSE and $54.5\%$ in MAXE beyond AFSMC, as well as $39\%$ in RMSE and $36.5\%$ in MAXE in comparison to TSMC. Outcomes from the simulations affirm that the suggested controller exhibits elevated precision, formidable robustness, and good applicability to actuators, thereby highlighting its considerable promise for implementation in actual engineering scenarios. Full article

This paper proposes a novel intelligent switching control strategy for a five-bar lower limb exoskeleton. First, during the support phase, terminal sliding mode control (TSMC) is employed to ensure robust stability and high-torque amplification capabilities. Then, during the swing phase, a hybrid controller combining proportional-integral-derivative (PID) control and the adaptive neuro-fuzzy inference system (ANFIS) is implemented to generate natural and compliant leg movements. Finally, to achieve smooth transitions between phases, an intelligent switching algorithm based on multi-sensor information fusion is proposed. Simulation results demonstrate that the proposed strategy keeps trajectory tracking errors below 0.05 across all gait phases and achieves stable torque amplification ratios ranging from 1:6 to 1:10. This performance significantly reduces the user’s physical exertion. These findings validate the effectiveness of this control framework in improving the stability and comfort of human–machine interaction. Full article

A hybrid control architecture is proposed for enhancing the stability and energy management of DC microgrids (DCMGs) integrating photovoltaic generation, batteries, and supercapacitors. The approach combines nonlinear Sliding Mode Control (SMC) for fast and robust DC bus voltage regulation with a Deep Q-Learning (DQL) agent that learns optimal high-level policies for charging, discharging, and load management. This dual-layer design leverages the real-time precision of SMC and the adaptive decision-making capability of DQL to achieve dynamic power sharing and balanced state-of-charge levels across storage units, thereby reducing asymmetric wear. Simulation results under variable operating scenarios showed that the proposed method significantly improvedvoltage stability, loweredthe occurrence of deep battery discharges, and decreased load shedding compared to conventional fuzzy-logic-based energymanagement, highlighting its effectiveness and resilience in the presence of renewable generation variability and fluctuating load demands. Full article

Hybrid AC/DC microgrids have emerged as a promising solution for integrating diverse renewable energy sources, enhancing efficiency, and strengthening resilience in modern power systems. However, existing control schemes exhibit critical shortcomings that limit their practical effectiveness. Traditional linear controllers, designed around nominal operating points, often fail to maintain stability under large load and generation fluctuations. Optimization-based methods are highly sensitive to model inaccuracies and parameter uncertainties, reducing their reliability in dynamic environments. Intelligent approaches, such as fuzzy logic and ML-based controllers, provide adaptability but suffer from high computational demands, limited interpretability, and challenges in real-time deployment. These limitations highlight the need for robust control strategies that can guarantee reliable operation despite disturbances, uncertainties, and varying operating conditions. Numerical performance indices demonstrate that the reviewed robust control strategies outperform conventional linear, optimization-based, and intelligent controllers in terms of system stability, voltage and current regulation, and dynamic response. This paper provides a comprehensive review of recent robust control strategies for hybrid AC/DC microgrids, systematically categorizing classical model-based, intelligent, and adaptive approaches. Key research gaps are identified, including the lack of unified benchmarking, limited experimental validation, and challenges in integrating decentralized frameworks. Unlike prior surveys that broadly cover microgrid types, this work focuses exclusively on hybrid AC/DC systems, emphasizing hierarchical control architectures and outlining future directions for scalable and certifiable robust controllers. Also, comparative results demonstrate that state of the art robust controllers—including H∞-based, sliding mode, and hybrid intelligent controllers—can achieve performance improvements for metrics such as voltage overshoot, frequency settling time, and THD compared to conventional PID and droop controllers. By synthesizing recent advancements and identifying critical research gaps, this work lays the groundwork for developing robust control strategies capable of ensuring stability and adaptability in future hybrid AC/DC microgrids. Full article

Global agriculture is confronting unprecedented pressures from population growth, diminishing arable land, and severe rural labor scarcity, necessitating the advancement of intelligent agricultural equipment. As a core component of precision farming, unmanned agricultural tractors demand highly accurate and robust path tracking control. However, conventional methods often fail to cope with unstructured terrain and dynamic wheel slip under real field conditions. This paper proposes an extended state observer (ESO)-based improved Stanley guidance law, which incorporates real-time sideslip angle observation, adaptive preview-based path curvature compensation, and a sliding mode heading controller. The ESO estimates lateral slip caused by varying soil conditions, while the modified Stanley law utilizes look-ahead path information to proactively adjust the desired heading angle during high-curvature turns. Both co-simulation in Matlab-Carsim and field experiments demonstrate that the proposed method significantly reduces lateral tracking error and overshoot, outperforming classical algorithms such as fuzzy Stanley and sliding mode controller, especially in U-turn scenarios and under low-adhesion conditions. Full article

With the rapid development of the electric vehicle (EV) industry, the regenerative braking system (RBS) has become a pivotal technology for enhancing overall vehicle energy efficiency and safety. This article systematically reviews recent research advances, spanning macro-architecture, drive and energy-storage hardware, control strategies, and evaluation frameworks. It focuses on comparing the mechanisms and performance of six categories of intelligent control algorithms—fuzzy logic, neural networks, model predictive control, sliding-mode control, adaptive control, and learning-based algorithms—and, leveraging the structural advantages of four-wheel independent drive (4WID) electric vehicles, quantitatively analyzes improvements in energy-recovery efficiency and coordinated vehicle-dynamics control. The review further discusses how high-power-density motors, hybrid energy storage, brake-by-wire systems, and vehicle-road cooperation are pushing the upper limits of RBS performance, while revealing current technical bottlenecks in high-power recovery at low speeds, battery thermal safety, high-dimensional real-time optimization, and unified evaluation standards. A closed-loop evolutionary roadmap is proposed, consisting of the following stages: system integration, intelligent control, scenario prediction, hardware upgrading, and standard evaluation. This roadmap emphasizes the central roles of deep reinforcement learning, hierarchical model predictive control (MPC), and predictive energy management in the development of next-generation RBS. This review provides a comprehensive and forward-looking reference framework, aiming to accelerate the deployment of efficient, safe, and intelligent regenerative braking technologies. Full article

Facing global pressures such as population growth, shrinking arable land, and climate change, intelligent agriculture has emerged as a critical pathway toward sustainable and efficient agricultural production. Control algorithms serve as the core enabler of this transition, finding applications in crop production, pest management, agricultural machinery, and resource optimization. This review systematically examines the performance and applications of both traditional (e.g., PID, fuzzy logic) and advanced control algorithms (e.g., neural networks, model predictive control, adaptive control, active disturbance rejection control, and sliding mode control) in agriculture. While traditional methods are valued for simplicity and robustness, advanced algorithms better handle nonlinearity, uncertainty, and multi-objective optimization, enhancing both precision and resource efficiency. However, challenges such as environmental heterogeneity, hardware limitations, data scarcity, real-time requirements, and multi-objective conflicts hinder widespread adoption. This review contributes a structured, critical synthesis of these algorithms, highlighting their comparative strengths and limitations, and identifies key research gaps that distinguish it from prior reviews. Future directions include lightweight algorithms, digital twins, multi-sensor integration, and edge computing, which together promise to enhance the scalability and sustainability of intelligent agricultural systems. Full article

The ground taxiing phase is a crucial stage for the autonomous takeoff and landing of fixed-wing unmanned aerial vehicles (UAVs), and its trajectory tracking accuracy and stability directly determine the success of the UAV’s autonomous takeoff and landing. Therefore, researching the adaptive robust optimal control technology for UAV taxiing is of great significance for enhancing the autonomy and environmental adaptability of UAVs. This study integrates the linear quadratic regulator (LQR) with sliding mode control (SMC). A compensation control signal is generated by the SMC to mitigate the potential effects of uncertain parameters and random external disturbances, which is then added onto the LQR output to achieve a robust optimal controller. On this basis, through ANFIS (Adaptive Neuro-Fuzzy Inference System), the nonlinear mapping relationship between multiple state parameters such as speed, lateral/heading deviation and the weight matrix of the LQR controller is learned, realizing a data-driven adaptive adjustment mechanism for controller parameters to improve the tracking accuracy and anti-interference stability of the UAV’s taxiing trajectory. Full article

We propose a control method that integrates adaptive fuzzy sliding-mode control (AF-SMC) with a fixed-time disturbance observer (FTDO) to address modeling errors, external disturbances, and input saturation in ship path tracking. The designed adaptive fuzzy system dynamically adjusts the SMC gain to enhance adaptability to parameter variations and modeling errors. Furthermore, the proposed method enables rapid estimation of the total uncertainty term by incorporating an FTDO, ensuring fixed-time estimation and feedforward compensation of the total matched uncertainty without requiring prior knowledge of the disturbance bound. Lyapunov stability analysis was employed to verify the bounded stability of the closed-loop system. Simulation results indicate that the proposed method provides high control accuracy and robustness. Full article

A key trade-off persists in the control of permanent magnet synchronous motors (PMSMs): achieving fast finite-time convergence often exacerbates control chattering, while conventional chattering-suppression methods can compromise the system’s dynamic response. The existing literature often addresses these challenges in isolation. The core original contribution of this research lies in proposing a novel robust fuzzy adaptive control scheme that effectively resolves this trade-off through a synergistic design. The contributions are as follows: (1) A novel reaching law is formulated to significantly accelerate error convergence, achieving finite-time stability and improving upon conventional reaching law designs. (2) A super-twisting sliding mode observer is integrated into the control loop, providing accurate real-time estimation of load torque disturbances, which is used for feedforward compensation to drastically improve the system’s disturbance rejection capability. (3) A fuzzy adaptive mechanism is developed to dynamically tune key gains in the sliding mode law. This approach effectively suppresses chattering without sacrificing response speed, enhancing system robustness. (4) The stability and convergence of the proposed controller are rigorously analyzed. Simulations, comparing the proposed method with conventional adaptive sliding mode control (ASMC), demonstrate its marked superiority in control accuracy, transient behavior, and disturbance rejection. This work provides an integrated solution that balances rapidity and smoothness for high-performance motor control, offering significant theoretical and engineering value. Full article