Search Results (1,786)

In recent years the use of delayed controllers has increased considerably, since they can attenuate noise, replace derivative actions, avoid the construction of observers, and reduce the use of extra sensors, while maintaining inherent insensitivity to high-frequency noise. Therefore, it is important to continue improving the tuning of these controllers, including properties such as performance, fragility and robustness that may be beneficial for this purpose. However, currently most studies prioritize tuning using only the performance property, some others only the fragility property, and some less only the robustness property. This work provides the first rigorous joint analysis of performance, fragility, and robustness for a class of systems whose characteristic equation is a quasi-polynomial of degree two, filling a gap in the current literature. Thus, necessary and sufficient conditions are proposed to improve the tuning of delayed-action controllers by ensuring a exponential decay rate on the convergence of the closed-loop system response (performance) and by ensuring stabilization and/or trajectory tracking in the face of changes in system parameters (robustness) and controllers gains (fragility). To illustrate and corroborate the effectiveness of the proposed theoretical results, a real-time implementation is presented on a mobile prototype consisting of an omnidirectional mobile robot, to streamline/guarantee trajectory tracking in response to variations in controller gains and robot parameters. This implementation and application of theoretical results are possible thanks to the proposal of a novel delayed nonlinear controller and some simple but strategic algebraic manipulations that reduce the original problem to the study of a quasi-polynomial of degree 9 with three commensurable delays. Finally, our results are compared with a classical proportional nonlinear controller showing that our proposal is relevant. Full article

Motor impairments significantly affect individuals’ ability to perform activities of daily living, reducing autonomy and quality of life. In response to this, robot-assisted rehabilitation has emerged as an effective and practical solution, enabling controlled limb movements and supporting functional recovery. This study presents the development of an upper-limb exoskeleton designed to assist rehabilitation by integrating neurophysiological signal processing and real-time control strategies. The system incorporates a proportional–derivative (PD) controller to execute cyclic flexion and extension movements based on a sinusoidal reference signal, providing repeatability and precision in motion. The exoskeleton integrates a brain–computer interface (BCI) that utilizes electroencephalographic signals for therapy selection and engagement enabling user-driven interaction. The EEG data extraction was possible by using the UltraCortex Mark IV headset, with electrodes positioned according to the international 10–20 system, targeting alpha-band activity in channels O1, O2, P3, P4, Fp1, and Fp2. These channels correspond to occipital (O1, O2), parietal (P3, P4), and frontal pole (Fp1, Fp2) regions, associated with visual processing, sensorimotor integration, and attention-related activity, respectively. This approach enables a more adaptive and personalized rehabilitation experience by allowing the user to influence therapy mode selection through real-time feedback. Experimental evaluation across five subjects showed an overall mean accuracy of 86.25% in alpha wave detection for EEG-based therapy selection. The PD control strategy achieved smooth trajectory tracking with a mean angular error of approximately 1.70°, confirming both the reliability of intention detection and the mechanical precision of the exoskeleton. Also, our core contributions in this research are compared with similar studies inspired by the rehabilitation needs of stroke patients. In this research, the proposed system demonstrates the potential of integrating robotic systems, control theory, and EEG data processing to improve rehabilitation outcomes for individuals with upper-limb motor deficits, particularly post-stroke patients. By focusing the exoskeleton on a single degree of freedom and employing low-cost manufacturing through 3D printing, the system remains affordable across a wide range of economic contexts. This design choice enables deployment in diverse clinical settings, both public and private. Full article

Wheel slipping during trajectory tracking presents significant challenges for wheeled mobile robots (WMRs), degrading accuracy and stability on low-friction or dynamic terrain. Effective control requires addressing unknown slipping parameters while balancing tracking precision and energy efficiency. To address this challenge, a control framework integrating a sliding mode observer (SMO), an improved particle swarm optimization (PSO) algorithm, and a linear quadratic regulator (LQR) is proposed. First, a dynamic model incorporating longitudinal slipping is established. Second, an SMO is designed to estimate the slipping ratio in real-time, with chattering suppressed using a low-pass filter. Finally, an improved PSO algorithm featuring a nonlinear cosine-decreasing inertia weight strategy optimizes the LQR weighting matrices (Q/R) online to both minimize tracking errors and control energy consumption. Simulations including both circular and sine wave trajectories demonstrate that the SMO achieves rapid and accurate slipping ratio estimation, while the PSO-optimized LQR significantly enhances tracking accuracy, achieves smoother control inputs, and maintains stability under varying slipping conditions. Full article

In high-precision fields such as advanced manufacturing, semiconductor processing, aerospace assembly, and precision machining, motion control systems often face challenges such as large tracking errors and low control efficiency due to complex dynamic environments. To address this, this paper innovatively proposes a data-driven feedforward compensation control strategy based on a Parallel Gated Recurrent Unit (GRU)–Transformer. This method does not require an accurate model of the controlled object but instead uses motion error data and controller output data collected from actual operating conditions to complete network training and real-time prediction, thereby reducing data requirements. The proposed feedforward control strategy consists of three main parts: first, a Parallel GRU–Transformer prediction model is constructed using real-world data collected from high-precision sensors, enabling precise prediction of system motion errors after a single training session; second, a nonlinear PD controller is introduced, using the prediction errors output by the Parallel GRU–Transformer network as input to generate the primary correction force, thereby significantly reducing reliance on the main controller; and finally, the output of the nonlinear PD controller is combined with the output of the main controller to jointly drive the precision motion platform. Verification on a permanent magnet synchronous linear motor motion platform demonstrates that the control strategy integrating Parallel GRU–Transformer feedforward compensation significantly reduces the tracking error and fluctuations under different trajectories while minimizing moving average (MA) and moving standard deviation (MSD), enhancing the system’s robustness against environmental disturbances and effectively alleviating the load on the main controller. The proposed method provides innovative insights and reliable guarantees for the widespread application of precision motion control in industrial and research fields. Full article

The widespread application of unmanned vehicles in logistics distribution and special transportation has made improving trajectory tracking accuracy and dynamic adaptability critical for operational efficiency. This article proposes a biologically inspired combination control strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm, enhanced by the Crayfish Optimization Algorithm (COA) to address limitations in generalization and dynamic adaptability. The proposed DDPG-COA controller embodies a symmetrical structure: DDPG acts as the primary controller for global trajectory tracking, while COA serves as a compensatory regulator, dynamically optimizing actions through a disturbance observation mechanism. This symmetrical balance between learning-based control (DDPG) and bio-inspired optimization (COA) ensures robust performance in complex scenarios. Experiments on symmetrical trajectories demonstrated significant improvements, with the average tracking errors reduced by 56.3 percent, 71.6 percent, and 74.6 percent, respectively. The results highlight how symmetry in control architecture and trajectory design synergistically enhances precision and adaptability for unmanned systems. Full article

Aiming at the trajectory tracking control problem of wheeled mobile robots under unknown slip ratio conditions, this paper designs a trajectory tracking controller based on an improved rapid variable power reaching law and a sliding mode observer. First, a kinematic model of the wheeled mobile robot is established, explicitly considering the influence of slip ratio. Then, a sliding mode observer is developed for online estimation of the slip ratio, addressing the difficulty of direct slip ratio measurement. On this basis, a trajectory tracking controller is designed based on the improved rapid variable power reaching law, enabling fast tracking of multiple complex trajectories under slip conditions. Simulation and experimental results show that the proposed trajectory tracking controller not only effectively eliminates the influence of unknown slip disturbances on trajectory tracking, improving smoothness and tracking accuracy but also greatly accelerates the convergence process. The shortest convergence time is only 20.56% of that achieved by a fuzzy PID trajectory tracking controller and 61.43% of that achieved by a rapid double power reaching law trajectory tracking controller with a sliding mode observer. Full article

This study explores trajectory control in flexible manipulators using neural-network-based forward and inverse modeling. Unlike traditional approaches that enhance precision by increasing structural rigidity—often at the cost of added weight and energy consumption—this work focuses on lightweight flexible manipulators, which are more suitable for aerospace and other weight-sensitive applications but introduce control complexities due to elastic deformations. To address these challenges, neural-network-based models are proposed for a two-link, three-degree-of-freedom (3-DOF) flexible manipulator. Simulation and experimental results show that incorporating system delay compensation into the training data significantly improves tracking accuracy. Nonetheless, difficulties remain in achieving smooth trajectory generation. The findings highlight the potential of neural networks in adaptive control and point to future opportunities for refining input–output modeling to better align theoretical developments with practical implementation. Full article

This study presents the configuration design and attitude control of a moving-mass coaxial dual-rotor UAV (MMCDRUAV) for indoor applications. Compared with existing configurations, the proposed configuration avoids additional actuation mass and improves the control authority. Based on these improvements, a promising micro UAV platform with a high payload ability for agile indoor flight could be developed. Ground validation tests demonstrated its maneuverability, as provided by a moving-mass control (MMC) module requiring only the repositioning of existing components (e.g., battery packs) as movable masses. For trajectory tracking, an adaptive backstepping active disturbance rejection controller (ADRC) is proposed. The architecture integrates extended-state observers (ESOs) for disturbance estimation, parameter-adaptation laws for uncertainty compensation, and auxiliary systems to address control saturation. Lyapunov stability analysis proved the existence of uniformly ultimately bounded (UUB) closed-loop tracking errors. The results of the ground verification experiment confirmed enhanced tracking performance under real-world disturbances. Full article

This paper introduces the ProVANT Simulator, a comprehensive environment for developing and validating control algorithms for Unmanned Aerial Vehicles (UAVs). Built on the Gazebo physics engine and integrated with the Robot Operating System (ROS), it enables reliable Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) testing. Addressing key challenges such as modeling complex multi-body dynamics, simulating disturbances, and supporting real-time implementation, the framework features a modular architecture, an intuitive graphical interface, and versatile capabilities for modeling, control, and hardware validation. Case studies demonstrate its effectiveness across various UAV configurations, including quadrotors, tilt-rotors, and unmanned aerial manipulators, highlighting its applications in aggressive maneuvers, load transportation, and trajectory tracking under disturbances. Serving both academic research and industrial development, the ProVANT Simulator reduces prototyping costs, development time, and associated risks. Full article

Utilizing a coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) approach, this study constructs a comprehensive three-dimensional numerical model to simulate particle migration dynamics within rough artificial fractures subjected to the high-energy impact of water inrush. The model explicitly incorporates key governing factors, including intricate fracture wall geometry characterized by the joint roughness coefficient (JRC) and aperture variation, hydraulic pressure gradients representative of inrush events, and polydisperse sand particle sizes. Sophisticated simulations track the complete mobilization, subsequent acceleration, and sustained transport of sand particles driven by the powerful high-pressure flow. The results demonstrate that particle migration trajectories undergo a distinct three-phase kinetic evolution: initial acceleration, intermediate coordination, and final attenuation. This evolution is critically governed by the complex interplay of hydrodynamic shear stress exerted by the fluid flow, frictional resistance at the fracture walls, and dynamic interactions (collisions, contacts) between individual particles. Sensitivity analyses reveal that parameters like fracture roughness exert significant nonlinear control on transport efficiency, with an identified optimal JRC range (14–16) promoting the most effective particle transit. Hydraulic pressure and mean aperture size also exhibit strong, nonlinear regulatory influences. Particle transport manifests through characteristic collective migration patterns, including “overall bulk progression”, processes of “fragmentation followed by reaggregation”, and distinctive “center-stretch-edge-retention” formation. Simultaneously, specific behaviors for individual particles are categorized as navigating the “main shear channel”, experiencing “boundary-disturbance drift”, or becoming trapped as “wall-adhered obstructed” particles. Crucially, a robust multivariate regression model is formulated, integrating these key parameter effects, to quantitatively predict the critical migration time required for 80% of the total particle mass to transit the fracture. This investigation provides fundamental mechanistic insights into the particle–fluid dynamics underpinning hazardous water–sand inrush phenomena, offering valuable theoretical underpinnings for risk assessment and mitigation strategies in deep underground engineering operations. Full article

This paper presents a novel speed controller design for a brushless DC motor (BLDCM) operating under field-oriented control (FOC). The proposed speed controller is developed by integrating the zebra optimization algorithm (ZOA) with sliding mode theory (SMT). In this approach, the parameter ranges of the sliding mode dynamic trajectory control gain, exponential reaching gain, and constant speed reaching gain—three key components of the exponential reaching law-based sliding mode controller (ERLSMC)—are defined as the research space for the ZOA. The feedback speed error and its rate of change are used as features to calculate the fitness value. Subsequently, the fitness value computed by the algorithm is compared with the current best fitness value to determine the optimal position coordinates. These coordinates correspond to the optimal set of gain parameters for the sliding mode speed controller. During the operation of the BLDCM, these optimized parameters are applied to the controller in real time. This enables the system to adjust the three gain parameters dynamically under different operating conditions, thereby reducing the overshoot commonly induced by the ERLSMC. As a result, the speed response of the BLDCM drive system can more accurately and rapidly track the speed command. Therefore, the proposed control strategy is not only characterized by a small number of parameters and ease of tuning, but also does not require large datasets for training, making it highly practical and easy to implement. Finally, the proposed control strategy is simulated using Matlab/Simulink (2024b version) and applied to the BLDCM drive system for experimental testing. Its performance is compared against three types of sliding mode controllers employing different reaching laws: the constant speed reaching law, the exponential reaching law, and the exponential reaching law combined with extension theory (ET). Simulation and experimental results confirm that the proposed novel speed controller outperforms the other three sliding mode controllers based on different reaching laws, both in terms of speed command tracking and load regulation response. Full article

Given the growing need for high-performance operation of 4WID-UGVs, coordinated optimization of trajectory tracking, vehicle stability, and energy efficiency poses a challenge. Existing control strategies often fail to effectively balance these multiple objectives, particularly in integrating energy-saving goals while ensuring precise trajectory following and stable vehicle motion. Thus, a hierarchical control architecture based on Model Predictive Control (MPC) is proposed. The upper-level controller focuses on trajectory tracking accuracy and computes the optimal longitudinal acceleration and additional yaw moment using a receding horizon optimization scheme. The lower-level controller formulates a multi-objective allocation model that integrates vehicle stability, energy consumption, and wheel utilization, translating the upper-level outputs into precise steering angles and torque commands for each wheel. This work innovatively integrates multi-objective optimization more comprehensively within the intelligent vehicle context. To validate the proposed approach, simulation experiments were conducted on S-shaped and circular paths. The results show that the proposed method can keep the average lateral and longitudinal tracking errors at about 0.2 m, while keeping the average efficiency of the wheel hub motor above 85%. This study provides a feasible and effective control strategy for achieving high-performance, energy-saving autonomous driving of distributed drive vehicles. Full article

This paper addresses the issue of the poor collision avoidance effect of underactuated surface vehicles (USVs) during local path tracking. A virtual ship group control method is suggested by using Freiner coordinates and a model predictive control (MPC) algorithm. We track the planned path using the MPC algorithm according to the known vessel state and build a hierarchical weighted cost function to handle the state of the virtual vessel, to ensure that the vessel avoids obstacles while tracking the path. In addition, the control system incorporates an Extended Kalman Filter (EKF) algorithm to minimize the state estimation error by continuously updating the ship state and providing more accurate state estimation for the system in a timely manner. In order to validate the anti-interference and robustness of the control system, the simulation experiment is carried out with the “Yukun” as the research object by adding the interference of wind and wave of level 6. The outcome shows that the algorithm suggested in this paper can accurately perform the trajectory-tracking task and make collision avoidance decisions under six levels of external interference. Compared with the original MPC algorithm, the improved MPC algorithm reduces the maximum rudder angle output value by 58%, the integral absolute error by 46%, and the root mean square error value by 46%. The improved control algorithm reduces the maximum rudder angle output value by 42% and the maximum rudder angle output value by 10%. The control method provides a new technical choice for trajectory tracking and collision avoidance of USVs in complex marine environments, with a reliable theoretical basis and practical application value. Full article

Schizophrenia is a complex disorder that affects mental organization and cognitive functions, including concentration and memory. One notable manifestation of cognitive changes in schizophrenia is a diminished ability to scan and perform tasks related to visual inspection. From the three evaluable aspects of the ocular movements (saccadic, smooth pursuit, and fixation) in particular, smooth pursuit eye movement (SPEM) involves the tracking of slow moving objects and is closely related to attention, visual memory, and processing speed. However, evaluating smooth pursuit in clinical settings is challenging due to the technical complexities of detecting these movements, resulting in limited research and clinical application. This pilot study investigates whether the quantitative metrics derived from eye-tracking data can distinguish between patients with schizophrenia under treatment and healthy controls. The study included nine healthy participants and nine individuals receiving treatment for schizophrenia. Gaze trajectories were recorded using an eye tracker during a controlled visual tracking task performed during a clinical visit. Spatiotemporal analysis of gaze trajectories was performed by evaluating three different features: polygonal area, colocalities, and direction difference. Subsequently, a support vector machine (SVM) was used to assess the separability between healthy individuals and those with schizophrenia based on the identified gaze trajectory features. The results show statistically significant differences between the control and subjects with schizophrenia for all the computed indexes (p < 0.05) and a high separability achieving around 90% of accuracy, sensitivity, and specificity. The results suggest the potential development of a valuable clinical tool for the evaluation of SPEM, offering utility in clinics to assess the efficacy of therapeutic interventions in individuals with schizophrenia. Full article

Variations in the drive motor’s load inertia during wafer transfer robot arm motion critically degrade end-effector trajectory accuracy. To address this challenge, this study proposes an anti-disturbance control strategy integrating Radial Basis Function Neural Network (RBFNN) and event-triggered mechanisms. Firstly, dynamic simulations reveal that nonlinear load inertia growth increases joint reaction forces and diminishes trajectory precision. The RBFNN dynamically approximates system nonlinearities, while an adaptive law updates its weights online to compensate for load variations and external disturbances. Secondly, an event-triggered mechanism is introduced, updating the controller only when specific conditions are met, thereby reducing communication burden and actuator wear. Subsequently, Lyapunov stability analysis proves the closed-loop system is Uniformly Ultimately Bounded (UUB) and prevents Zeno behavior. Finally, simulations on a planar 2-DOF manipulator demonstrate significantly enhanced trajectory tracking accuracy under variable loads. Critically, the adaptive neural network control method reduces trajectory tracking error by 50% and decreases controller update frequency by 84.7%. This work thus provides both theoretical foundations and engineering references for high-precision wafer transfer robot control. Full article