Search Results (3,261)

In this paper, an observer-based proportional-integral-derivative (PID) controller is designed for a class of uncertain nonlinear systems with integral measurements, denial-of-service (DoS) attacks and bounded stochastic noises under a binary encoding scheme (BES). Parameter uncertainty is involved with a norm-bounded multiplicative expression. Integral measurements are considered to reflect the delayed signal collection of sensor. For communication, BES is put into use in the signal transmission process from the sensor to the observer and from the controller to the actuator. Random bit flipping is described that may take place caused by channel noises, whose impact is described by a stochastic noise. Randomly occurring DoS attacks are taken account of that may appear due to the shared network, which block the transmitted signals totally. Three sets of Bernoulli-distributed random variables are adopted to reveal the random occurrence of uncertainties, bit flipping and DoS attacks. The aim of this paper is to design an observer-based PID controller which guarantees that the closed-loop system reaches exponential ultimate boundedness in mean square (EUBMS). By virtue of Lyapunov stability theory, stochastic analysis technique and matrix inequality method, a sufficient condition is developed for designing the observer-based PID controller such that the closed-loop system achieves EUBMS performance, and the ultimate upper bound of the controlled output is bounded and such a bound is minimized. The gain matrices of the observer-based controller are acquired explicitly by virtue of solving the solution to an optimized issue with several matrix inequality constraints. Two simulation examples are given which indicate the usefulness of the proposed control method in this paper adequately. Full article

To address dynamic positioning (DP) challenges encountered by ships navigating amid unknown model parameters, environmental disturbances, and input saturation, this study proposes a nonlinear gains recursive sliding mode (RSM) DP control law. Within this control framework, an RSM strategy is devised, leveraging variable-gain technology to enhance DP system control performance. A variable-gain adaptive radial basis function (RBF) neural network is employed for real-time online training to approximate the unknown ship model. Simultaneously, an auxiliary dynamic system is incorporated to deal with input saturation. Furthermore, a robust control item is implemented to counteract the influence of RBF neural network approximation errors and external disturbances on the DP system. By constructing an appropriate Lyapunov function, it is proven that all signals in the DP closed-loop control system are uniformly ultimately bounded. Finally, simulation results demonstrate the ship DP system’s rapid response and high accuracy under the proposed control law, along with an enhanced ability to reject environmental disturbances. Full article

To meet the requirements for the automatic alignment, insertion, and inspection of guide-tube opening pins on the upper core plate in a component pool during refueling outages of nuclear power units, this paper proposes a cognition-enhanced visual-servoing framework that integrates geometric cognition-based compensation, observation-confidence modeling, and constraint-aware optimal control. The framework addresses the key challenge posed by the coexistence of long-term geometric drift and underwater observation uncertainty. Specifically, historical closed-loop data are leveraged to learn and compensate for systematic geometric errors online, substantially improving coarse-positioning accuracy. In addition, an explicit confidence model is introduced to quantitatively assess the reliability of visual measurements. Building on these components, a confidence-driven, finite-horizon, constrained model predictive control strategy is designed to achieve safe and efficient finite-step convergence while strictly respecting actuator physical constraints. Ground experiments and deep-water component-pool validations demonstrate that the proposed method reduces coarse-positioning error by approximately 75%, achieves stable sub-millimeter alignment with an ample engineering safety margin, and effectively decreases erroneous insertions and the need for manual intervention. These results confirm the engineering applicability and safety advantages of the proposed cognition-enhanced visual-servoing framework for underwater alignment tasks in nuclear component pools. Full article

Parallel inverter systems constitute the fundamental units of AC microgrids and distributed renewable energy generation systems, wherein accurate power sharing among units represents a critical challenge for stable operation. Conventional droop control fails to share the harmonic power in proportionality to the capacity of inverters due to disparities on line impedance, leading to circulating currents, degraded power quality, and reduced system load capability. To address these issues, this paper proposes a harmonic power-sharing control strategy based on perturbative virtual impedance injection. Under the premise that fundamental power sharing according to capacity ratios has been ensured, the strategy first converts the harmonic power information of each inverter into a small-signal perturbation, which is injected into the virtual impedance of its fundamental control loop. Subsequently, by detecting the resulting variations in fundamental power coefficients induced by this perturbation, a closed-loop feedback is constructed to adaptively adjust the virtual impedance value of each inverter at harmonic frequencies. This adjustment enables the automatic matching of the harmonic power distribution ratio to the inverter capacity ratio, ultimately achieving precise harmonic power sharing. The proposed strategy operates without requiring inter-unit communication links or sampling the voltage at the common coupling point, relying solely on local information, thereby enhancing system reliability. Finally, the effectiveness of the proposed control strategy in achieving harmonic power sharing under conditions of line impedance mismatch is validated through an RT-LAB hardware-in-the-loop platform. Full article

To address the low application accuracy and poor spreading uniformity of conventional lime spreaders, an electromagnetic vibration-assisted variable-rate lime spreader integrating a shaftless screw metering mechanism was developed. The overall configuration and operating principle are presented. Considering the physicochemical characteristics of lime powder, including fine particle size, strong drift tendency, and poor flowability, a shaftless screw metering unit was designed to improve discharge stability and metering accuracy. To enhance dispersion uniformity, a vertical electromagnetic vibration device was developed, and its key parameters were determined through a theoretical analysis of vibration frequency and amplitude. In addition, the structure and kinematic parameters of the spreading disc were optimized by analyzing particle trajectories and outlet distribution patterns. A closed-loop feedback control strategy was implemented to enable precise variable-rate application. Static bench tests demonstrated a metering accuracy of 96.42%, and the dispersion uniformity was at least 84.14% at an electromagnetic vibration frequency of 10 to 18 Hz. Field evaluations further showed that the coefficient of variation for transverse uniformity was no more than 17.88%, while the maximum coefficient of variation for longitudinal stability was 18.09%. These results indicate that the proposed spreader satisfies the operational requirements for accurate and uniform variable-rate application of lime powder. Full article

Aiming at the problem that the position control accuracy of the traditional semi-closed-loop control and the vibration caused by the nonlinear characteristics of the system are easily affected by the full closed-loop control, a double-loop position feedback control based on the state information feedback of the motor and the load is proposed. Based on the double-loop position feedback control framework, a vibration suppression method combining the linear extended state observer, torque feedback compensation and speed feedforward is introduced. The simulation results show that the proposed control method effectively suppresses load vibration, improves the system’s servo control performance, and maintains position control accuracy. Full article

The convergence of Lean manufacturing and Industry 4.0 requires digital infrastructures capable of transforming high-frequency telemetry into actionable insights. However, architectures that integrate near real-time data with closed-loop process control remain scarce, particularly in the food-processing industry. This study proposes a “Lean 4.0” framework based on a six-layer Digital Twin (DT) architecture to digitise waste detection and optimise a medium-scale bakery. The methodology integrates a heterogeneous Industrial Internet of Things (IIoT) network comprising 17 ESP32 (Espressif Systems, Shanghai, China)-based monitoring nodes. Data collection is managed via an edge-centric MQTT–InfluxDB (version 2.7, InfluxData, San Francisco, CA, USA) data pipeline. Furthermore, the analytics layer employs discrete-event simulation in Siemens Plant Simulation (version 2302, Siemens Digital Industries Software, Plano, TX, USA), constraint programming with Google OR-Tools (version 9.8, Google LLC, Mountain View, CA, USA), and machine learning models (Isolation Forest and SARIMA). Multi-month validation in a brownfield bakery, including a 60-day continuous monitoring test, demonstrated that the proposed architecture reduced production cycle time by 24.4% and inter-operational waiting time by 51.2%. Moreover, manual planning time decreased by 87.4% through the use of low-code scheduling interfaces. In addition, state-based control of critical ovens reduced energy consumption by 23.06%. These findings indicate that combining deterministic simulation and combinatorial optimisation with data-driven analytics provides a scalable blueprint for implementing cyber-physical systems in food-processing SMEs. This approach effectively bridges the gap between traditional Lean principles and data-driven smart manufacturing. Full article

Food manufacturing faces challenges in balancing efficiency, energy use, and quality. This paper presents a Hybrid Digital Twin Architecture (HDTA). It combines simulation, constraint programming, and Industrial IoT into a closed-loop system. The architecture has three layers: simulation for planning, optimization for scheduling, and an edge layer for control. We validated this using a bakery model with 10 products. The results show a 24.4% reduction in production time and 23% energy savings. Simulation results show complete elimination of quality time-window violations (0.0% vs. 13.3% baseline, p < 0.001). The system achieved a 2.4-month return on investment. This work demonstrates how combining these technologies can improve process industries. Full article

As a core functional component of multi-degree-of-freedom precision motion mechanisms, spherical hinges are widely used in high-end equipment fields such as industrial robots, vehicle engineering, and intelligent manufacturing. Their dynamic performance directly determines the motion accuracy and the level of intelligent control of the equipment. The high-precision real-time measurement of two-degree-of-freedom (2-DOF) angles is a key prerequisite for achieving precise closed-loop control of spherical hinges. However, due to the strong coupling characteristics between the 2-DOF angle signals, it is difficult to directly and accurately measure the angular motion parameters of spherical hinges, which has become a core technical bottleneck restricting the improvement in their application efficiency. To address this challenge, this paper presents an improved study of the previously proposed spherical differential quadrature capacitance sensor for measuring the 2-DOF angle signals of spherical hinges. Firstly, the 2-DOF angle signal decoupling model is reconstructed and optimized. Secondly, a real-time decoupling circuit architecture for phase-shift detection with single-frequency signal excitation is innovatively proposed. This solution effectively addresses the incomplete decoupling of 2-DOF angle signals in previous studies, as well as the problems of considerable measurement noise, low resolution, and high calibration difficulty caused by random amplitude and phase errors in the excitation signals. Through the construction of an experimental platform for verification tests, the results show that the proposed scheme can significantly suppress the random errors caused by the parameter dispersion of the device, achieve an angle measurement resolution of 0.001°, and simultaneously considerably reduce the complexity of system calibration, laying a key technical foundation for the engineering application of spherical hinges in the fields of precision measurement and high-performance control. Full article

To mitigate the significant impact of system nonlinearities, time-varying parameters, and external load disturbances on the output force of hydraulic servo systems in active hydraulic suspensions for engineering vehicles, this study proposes a beetle swarm optimization (BSO)-optimized extended state observer (ESO)-based sliding mode control (SMC) strategy. A comprehensive mathematical model of the hydraulic servo system is established, and an ESO-based SMC controller is designed, taking into account the coupled effects of chamber pressure dynamics and external loads on the uncertain output force. The stability of the closed-loop system is rigorously analyzed and verified using Lyapunov stability theory. The effectiveness of the proposed control strategy is verified through both numerical simulations and experimental tests. For step inputs of 5000 N and 8000 N, overshoot is significantly reduced compared with the conventional proportional–integral–derivative control and the standard extended state observer-based sliding mode control, while the settling time is shortened by more than 65% in simulations and up to 75% in experiments. Under sinusoidal force excitations at frequencies of 0.5 Hz, 1 Hz, and 2 Hz, the maximum tracking error, mean error, and standard deviation of the tracking error are substantially reduced, with the maximum error reduction exceeding 90%. These results demonstrate that the proposed method achieves high-precision force tracking under external disturbances and pronounced system uncertainties, providing an effective solution for force control of hydraulic servo systems in active suspension applications for engineering vehicles. Full article

In this paper, frequency control of shipboard microgrids is achieved in the presence of measurement noise, dynamic uncertainty, and actuator faults. Measurement noise arises from incorrect signal processing, electromagnetic interference, converter switching dynamics, mechanical vibrations from propulsion and generators, and transients caused by sudden changes in load or generation. Actuator faults are caused by intense mechanical vibrations, temperature-induced stress, degradation of power electronic devices, communication latency, and wear or saturation in fuel injection and governor components. To regulate the frequency deviation under these challenges, a cross-entropy-based fault-tolerant model predictive control method, utilizing a discrete-time linear Kalman filter, is developed. Firstly, the discrete-time linear Kalman filter ensures that uncertain states of the shipboard microgrids are measurable in a noisy environment. Afterward, the model predictive control scheme is employed to obtain an optimal control input based on the measurable states. This controller ensures the frequency regulation of shipboard microgrids in the presence of measurement noise. Furthermore, a fault-tolerant control technique that utilizes the concept of cross-entropy is extended to provide a robust controller that verifies the frequency regulation of shipboard microgrids with actuator faults. To demonstrate the stability of the closed-loop system of the shipboard microgrids based on the proposed controller, considering the effects of measurement noise, state uncertainty, and actuator faults, the Lyapunov stability concept is employed. Finally, simulation results in MATLAB/Simulink R2025b are provided to show that the proposed control method for frequency regulation in renewable shipboard microgrids is both effective and practicable. Full article

Under extreme fault conditions or during maintenance restarts, DC collection wind farms may experience a total blackout due to protective isolation. Addressing the black-start challenges arising from the unidirectional power flow structure and weak damping characteristics inherent to DC step-up collection wind farms, this paper proposes a sequential black-start control scheme predicated on grid-source coordination. A representative topology and an equivalent black-start model of the DC collection system are established to analyze the start-up mechanism and to design an active voltage build-up strategy with virtual impedance for the grid-side Modular Multilevel Converter (MMC). Meanwhile, generator-side permanent-magnet direct-drive wind turbines exploit their self-excitation capability and optimized pitch control to realize islanded self-bootstrapping and stable rotational speed. In addition, we develop a two-stage soft cut-in strategy that combines open-loop voltage scanning for pre-synchronization with closed-loop constant-current ramping of DC/DC converters, together with control logic for sequentially connecting multiple units to the DC grid. Simulation results show that the proposed approach smoothly restores the system from a zero-energy state to the rated operating point without external power sources, confirming the feasibility of full-farm start-up using the grid-side converter station and unit self-bootstrapping. Full article

Achieving safe and efficient sequential decision-making in dynamic and uncertain environments is a core challenge in intelligent manufacturing and robotic systems. During operation, systems are often subject to coupled multi-source uncertainties—such as stochastic disturbances, model mismatch, and environmental shifts—rendering traditional approaches based on deterministic models or post hoc safety verification incapable of simultaneously ensuring performance and safety. In particular, the non-differentiability of constraint satisfaction probabilities in chance-constrained decision-making severely impedes its integration with data-driven learning paradigms. To address these challenges, this paper proposes DeepChance-OPT (Deep Chance Optimization), an end-to-end differentiable disturbance-rejection decision framework tailored for dynamic grasping tasks in precision assembly. The framework first encodes historical observations and control sequences into a low-dimensional latent representation to extract key dynamic features relevant to decision-making. Subsequently, it models the temporal propagation of uncertainty in this latent space to predict the probability distribution of future states. Furthermore, via a differentiable chance-constrained mechanism, the risk of constraint violation is transformed into a continuous and differentiable penalty term, which is jointly optimized with the task performance objective to achieve synergistic improvement in both safety and efficiency. The entire framework is trained and executed under a unified end-to-end architecture, enabling closed-loop online sequential decision-making. Experiments on a precision silicon carbide wafer grasping task demonstrate that DeepChance-OPT achieves real-time performance (average decision latency < 4 ms) while reducing the constraint violation rate to 2.3%, significantly outperforming both traditional optimization and purely data-driven baselines. Under composite uncertainty scenarios—including parameter perturbations, measurement noise, and external disturbances—the success rate remains stably above 87.5%, fully validating the effectiveness of the proposed framework for robust, safe, and efficient decision-making in complex dynamic environments. This work provides a new paradigm for intelligent disturbance-rejection decision-making in high-precision manufacturing, offering both theoretical rigor and engineering practicality. Full article

The three-phase four-leg (3P4L) parallel-inverter system has been increasingly applied in the field of new energy power generation due to its capability of feeding single-phase loads. However, zero-sequence circulating current (ZSCC) can jeopardize the stable operation of the parallel-inverter system. To address this issue, this paper proposes a ZSCC suppression strategy based on the coordination of Model Predictive Control (MPC) and an improved 3D-SVPWM technique. Firstly, an overall methodology is established by introducing a regulation factor into each switching cycle of the inverter modulation. This introduction enables flexible adjustment of the zero-sequence duty cycle difference between the two inverters, laying the foundation for ZSCC suppression. Secondly, the MPC algorithm is applied to construct a transfer function model of the parallel system incorporating the regulation factor. Closed-loop feedback of ZSCC is introduced, using the deviation between the actual ZSCC and zero as the cost function, and the zero-vector duty cycle adjustment margin as the constraint. The optimal regulation factor is calculated and injected into the improved 3D-SVPWM. Through receding horizon optimization within MPC, disturbances are actively predicted and compensated, achieving precise ZSCC suppression. Finally, simulation results based on Matlab and hardware-in-the-loop (HIL) verify the effectiveness of the proposed strategy. Full article

This paper presents a robust sensorless control strategy for a dual-inverter doubly fed induction motor (DFIM) designed for high-performance electric vehicle (EV) traction systems. The proposed scheme eliminates the mechanical speed sensor by employing a sliding-mode observer (SMO) for real-time estimation of rotor speed and flux, ensuring accurate feedback under load disturbances and thereby enhancing reliability while reducing implementation cost. The DFIM is powered by two voltage-source inverters that independently control the stator and rotor windings through space vector pulse-width modulation (SVPWM). A power-sharing strategy optimally distributes the electromagnetic power between the two converters, ensuring smooth transitions between sub-synchronous and super-synchronous operating modes. Furthermore, a stator-flux-oriented vector control (SFOC) scheme incorporating a graphical torque optimization algorithm is developed to maximize torque while satisfying inverter and machine constraints across both base-speed and flux-weakening regions. The stability of the SMO-based estimation and closed-loop control is rigorously validated using Lyapunov theory. Comprehensive MATLAB R2024b/Simulink simulations conducted under the WLTC-Class 3 driving cycle confirm high accuracy and robustness, showing fast dynamic response, precise speed estimation, and smooth torque behavior across the full speed range. The results demonstrate that the SMO-based DFIM drive offers a cost-effective and reliable solution for next-generation EV traction applications. Full article