Search Results (56)

To mitigate rotor vibrations in magnetic bearing systems arising from mass imbalance, this study proposes a novel suppression strategy that integrates the crested porcupine optimizer (CPO) with an enhanced linear active disturbance rejection control (ELADRC) framework. The approach introduces a disturbance estimation and compensation scheme based on a linear extended state observer (LESO), wherein both the LESO bandwidth ${\omega }_0$ and the LADRC controller parameter ${\omega }_c$ are adaptively tuned using the CPO algorithm to enable decoupled control and real-time disturbance rejection in complex multi-degree-of-freedom (DOF) systems. Drawing inspiration from the crested porcupine’s layered defensive behavior, the CPO algorithm constructs a state-space model incorporating rotor displacement, rotational speed, and control current, while leveraging a reward function that balances vibration suppression performance against control energy consumption. The optimized parameters guide a real-time LESO-based compensation model, achieving accurate disturbance cancelation via amplitude-phase coordination between the generated electromagnetic force and the total disturbance. Concurrently, the LADRC feedback structure adjusts the system’s stiffness and damping matrices to improve closed-loop robustness under time-varying operating conditions. Simulation studies over a wide speed range (0~45,000 rpm) reveal that the proposed CPO-ELADRC scheme significantly outperforms conventional control methods: it shortens regulation time by 66.7% and reduces peak displacement by 86.8% under step disturbances, while achieving a 79.8% improvement in adjustment speed and an 86.4% reduction in peak control current under sinusoidal excitation. Overall, the strategy offers enhanced vibration attenuation, prevents current saturation, and improves dynamic stability across diverse operating scenarios. Full article

The article discusses the control of nonlinear processes with first-order dominant dynamics, focusing on implementation using modern hardware available in various programmable devices and embedded systems. The first two approaches rely on linearization with an ultra-local process model, considering small changes of the process input and output around a fixed operating point, which can be adjusted through gain scheduling with the setpoint variable. This model is used to configure either the historically established automatic reset controller (ARC) or a stabilizing proportional (P) controller enhanced by an inversion-based disturbance observer (DOB). This solution can be interpreted as an application of modern control theory (MCT), as DOB-based control (DOBC) or as advanced disturbance rejection control (ADRC). Alternatively, they can be viewed as a special case of automatic offset control (AOC) based on two types of linear process models. In the third design method, setpoint tracking by exact linearization (EL) is extended with a nonlinear DOB designed using the inverse of the nonlinear process dynamics (EEL). The fourth approach augments EL-based tracking with a DOB derived from the transfer functions of nonlinear processes (NTF). An illustrative example involving the control of a liquid reservoir with a variable cross-section clarifies motivation for the definition of (linear) local and ultra-local process models as well as their advantages in designing robust control that accounts for process uncertainties. Thus, the speed, homogeneity, and shape of transient responses, the ability to reconstruct disturbances, control signal saturation, and measurement noise attenuation are evaluated according to the assumptions specified in the controller design. The novelty of the paper lies in presenting a unifying perspective on several seemingly different control options under the impact of measurement noise. By explaining their essence, advantages, and disadvantages, it provides a foundation for controlling more complex time-delayed systems. The paper emphasizes that certain aspects of controller design, often overlooked in traditional linearization procedures, can significantly improve closed-loop properties. Full article

Automated insulin delivery (AID) systems depend on their actuators’ behavior since saturation limits, rate constraints, and hardware degradation directly affect the stability and safety of glycemic regulation. In this paper, we conducted an actuator-centric evaluation of five control strategies: Nonlinear Model Predictive Control (NMPC), Linear MPC (LMPC), Adaptive MPC (AMPC), Proportional-Integral-Derivative (PID), and Linear Quadratic Regulator (LQR) in three physiologically realistic scenarios: the first combines exercise and sensor noise to test for stress robustness; the second tightens the actuation constraints to provoke saturation; and the third models partial degradation of an insulin actuator in order to quantify fault tolerance. We have simulated a full virtual cohort under the two-actuator configurations, DG3.2 and DG4.0, in an effort to investigate generation-to-generation consistency. The results detail differences in the way controllers distribute insulin and glucagon effort, manage rate limits, and handle saturation: NMPC shows persistently tighter control with fewer rate-limit violations in both DG3.2 and DG4.0, whereas the classical controllers are prone to sustained saturation episodes and delayed settling under hard disturbances. In response to actuator degradation, NMPC suffers smaller losses in insulin effort with limited TIR losses, whereas both PID and LQR show increased variability and overshoot. This comparative analysis yields fundamental insights into important trade-offs between robustness, efficiency, and hardware stress and demonstrates that actuator-aware control design is essential for next-generation AID systems. Such findings position MPC-based algorithms as leading candidates for future development of actuator-limited medical devices and deliver important actionable insights into actuator modeling, calibration, and controller tuning during clinical development. Full article

This study addresses the trajectory tracking problem for unmanned hovercrafts operating under unknown time-varying environmental disturbances and actuator saturation. To balance real-time performance with control accuracy, an event-triggered adaptive nonlinear model predictive control (EANMPC) method is proposed. The approach dynamically adjusts the prediction horizon based on tracking error and incorporates an event-triggering mechanism to reduce unnecessary control updates. This design significantly alleviates computational burden while maintaining robust tracking performance. Furthermore, a rigorous input-to-state stability proof is provided without resorting to local linearization. Simulation results under two distinct trajectories demonstrate that the proposed method achieves superior tracking accuracy and reduces computational cost by 57% compared to conventional NMPC. The framework thus offers a practical and efficient control solution for underactuated hovercraft systems operating in complex maritime environments. Full article

This paper develops a unified synchronization framework for octonion-valued fractional-order neural networks (FOOVNNs) subject to mixed delays, Lévy disturbances, and topology switching. A fractional sliding surface is constructed by combining $I^{1-\mu }{e}_g$ with integral terms in powers of $|e_g|$. The controller includes a nonsingular term $-{\rho }_{2g}{s}_g^{c_2}\mathrm{sign}\left(s_g\right)$, a disturbance-compensation term $-{\widehat{\theta }}_g\mathrm{sign}\left(s_g\right)$, and a delay-feedback term $-{\lambda }_g{e}_g(t-\tau )$, while dimension-aware adaptive laws ${\phantom{,}}^C{D}_t^{\mu }{\rho }_g=k_{1g}N{\parallel s_g\parallel }^{c_2}$ and ${\phantom{,}}^C{D}_t^{\mu }{\widehat{\theta }}_g=k_{2g}N\parallel s_g\parallel$ ensure scalability with network size. Fixed-time convergence is established via a fractional stochastic Lyapunov method, and predefined-time convergence follows by a time-scaling of the control channel. Markovian switching is treated through a mode-dependent Lyapunov construction and linear matrix inequality (LMI) conditions; non-Gaussian perturbations are handled using fractional Itô tools. The architecture admits observer-based variants and is implementation-friendly. Numerical results corroborate the theory: (i) Two-Node Baseline: The fixed-time design drives ${\parallel e\left(t\right)\parallel }_1$ to $\mathcal{O}\left({10}^{-4}\right)$ by $t\approx 0.94\mathrm{s}$, while the predefined-time variant meets a user-set $T_p=0.5\mathrm{s}$ with convergence at $t\approx 0.42\mathrm{s}$. (ii) Eight-Node Scalability: Sliding surfaces settle in an $\mathcal{O}\left(1\right)$ band, and adaptive parameter means saturate well below their ceilings. (iii) Hyperspectral (Synthetic): Reconstruction under Lévy contamination achieves a competitive PSNR consistent with hypercomplex modeling and fractional learning. (iv) Switching Robustness: under four modes and twelve random switches, the error satisfies ${max}_t{\parallel e\left(t\right)\parallel }_1\le 0.15$. The results support octonion-valued, fractionally damped controllers as practical, scalable mechanisms for robust synchronization under non-Gaussian noise, delays, and time-varying topologies. Full article

The influence of vanadium substitution on the structure, elastic, mechanical, and magnetic behavior of lithium ferrite (Li_0.5+xV_xFe_2.5−2xO₄; x = 0.00–0.2) was systematically studied. X-ray diffraction (XRD) was used to investigate the crystal structure, and infrared spectroscopy (IR) was used to determine the cation distribution between the two ferrite sublattices, in addition to the elastic and mechanical behavior of Li_0.5+xV_xFe_2.5−2xO₄ ferrites. X-ray analysis revealed a monotonic decrease in lattice parameter from 8.344 Å to 8.320 Å with increasing V⁵⁺ content, confirming lattice contraction and stronger metal–oxygen bonding. Despite a moderate increase in porosity (from 6.9% to 8.9%), the elastic constants C₁₁ and C₁₂ increased, indicating improved stiffness and reduced compressibility. The derived Young’s, bulk, and rigidity moduli rose with the doping of V⁵⁺. Correspondingly, the longitudinal, shear, and mean velocities (V_l, V_s, and V_m) increased. The Debye temperature also showed a linear rise from 705 K to 723 K with V⁵⁺ doping, directly reflecting enhanced lattice stiffness and phonon frequency. Furthermore, both the saturation magnetization (M_S) and the initial permeability (μ_i) increased up to V⁵⁺ concentration x = 0.1 and then decreased. Curie temperature (T_C) decreased with increasing V⁵⁺ concentration, while both the saturation magnetization (M_S) and the initial permeability (μ_i) increased up to V⁵⁺ concentration x = 0.1 and then decreased, while the coercivity (H_C) showed the reverse trend. These results confirm that V⁵⁺ incorporation significantly enhances the Li ferrite, improving its elastic strength, lattice energy, thermal stability, and magnetically controlling properties and making them suitable for a variety of daily uses such as magneto-elastic sensors, high-frequency devices, and applications requiring mechanically robust ferrite materials. Full article

Photoplethysmogram (PPG) signals are increasingly utilized in wearable and mobile healthcare applications due to their non-invasive nature and ease of use in measuring physiological parameters, such as heart rate, blood pressure, and oxygen saturation. Recent advancements have highlighted green-light photoplethysmogram (gPPG) as offering superior signal quality and accuracy compared to traditional red-light photoplethysmogram (rPPG). Given the deterministic chaotic nature of PPG signals’ dynamics, nonlinear time series analysis has emerged as a powerful method for extracting health-related information not captured by conventional linear techniques. However, optimal data conditions, including appropriate sampling frequency and minimum required time series length for effective nonlinear analysis, remain insufficiently investigated. This study examines the impact of downsampling frequencies and reducing time series lengths on the accuracy of estimating dynamical characteristics from gPPG and rPPG signals. Results demonstrate that a sampling frequency of 200 Hz provides an optimal balance, maintaining robust correlations in dynamical indices while reducing computational load. Furthermore, analysis of varying time series lengths revealed that the dynamical properties stabilize sufficiently at around 170 s, achieving an error of less than 5%. A comparative analysis between gPPG and rPPG revealed no significant statistical differences, confirming their similar effectiveness in estimating dynamical properties under controlled conditions. These results enhance the reliability and applicability of PPG-based health monitoring technologies. Full article

Subject to magnetic saturation, temperature rise, and other factors, the electrical parameters of permanent magnet synchronous motors (PMSMs) in marine electric propulsion systems exhibit time-varying characteristics. Existing parameter identification algorithms fail to fully satisfy the requirements of high-performance PMSM control systems in terms of accuracy, response speed, and robustness. To address these limitations, this paper introduces multi-innovation theory and proposes a novel multi-innovation extended Kalman filter (MIEKF) for the identification of key electrical parameters of PMSMs, including stator resistance, d-axis inductance, q-axis inductance, and permanent magnet flux linkage. Firstly, the extended Kalman filter (EKF) algorithm is applied to linearize the nonlinear system, enhancing the EKF’s applicability for parameter identification in highly nonlinear PMSM systems. Subsequently, multi-innovation theory is incorporated into the EKF framework to construct the MIEKF algorithm, which utilizes historical state data through iterative updates to improve the identification accuracy and dynamic response speed. An MIEKF-based PMSM parameter identification model is then established to achieve online multi-parameter identification. Finally, a StarSim RCP MT1050-based experimental platform for online PMSM parameter identification is implemented to validate the effectiveness and superiority of the proposed MIEKF algorithm under three operational conditions: no-load, speed variation, and load variation. Experimental results demonstrate that (1) across three distinct operating conditions, compared to forget factor recursive least squares (FFRLS) and the EKF, the MIEKF exhibits smaller fluctuation amplitudes, shorter fluctuation durations, mean values closest to calibrated references, and minimal deviation rates and root mean square errors in identification results; (2) under the load increase condition, the EKF shows significantly increased deviation rates while the MIEKF maintains high identification accuracy and demonstrates enhanced anti-interference ability. This research has achieved a comprehensive improvement in parameter identification accuracy, dynamic response speed, convergence effect, and anti-interference performance, providing an electrical parameter identification method characterized by high accuracy, rapid dynamic response, and strong robustness for high-performance control of PMSMs in marine electric propulsion systems. Full article

Achieving long downhill constant-speed driving of heavy vehicles is of great significance for improving vehicle transport safety. As a kind of auxiliary brake, the hydraulic retarder has the characteristics of large braking torque and compact structure. More importantly, the hydraulic retarder is capable of braking for a long period of time, which enables the vehicle to travel downhill at a constant speed with less or no use of mechanical brakes. However, due to the complexity of hydraulic retarder braking conditions, its output braking torque presents time-varying and non-linear characteristics, and the control of the hydraulic retarder filling rate in order to achieve the vehicle’s long downhill constant-speed braking is a challenging problem. This research proposes a constant-speed control strategy utilizing the robust control method to address the issue of prolonged downhill braking at constant speed for heavy-duty vehicles, which achieves constant-speed and stable driving downhill by controlling the filling rate of the hydraulic retarder. Initially, the dynamic model of the downhill process for heavy-duty vehicles and the physical model of the hydraulic retarder are established. Then, based on the concept of sliding mode control, the sliding mode controller with saturation function and the high-frequency robust controller are developed to modulate the filling rate of the hydraulic retarder in response to variations in vehicle speed. In order to verify the effectiveness of the algorithm, three different operating conditions were set according to the vehicle mass and road gradient, and simulation tests were carried out in the MATLAB/Simulink environment. Simulation results indicate that the high-frequency controller exhibits remarkable robustness against dynamic disturbances within the system. Additionally, when variations in vehicle mass and road gradient occur, the root mean square error of the high-frequency controller’s speed, in comparison to the fuzzy controller, decreases by 0.1157 km/h, while the maximum absolute error in vehicle speed diminishes by 0.248 km/h. Simultaneously, the high-frequency controller proficiently suppresses chatter, thereby meeting the demand for consistent speed braking in big trucks on prolonged downhill gradients. Full article

The conventional-speed Sliding Mode Controller (SMC) for ship PM propulsion motors, which employs exponential reaching laws and linear sliding surface functions, demonstrates susceptibility to oscillatory phenomena. To solve this problem, this paper proposes a saturation adaptive nonlinear integral sliding mode controller (SANI-SMC) which combines a nonlinear integral sliding surface function with an adaptive saturation gain reaching rate. The nonlinear integral sliding surface function improves the system responsiveness, and then enhances the stability and robustness of the system. The adaptive saturation gain reaching rate not only mitigates the chattering effect induced by the sign function in traditional exponential reaching rates, but also weakens the underlying oscillations. This approach effectively solves the overshoot problem inherent in traditional PI controllers, and has better anti-interference ability under sudden load variations. Finally, the proposed controller is experimentally verified based on an electric propulsion semi-physical experimental platform consisting of Rapid Control Prototyping (RCP), and compared with a Proportional–Integral (PI) controller and an SMC. Moreover, the integral absolute error (IAE), integral time-weighted absolute error (ITAE), and integral of the square value (ISV) metrics are calculated for the PI controller, SMC, and SANI-SMC based on experimental data collection. The results demonstrate that the SANI-SMC exhibits superior stability and robustness compared to both the PI controller and SMC. Full article

This paper studies the data-driven attitude tracking control issue for an unmanned underwater vehicle (UUV) with disturbances. First, a new polynomial finite-time prescribed performance function (FTPF) is introduced to avoid the problem of the computation number increasing as the exponential term increases in the conventional exponential FTPF. By using the new polynomial FTPF, the tracking error is converted into a constrained form. Then, an estimator is designed for estimating the unknown pseudo-partitioned Jacobian matrix (PJM) in the linearization model, and a discrete-time nonlinear disturbance observer (DNDO) is adopted for observing unknown disturbances. It is worth noting that the DNDO can avoid the large overshoot by introducing a saturated function. With the help of the estimator for the PJM, the DNDO, and the constrained error, a data-driven robust control strategy with performance constraints is designed to fulfill accurate attitude tracking control of the UUV, which ensures that the tracking error draws into a prescribed region in a predetermined time. Eventually, the control strategy is verified by numerical simulations. Full article

This study proposes a novel nonlinear adaptive fuzzy hybrid sliding mode (AFHSM) control strategy for the precise trajectory tracking of autonomous mobile robots (AMRs) equipped with four Mecanum wheels. The control design addresses the inherent complexities of such platforms, which include strong system nonlinearities, significant parametric uncertainties, torque saturation effects, and external disturbances that can adversely affect dynamic performance. Unlike conventional approaches that rely on model linearization or dimension reduction, the proposed AFHSM control retains the full nonlinear characteristics of the system to ensure accurate and robust control. The controller is systematically derived from the trajectory-tracking error dynamics between the AMR and the desired trajectory (DT). It integrates higher-order sliding mode (SM) control, fuzzy logic inference, and adaptive learning mechanisms to enable real-time compensation for model uncertainties and external perturbations. In addition, a saturation handling mechanism is incorporated to ensure that the control signals remain within feasible limits, thereby preserving actuator integrity and improving practical applicability. The stability of the closed-loop nonlinear system is rigorously established through the Lyapunov theory, guaranteeing the asymptotic convergence of tracking errors. Comprehensive simulation studies conducted under severe conditions with up to 60 percent model uncertainty confirm the superior performance of the proposed method compared to classical SM control. The AFHSM control consistently achieves lower trajectory and heading errors while generating smoother control signals with reduced torque demand. This improvement enhances tracking precision, suppresses chattering, and significantly increases energy efficiency. These results validate the effectiveness of the AFHSM control approach as a robust and energy-aware control solution for AMRs operating in highly uncertain and dynamically changing environments. Full article

This paper provides a comprehensive survey on advanced control techniques for Takagi-Sugeno (T-S) fuzzy systems that are subject to input and output performance constraints. The focus is on addressing practical applications, such as actuator saturation and output limits, which are often encountered in industries like aerospace, automotive, and robotics. The paper discusses key control methods such as model predictive control, anti-windup compensators, and Linear Matrix Inequality (LMI)-based control, emphasizing their effectiveness in handling input and output constraints. These techniques ensure system stability, robustness, and performance even under strict physical limitations. The survey also highlights the importance of T-S fuzzy systems, which provide a flexible framework for modeling and controlling nonlinear systems by breaking them down into simpler linear models. Additionally, recent developments in robust and adaptive control strategies are explored, particularly in handling time delays, disturbances, and uncertainties. These methods are crucial for real-time applications where the system must remain stable and safe despite unmeasured states or external disturbances. By reviewing these advanced techniques, the paper aims to identify research gaps and future directions, particularly in scalable solutions and integrating data-driven approaches with T-S fuzzy control frameworks. Full article

This paper presents a novel dynamic adaptive event-triggered mechanism (DAETM) for addressing actuator saturation in leader–follower fractional-order nonlinear multi-agent networked systems (FONMANSs). By utilizing a sector-bounded condition approach and a convex hull representation technique, the proposed method effectively addresses the effects of actuator saturation. This results in less conservative linear matrix inequality (LMI) criteria, guaranteeing asymptotic consensus among agents within the FONMANS framework. The proposed sufficient conditions are computationally efficient, requiring only simple LMI solutions. The effectiveness of the approach is validated through practical applications, such as synchronous generators within a FONMANS framework, where it demonstrates superior performance and robustness. Additionally, comparative studies with Chua’s circuit system enhance the robustness and efficiency of control systems compared to existing techniques. These findings highlight the method’s potential for broad application across various multi-agent systems, particularly in scenarios with limited communication and actuator constraints. The proposed approach enhances system performance and provides a robust, adaptive control solution for dynamic and uncertain environments. Full article

This paper presents the development of an Active Disturbance Rejection Controller (ADRC) to address the trajectory tracking problem of a 2DOF (Degrees of Freedom) Serial Flexible Robot. The proposed approach leverages differential flatness theory to determine the system’s flat output, simplifying the trajectory tracking problem into a linear state feedback control with disturbance rejection. A set of a Generalized Proportional Integral Observer (GPIO) and Luenberger observers is employed to estimate the derivatives of the flat output and both internal and external disturbances in real time. The control law is experimentally validated on a 2DOF Serial Flexible Robot prototype developed by Quanser. Quantitative results demonstrate that the ADRC achieves superior performance compared to a partial state feedback control scheme, with a Mean Squared Error (MSE) as low as 1.0651 × 10⁻⁵ rad² for trajectory tracking. The ADRC effectively suppresses oscillations, minimizes high-frequency noise and reduces saturation effects, even under external disturbances. These findings underscore the robustness and efficiency of the proposed method for underactuated flexible systems. Full article