Search Results (1,412)

Electromechanical actuators play a pivotal role in aerospace servo systems; however, their high-precision tracking performance is frequently compromised by external disturbances and system nonlinearities. To address these challenges, this paper proposes a disturbance observer-based fixed-time backstepping sliding-mode control strategy. Firstly, the high-order dynamics are decomposed into load and electrical subsystems employing a backstepping control framework. To effectively handle mismatched external disturbances in the load subsystem, a prescribed-time integral sliding-mode observer is designed, which guarantees accurate disturbance estimation within a prescribed time for feedforward compensation. Subsequently, a fixed-time sliding-mode controller incorporating a segmented reaching law is developed. This controller ensures that tracking errors converge to zero within a fixed time, independent of initial system states, while mitigating chattering. Hardware-in-the-loop experimental results demonstrate the superior performance of the proposed strategy. Compared to conventional methods, the proposed controller significantly enhances transient response under step disturbances by reducing the peak deviation by up to 94% and shortening the recovery time by at least 60%. Furthermore, under sustained sinusoidal disturbances and dynamic tracking scenarios, the output fluctuations and tracking errors are attenuated to negligible levels, thereby exhibiting notable improvements over traditional methods. Full article

To satisfy the stringent functional-safety requirements of steer-by-wire steering systems for advanced autonomous driving, this paper proposes a novel dual-motor collaborative fault-tolerant control strategy. The proposed approach aims to overcome the insufficient fault tolerance of conventional single-motor architectures, as well as the limited dynamic response and disturbance-rejection capability observed in existing multi-motor schemes. The key contribution is an integrated control framework consisting of two components: (i) dual-motor torque synchronization achieved via a fuzzy-PID–based mean-deviation coupling method, and (ii) a super-spiral sliding-mode control law optimized by an adaptive differential-evolution algorithm to enhance the dynamic performance and robustness of the current loop. Experimental results demonstrate that, relative to a non-synchronized baseline, the proposed strategy reduces the inter-motor current mismatch by 8.1%–78.6% across multiple operating conditions. Moreover, following fault occurrence, the proposed Self-Adaptive Differential-Evolution-algorithm-based Super-Twisting Sliding-Mode Control method shortens the stabilization time by 50%–70%, 9%–20%, and 16.7% compared with conventional PID, Super-Twisting Sliding-Mode Control methods, and classical $H_{\infty }$ robust control, respectively. Overall, the developed solution meets functional-safety requirements and provides a highly reliable steering-actuation mechanism for advanced autonomous driving applications. Full article

Multi-robot collaboration and marine robotics constitute key research directions in intelligent autonomous systems. In this context, multi-ROV cooperative operations are increasingly deployed for sunken ship search missions. A central technical challenge in such applications is to ensure efficient, non-redundant coverage while maintaining accurate formation tracking. This scenario confronts two principal difficulties. First, overlapping operational regions among multiple ROVs tend to produce both redundant coverage and search blind zones. Second, trajectory tracking accuracy is significantly degraded by the combined effects of hydrodynamic disturbances and inherent actuator constraints in ROVs. To address these challenges, an improved dynamic window approach (DWA), incorporating a search distance penalty mechanism, is proposed for multi-ROV trajectory planning. Concurrently, a cascaded tracking control architecture is constructed, wherein a model predictive kinematic controller generates constrained velocity references, while an adaptive sliding mode dynamic controller augmented with an extended state observer provides robust disturbance rejection. Collaborative search is conducted using a three-ROV leader–follower formation. Simulation results indicate that regional search coverage is effectively improved and areas of repeated detection are significantly reduced by the proposed planning algorithm. Real-time trajectory tracking is achieved by the designed controller under two typical extreme strong disturbance conditions, namely, time-varying disturbances and abrupt disturbances, on the premise of satisfying thruster thrust constraints. The proposed scheme enables all three ROVs to successfully complete the tracking task under time-varying disturbances while reducing the frequency of thrust saturation events by up to seven times. In contrast, under the conventional MPC–ASMC controller, one ROV deviates from the formation and fails to complete the tracking task. Under abrupt disturbances, the proposed approach reduces the trajectory tracking error by up to six times and decreases the frequency of thrust saturation events by up to four times. Full article

The control of Twin-Rotor Multi-Input Multi-Output (TRMS) systems presents a significant challenge due to high nonlinearity, strong aerodynamic cross-coupling, and the inevitable discrepancies between theoretical models and physical plants. This paper first exposes the instability of conventional Backstepping control under real hardware conditions, where unmodeled dynamics and parametric uncertainties drive the yaw subsystem into divergent oscillation, then proposes and experimentally validates two advanced architectures to overcome this limitation. The first is an online adaptive Backstepping gain-tuning scheme based on a novel Rate-Constrained Sequential Quantum Black Hole Optimization (RS-QBHO) algorithm. The second is a Hybrid Backstepping–Sliding Mode Control (BSMC) architecture that integrates structural disturbance rejection directly into the recursive design. Both schemes are formally verified via Lyapunov stability analysis and validated on a physical TRMS rig under identical hardware-in-the-loop conditions. Experimental results confirm that while the standard Backstepping controller failed in the yaw axis with an RMSE of 2.5624 rad, both proposed methods achieved stabilization. The QBHO-tuned controller yielded RMSE values of 0.0799 rad for pitch and 0.2305 rad for yaw, while the BSMC strategy proved superior, achieving 0.0682 rad and 0.1858 rad, respectively. These findings demonstrate that while meta-heuristic optimization effectively compensates for parametric mismatches, the passive disturbance rejection of the sliding mode term offers a more effective solution for mitigating unmodeled aerodynamic dynamics in MIMO flight platforms. Full article

This paper introduces the Quantum-Inspired Impulsive Continuous Hopfield Network (Q-ICHN), a novel hybrid control framework designed to handle non-smooth, high-energy perturbations in nonlinear dynamical systems. Standard Continuous Hopfield Networks (CHNs) rely on sigmoidal activation functions that are prone to gradient saturation, which leads to an insufficient corrective response when the system undergoes large deviations from equilibrium. To overcome this shortcoming, the proposed Q-ICHN adopts a wave-packet-based activation function grounded in the stationary Schrödinger equation, yielding a non-monotonic and oscillatory activation profile that sustains effective compensatory dynamics across a broad range of states. Furthermore, the proposed framework incorporates Madelung’s quantum potential into the control architecture, thereby enabling a fundamental reshaping of the system’s energy landscape. Specifically, this induces a tunneling-like mechanism that allows the system to circumvent local minima and rapidly recover from impulsive disturbances, manifested as a sharpened attractor structure in the phase-space domain. Together, these properties yield enhanced convergence behavior and improved robustness over traditional neural control approaches. To rigorously assess its merits, the performance of the Q-ICHN is evaluated through a large-scale benchmark involving 20 established control methods, including Sliding Mode Control (SMC), Model Predictive Control (MPC), and Backstepping. The experimental results obtained across 20 heterogeneous scenarios demonstrate that the proposed model achieves a 48% reduction in Mean Squared Error (MSE) relative to the classical ICHN. In addition, the Q-ICHN exhibits improved smoothness, reflected in a 30% reduction in jerk with respect to high-gain robust controllers, and enhanced reliability, validated by superior spectral purity and a 34% reduction in integrated variance under stochastic perturbations. Collectively, these results underscore the potential of quantum-inspired activation mechanisms to favorably balance control responsiveness and harmonic stability, providing a robust framework for handling both continuous dynamics and impulsive effects. Full article

This paper proposes a co-design methodology for the power and control stages of a bidirectional battery charger/discharger based on a boost converter topology. The approach ensures safe operation by limiting the battery current derivative, preventing abrupt transients that could degrade battery lifespan. The control strategy combines a cascade structure with an inner sliding mode current controller (for robustness and fast response) and an outer adaptive PI voltage loop (to regulate the DC-link voltage under varying load conditions). Additionally, the design constrains the switching frequency to reduce power losses. Experimental validation on a prototype converter demonstrates the effectiveness of the co-design framework, showing precise current/voltage regulation, adherence to switching frequency limits, and compliance with battery charging/discharging requirements. The results highlight the methodology’s potential to enhance efficiency and reliability in energy storage systems. The dynamic restrictions, overshoot lower than 5%, settling time shorter than 5 ms, and a battery current limitation less than 50 A/ms were always met with SMC and, in some cases, with the PI controller, but the results with SMC were always better: lower overshoot, shorter settling time, and greater restriction on the derivative of the battery current. In addition, the SMC system was 2.5–5.0% more efficient than the PI controller. Full article

To address the deteriorated control performance of permanent magnet synchronous motor (PMSM) drive systems for new energy vehicles (NEVs) under complex conditions caused by multi-source disturbances (internal parameter perturbations and external load mutations), this paper proposes an improved terminal integral sliding mode control (ITISMC-ADERL) strategy integrating a piecewise adaptive terminal integral sliding mode surface and an ADERL. The proposed sliding mode surface adopts interval-adaptive switching between high- and low-order power terms, completely eliminating singularity and integral saturation defects of traditional terminal sliding mode control while ensuring fast convergence, and achieving an optimal structural balance between convergence speed and chattering suppression. The state-dependent ADERL leverages the synergy of error-sliding variable coupled dynamic gain adjustment and variable exponential power compensation, realizing dual-mode adaptive switching of “strong driving for fast approaching far from the sliding surface, weak gain for smooth regulation near the sliding surface”, which significantly improves control accuracy and anti-disturbance robustness. The finite-time convergence of the closed-loop system is rigorously proved via Lyapunov stability theory. Full-operating-condition comparative tests on a TMS320F28379D DSP platform show that the proposed strategy outperforms SMC-ERL, ISMC-ERL and ITISMC-ERL in all test scenarios (no-load startup, acceleration/deceleration, sudden load changes, flux linkage perturbation), meeting the requirements of high-performance NEV drive systems and possessing important engineering application potential. Full article

In order to protect the high-sensitivity optical lens of the “magnetic field and velocity field imager” in extreme deep space environments, this paper proposes a new type of dual redundant planetary differential lens cover drive mechanism. In view of the critical vulnerability that traditional single-motor direct drive is prone to sudden mechanical jamming and catastrophic single-point failure (SPF) in severe tasks such as Jupiter exploration, this study constructs a “dual input single output (DISO)” rigid decoupling architecture from the perspective of physical topology. Through theoretical analysis and kinematic modeling, the adaptive decoupling mechanism of the two-degree-of-freedom (2-DOF) system under unilateral mechanical stalling is revealed. Dynamic analysis shows that in the nominal dual-motor synergy mode, the system shows a significant “kinematic load-sharing effect”, thus greatly reducing the sliding friction and gear wear rate. In addition, under the severe dynamic fault injection scenario (maximum gravity deviation and sudden jam superposition of a single motor), the cold standby motor is activated and the dynamic takeover is quickly performed. The high-fidelity transient simulation based on ADAMS verifies that although the fault will produce transient global torque spikes and pulsed internal gear contact forces at the moment, all extreme dynamic loads remain well within the structural safety margin. The output successfully achieved a smooth transition, which is characterized by a non-zero-crossing velocity recovery. This research provides an innovative theoretical basis and a practical engineering paradigm for the design of high-reliability fault-tolerant mechanisms in deep space exploration. Full article

This article is an in-depth analysis of the performance and efficiency of various control systems used in quadrotor unmanned aerial vehicles (UAVs). The study is focused on the comparison of three main control approaches, including Sliding Mode Control (SMC), Fuzzy Logic Control (FLC), and an extended version of Sliding Mode Control with the use of the Gray Wolf Optimizer (SMC-GWO), as well as a supportive validation model the Genetic Algorithm (SMC-GA). Based on the Newton–Euler formulation, the mathematical model of a quadrotor has been developed to provide a true picture of the dynamic behavior of the quadrotor. The model was then implemented in MATLAB/Simulink 2025b to test the performance of the system in its nominal and perturbed conditions. The findings have shown that the hybrid SMC-GWO controller has significant improvement in response speed, accuracy, and stability compared to the other controllers. Precisely, the SMC-GWO demonstrated 78.46 percent decrease in rise time and 23.40 percent decrease in settling time compared to the traditional SMC, as well as a nearly negligible steady-state error (SSE = 0.0008) in the roll channel. The proposed controller in the pitch channel reduced the rise time by 93.65 percent and the settling time by 20.22 percent, with a much smoother and more stable tracking and an effectively negligible steady-state error (SSE = 0.0001). The hybrid controller in the yaw channel had a 77.94 percent better rise time and 23.16 percent better settling time, resulting in a steady-state error of 0.0022. In relation to altitude control, SMC -GWO decreased the rise time by 91.87 percent and settling time by 25.04 percent over classical SMC, yet the steady-state error was almost zero. Under constant, time-varying actuator disturbances, the SMC-GWO controller also demonstrated better system stabilization and trajectory-tracking behavior than both SMC and FLC, as well as slightly better behavior than SMC-GA in the presence of faults and disturbances. These results verify that a UAV control framework based on the combination of the Gray Wolf Optimizer and Sliding Mode Control is more resilient, quick, and significantly more precise. Full article

The torque ripples of robotic permanent magnet synchronous motors (PMSMs) degrade motion smoothness and positioning accuracy of the system, while inevitable load transients in robotic tasks further complicate torque ripple attenuation. To address this issue, this paper develops an event-triggered torque ripple attenuation method that explicitly distinguishes torque ripple from dynamic load transients. First, a sliding-mode torque observer is constructed to obtain real-time torque information, whose stability is rigorously analyzed using a Lyapunov function. Second, frequency-selective torque ripple extraction schemes are proposed to accurately isolate steady-state high-frequency torque ripple from the estimated torque signal. In particular, two specially designed filtering structures are developed and compared, one of which is selected to preserve ripple-related frequency content during test, ensuring robust and accurate ripple identification under varying operating conditions in robotics. Third, a torque-ripple-regulation-based compensation strategy is used within a vector-controlled PMSM drive, in which the extracted torque ripple is processed by a dedicated ripple regulator to generate voltage compensation signals. This strategy achieves effective steady-state torque ripple attenuation with low implementation complexity, while avoiding performance degradation during dynamic load transients. Finally, experimental results are provided to validate the effectiveness of the proposed methods. Full article

To address the significant degradation of steering performance in dual-motor steer-by-wire (DMSBW) systems caused by the coupling of communication delays and motor faults, a robust fault-tolerant control strategy is proposed under the dual-motor collaborative driving mode. First, a matrix polytopic model is employed to describe the nonlinearities introduced by delays, establishing a delay-dependent DMSBW system dynamics model. Second, for electrical faults such as internal motor short circuits that cause a sudden drop in rotational speed, an adaptive motor-switching fault-tolerant mechanism is designed based on a smooth monitoring function to achieve rapid fault detection and steering function reconstruction. Furthermore, considering the coupled impact of delays and faults, a robust linear quadratic regulator (LQR) controller with feedforward compensation is designed to enhance system fault tolerance and robustness. Simulation results demonstrate that under steering wheel angle step input with delays, the proposed strategy achieves a rapid response without significant overshoot, and the steady-state tracking error is significantly reduced. In variable-speed single lane change maneuvers with coupled delays and severe motor faults, the peak and root mean square (RMS) errors of the front wheel angle are reduced to 0.0112 rad and 0.0031 rad, respectively. Compared to the delay-compensated nonlinear model predictive control (NMPC) and sliding mode control (SMC) strategies that do not account for delays, the peak error is reduced by 15.79% and 45.37%, while the RMS error decreases by 27.91% and 35.42%, respectively. Additionally, the peak and RMS errors of the sideslip angle and yaw rate are substantially reduced, validating the strategy’s excellent steering fault tolerance, robustness, and vehicle handling stability. Full article

This paper proposes a novel adaptive sliding mode control strategy for bionic aerial manipulators performing canopy-sampling tasks. Specifically, an adaptive neural barrier function-based fast terminal sliding mode control (BFASMC-NN) scheme is developed to address the joint-space trajectory tracking problem by integrating fast continuous nonsingular terminal sliding mode control (FNTSMC), neural networks (NNs), and barrier functions (BFs). The aerial manipulator is modeled as a rootless system, and its kinematic and dynamic characteristics are analyzed separately. Radial basis function neural networks (RBF-NNs) are introduced to approximate lumped disturbances, while BFs are incorporated to mitigate the effects of joint input saturation. Meanwhile, FNTSMC is employed to guarantee finite-time convergence of the system states. The stability of the closed-loop system is rigorously proven based on Lyapunov stability theory. Two simulation studies are conducted to validate the proposed method, and the results demonstrate that it achieves stronger disturbance rejection capability, faster convergence, and higher tracking accuracy than existing approaches. Full article

This paper presents an adaptive optimal consensus tracking control scheme for canonical nonlinear multi-agent systems (MASs) with unknown dynamics, employing an actor–critic reinforcement learning (RL) framework. The scheme integrates a sliding mode mechanism to suppress tracking errors and ensure consensus tracking between the followers and the leader. Additionally, optimal control is designed to find a Nash equilibrium in a graphical game. To address the intractability of obtaining an analytical solution for the coupled Hamilton–Jacobi–Bellman (HJB) equation, a policy iteration algorithm is utilized. Within this algorithm, a critic neural network (NN) approximates the gradient of the optimal value function, while an actor NN approximates the optimal control policy. Together, these networks form a compact actor–critic (AC) architecture that achieves optimal consensus tracking. Furthermore, the proposed method guarantees the boundedness of all closed-loop signals while ensuring consensus tracking. Finally, two simulations are conducted to verify the effectiveness and advantages of the proposed method. Full article

To address trajectory-tracking degradation of unmanned surface vehicles (USVs) in constrained waters caused by model uncertainty, strong environmental disturbances, and actuator limitations, this paper proposes a robust disturbance-observer-based optimization model predictive control method. First, a nonlinear tracking error model is established for a 3-DOF USV by incorporating environmental loads, parametric perturbations, and unmodeled dynamics into the kinematic and dynamic equations. Based on this model, a prediction model suitable for model predictive control is derived through linearization and discretization. Then, to estimate complex unknown disturbances online, a robust disturbance observer integrating a radial basis function neural network (RBFNN) with an adaptive sliding-mode mechanism is developed, enabling real-time approximation and compensation of lumped disturbances in the surge and yaw channels. Furthermore, to overcome actuator saturation caused by the direct superposition of feedforward compensation and feedback control in conventional composite strategies, a dynamic constraint reconstruction mechanism is introduced. By feeding the observer-generated compensation signal back into the MPC optimizer, the feasible control region is updated online so that the total control input satisfies both magnitude and rate constraints of the propulsion system. Theoretical analysis based on Lyapunov theory proves the uniform ultimate boundedness of the observation errors and neural-network weight estimation errors, while input-to-state stability theory is employed to establish closed-loop stability. Comparative simulations under sinusoidal trajectories, time-varying curvature paths, and large-maneuver turning conditions demonstrate that the proposed method significantly improves tracking accuracy, disturbance rejection capability, and control feasibility under severe disturbances and parameter mismatch. Full article

Light-responsive hydrogel-based oscillators typically exhibit small oscillation amplitudes because solvent diffusion is intrinsically slow, and their dependence on external periodic light modulation further results in limited amplitude, poor stability, and insufficient autonomy. Inspired by the trigger and sliding mechanism of the ancient crossbow, this study introduces an innovative system that integrates a sliding-block mechanism with time-delay feedback, breaking from conventional approaches that rely on hydrogel inertia or external modulation, within a purely theoretical and simulation-based framework. By establishing a nonlinear dynamic model coupling solvent diffusion, photoisomerization, and optical attenuation, this research shows through numerical simulations that the system can exhibit two distinct modes under constant illumination: a stable state and a self-sustained oscillatory state. The model predicts that the oscillation frequency can be flexibly tuned by varying key parameters, including the crosslinking density, Flory–Huggins interaction parameters of the spiropyran and hydrophilic polymer, ring-opening reaction rate, light intensity, fraction of light-sensitive molecules, and sliding displacement, whereas the initial absorption coefficient has only a minor influence. The slider displacement is also identified as an effective means to regulate the oscillation amplitude. Furthermore, the expansion force at the container bottom is predicted to oscillate synchronously with the hydrogel’s volume change. This theoretical framework represents a paradigm shift from “static small deformation” to “dynamic large-amplitude oscillation”, significantly enhancing the mechanical responsiveness of the material. This work provides a novel and controllable strategy for the conceptual design of autonomous light-driven micromechanical systems. Full article