Closed-loop Control Research Articles

Sliding Mode Observer-Based Phase-Locking Strategy for Current Source Inverter in Weak Grids

The current source inverter (CSI) has become the main grid-connected interface of distributed generation systems due to its advantages, such as boost capability, current controllability, and short-circuit protection capability. However, in weak grids, the grid-connected CSI that uses a phase-locked loop to achieve grid voltage synchronization has problems, such as instability in the fundamental positive-sequence voltage phase detection at the point of common coupling and instability in the current loop control, which seriously hamper the promotion and application of the CSI and its interconnected systems. For this reason, this paper proposes a sliding mode observer-based phase-locking strategy for the CSI. The strategy proposes a sliding mode observer for grid voltage phase detection, so that the grid current can directly follow the grid voltage, solving the problem of inconsistency or distortion between the voltage phase of the point of common coupling and the grid voltage phase in weak grids. On this basis, the grid impedance is regarded as part of the CL filter, and a robust parameter design method is proposed for the grid current closed-loop control in weak grids, which achieves robust operation of a CSI in weak grids. Finally, an experimental platform for a single-phase grid-connected CSI is built to verify the effectiveness and feasibility of the proposed scheme.

Event-triggered adaptive fault-tolerant control with pre-specified performance for AUVs trajectory tracking

This paper investigates the problem of fault-tolerant control of the preset performance of autonomous underwater vehicles (AUVs) in case of external disturbances and actuator failures. To begin with, a specific new nonlinear auxiliary function with dual performance constraints is designed to establish the foundation for achieving prescribed performance in the closed-loop system. Subsequently, an event-triggered fault tolerant controller employing Nussbaum gain technique is introduced to accomplish the precision trajectory tracking control of the AUVs by predetermining control index. It is shown that the effect by resulting in actuator faults is on-line compensated by an adaptive estimator. Theoretical analyses demonstrate the boundedness of the signals of the designed closed-loop control system, and the Zeno phenomenon can be effectively avoided. Finally, simulation experiments are conducted to verify the effectiveness of the proposed methods.

Development and Application of Digital Twin Control in Flexible Manufacturing Systems

Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, and energy efficiency. This paper explores the application of digital twin technology to tackle these challenges and enhance FMS optimization and control. A digital twin, constructed by integrating simulation models, data acquisition, and machine learning algorithms, was employed to replicate the behavior of a real-world FMS. This digital twin enabled real-time dynamic optimization and adaptive control of manufacturing operations, facilitating informed decision making and proactive adjustments to optimize resource utilization and process efficiency. Computational experiments were conducted to evaluate the digital twin implementation on an FMS equipped with robotic material handling, CNC machines, and automated inspection. Results demonstrated that the digital twin significantly improved FMS performance. Productivity was enhanced by 14.53% compared to conventional methods, energy consumption was reduced by 13.9%, and quality was increased by 15.8% through intelligent machine coordination. The dynamic optimization and closed-loop control capabilities of the digital twin significantly improved overall equipment effectiveness. This research highlights the transformative potential of digital twins in smart manufacturing systems, paving the way for enhanced productivity, energy efficiency, and defect reduction. The digital twin paradigm offers valuable capabilities in modeling, prediction, optimization, and control, laying the foundation for next-generation FMS.

Multilayer neuroadaptive constraint-handling control architecture for a family of nonlinear systems with uncertainty compensation

In practical applications, there are some requirements for time-varying constraints on full system states. Currently, Barrier Lyapunov Function is a popular tool for constraint control. However, how to handle various uncertainties while satisfying constraint requirements is worth further research. Accordingly, a high-performance multilayer neuroadaptive constraint-handling controller for a class of nonlinear systems with uncertainty compensation will be developed. Significantly, asymmetric time-varying constraints for full system states can be implemented. Furthermore, a set of novel multilayer neuroadaptive disturbance observers will be proposed to address modeling uncertainties. Moreover, by introducing a set of nonlinear command filters, the intelligent control algorithm will be designed via the command filtered backstepping method. The stability of the whole closed-loop system is strictly proved. Additionally, the proposed algorithm is applied to different nonlinear systems including a hydraulic robotic manipulator system to verify its feasibility.

On the Feasibility of a RGA-Based Decoupling Control in the Reactor-Follow-Turbine Operation of a PWR

Closed-loop control of the reactor-follow-turbine operation is attempted in this work. The reactor power is intended to follow the turbine load demand, whereas the coolant average temperature is to be maintained constant for reasons of design safety. To this effect, a standardized pressurized water reactor model is adopted wherein the reactor external reactivity and the feedwater mass flux are deemed the manipulated control signals. Offline system identification is carried out on the model input/output (I/O) terminals to extract first order plus time delay models thereof. A further relative gain array analysis is conducted to determine the most tightly coupled I/O channels. A proportional integral controller is thereafter designed for each channel, making use of an analytical gain/phase margin tuning mechanism. The results for different output tracking scenarios confirm a feasible and quite satisfactory closed-loop control practice.

Reinforcement learning-based robust formation control for Multi-UAV systems with switching communication topologies

This paper introduces a novel optimal robust formation method for quadcopter multiple unmanned aerial vehicle (multi-UAV) systems. Firstly, a reinforcement learning (RL) algorithm based on a unique gradient descent training approach is proposed to solve the Hamilton–Jacobi–Bellman (HJB) equation, which can effectively eliminate the requirement of the Persistent Excitation (PE) condition. Secondly, the robustness of the controlled system is emphasized, and an Uncertainty and Disturbance Estimator (UDE) observer is developed to suppress model uncertainty and external disturbances through filtering techniques. Furthermore, a switched sliding mode control technique according to the average dwell time (ADT) is employed to convert switching communication topology between UAVs dynamically, and the stability analysis of the corresponding closed-loop control systems is then performed by the use of Lyapunov analysis. Finally, the simulation examples are provided to verify the effectiveness of the designed control strategy.

Epidural Spinal Cord Recordings (ESRs): sources of neural-appearing artifact in stimulation evoked compound action potentials.

Evoked compound action potentials (ECAPs) measured during epidural spinal cord stimulation (SCS) can help elucidate fundamental mechanisms for the treatment of pain and inform closed-loop control of SCS. Previous studies have used ECAPs to characterize neural responses to various neuromodulation therapies and have demonstrated that ECAPs are highly prone to multiple sources of artifact, including post-stimulus pulse capacitive artifact, electromyography (EMG) bleed-through, and motion artifact. However, a thorough characterization has yet to be performed for how these sources of artifact may contaminate recordings within the temporal window commonly used to determine activation of A-beta fibers in a large animal model.
We characterized sources of artifacts that can contaminate the recording of ECAPs in an epidural SCS swine model using the Abbott Octrode™ lead. Spinal ECAP recordings can be contaminated by capacitive artifact, short latency EMG from nearby muscles of the back, and motion artifact. The capacitive artifact can appear nearly identical in duration and waveshape to evoked A-beta responses. EMG bleed-through can have phase shifts across the electrode array, similar to the phase shift anticipated by propagation of an evoked A-beta fiber response. The short latency EMG is often evident at currents similar to those needed to activate A-beta fibers associated with the treatment of pain. Changes in CSF between the cord and dura, and motion induced during breathing created a cyclic oscillation in all evoked components of recorded ECAPs. 
Controls must be implemented to separate neural signal from sources of artifact in SCS ECAPs. We suggest experimental procedures and reporting requirements necessary to disambiguate underlying neural response from these confounds. These data are important to better understand the framework for recorded ESRs, with components such as ECAPs, EMG, and artifacts, and have important implications for closed-loop control algorithms to account for transient motion such as postural changes and cough.

Research on Heave Compensation System Based on Switched Reluctance Motor

Aiming at the requirements of the marine work platform for real-time control, time-varying speed and the time-varying torque control of the motor-driven active heave compensation device, this paper introduces a composite control strategy based on the switched reluctance motor (SRM)-driven active heave compensation device. As for the compensation error caused by the time lag in the real-time system, the model prediction trajectory algorithm is used to predict the compensation displacement obtained using the dynamic model. The next time, the control parameters are then provided for the SRM control system in advance to reduce the compensation error. The SRM control strategy selects a double closed-loop compound control strategy of Back Propagation (BP) fuzzy neural network Proportion Integration Differentiation (PID) control. Its outer speed loop uses a fuzzy controller to quickly track a wide range of speed changes. The torque inner loop uses BP neural network adaptive PID control. This helps to reduce torque ripple and to ensure that the electromagnetic torque output of the SRM remains stable. Finally, the system feasibility is verified by setting different wave parameters. The simulation results show that the simulation conditions can reach 97.5% and 96.4% under the 3 and 4 wave levels, respectively. The simulation effect is satisfying, which verifies the feasibility of the proposed scheme.

Vision-based reinforcement learning control of soft robot manipulators

Purpose This study aims to tackle control challenges in soft robots by proposing a visually-guided reinforcement learning approach. Precise tip trajectory tracking is achieved for a soft arm manipulator. Design/methodology/approach A closed-loop control strategy uses deep learning-powered perception and model-free reinforcement learning. Visual feedback detects the arm’s tip while efficient policy search is conducted via interactive sample collection. Findings Physical experiments demonstrate a soft arm successfully transporting objects by learning coordinated actuation policies guided by visual observations, without analytical models. Research limitations/implications Constraints potentially include simulator gaps and dynamical variations. Future work will focus on enhancing adaptation capabilities. Practical implications By eliminating assumptions on precise analytical models or instrumentation requirements, the proposed data-driven framework offers a practical solution for real-world control challenges in soft systems. Originality/value This research provides an effective methodology integrating robust machine perception and learning for intelligent autonomous control of soft robots with complex morphologies.

Theoretical and experimental study of active vibration control of rotating composite laminated beams using Fx-NLMS algorithm

In the study, an adaptive active vibration control model of rotating composite laminated beam is presented by using the filtered-x normalized least mean square (Fx-NLMS) algorithm, and the experimental study is carried out based on the closed-loop control system. The macro fible composite (MFC) patches is employed to contruct the closed-loop control system of rotating composite laminated beam. By considering peizoelectric coupling effect and the influence of rotating angular velocity, the novel analysis model of vibration transfer function of rotating composite laminated beam with MFC patches is established. The calculation accurancy of the theoretical analysis model is validated by the experimental results. The experimental and simulation studies demonstrate that the low-frequnecy vibration of rotating composite beam is effectively controlled by using the lightweight system. The excellent stability and convergence effect of the closed-loop control system are obtained in the experimental testings. Moreover, the filter orders and learning factor on the active vibration control effect of the rotating composite beam are also stuided and discussed. This work provides a novel appoach for the low-frequency vibration control of the rotating cantilever beams by employing lightweight system.

Deep learning for blood glucose level prediction: How well do models generalize across different data sets?

Deep learning-based models for predicting blood glucose levels in diabetic patients can facilitate proactive measures to prevent critical events and are essential for closed-loop control therapy systems. However, selecting appropriate models from the literature may not always yield conclusive results, as the choice could be influenced by biases or misleading evaluations stemming from different methodologies, datasets, and preprocessing techniques. This study aims to compare and comprehensively analyze the performance of various deep learning models across diverse datasets to assess their applicability and generalizability across a broader spectrum of scenarios. Commonly used deep learning models for blood glucose level forecasting, such as feed-forward neural network, convolutional neural network, long short-term memory network (LSTM), temporal convolutional neural network, and self-attention network (SAN), are considered in this study. To evaluate the generalization capabilities of each model, four datasets of varying sizes, encompassing samples from different age groups and conditions, are utilized. Performance metrics include Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Coefficient of Determination (CoD) for analytical asssessment, Clarke Error Grid (CEG) for clinical assessments, Kolmogorov-Smirnov (KS) test for statistical analysis, and generalization ability evaluations to obtain both coarse and granular insights. The experimental findings indicate that the LSTM model demonstrates superior performance with the lowest root mean square error and highest generalization capability among all other models, closely followed by SAN. The ability of LSTM and SAN to capture long-term dependencies in blood glucose data and their correlations with various influencing factors and events contribute to their enhanced performance. Despite the lower predictive performance, the FFN was able to capture patterns and trends in the data, suggesting its applicability in forecasting future direction. Moreover, this study helps in identifying the optimal model based on specific objectives, whether prioritizing generalization or accuracy.

GoFish: a foray into open-source, aquatic behavioral automation.

As the most species-rich vertebrate group, fish provide an array of opportunities to investigate the link between ecological interactions and the evolution of behavior and cognition, yet, as an animal model, they are relatively underutilized in studies of comparative cognition. To address this gap, we developed a fully automated platform for behavioral experiments in aquatic species, GoFish. GoFish includes closed-loop control of task contingencies using real-time video tracking, presentation of visual stimuli, automatic food reward dispensers, and built-in data acquisition. The hardware is relatively inexpensive and accessible, and all software components of the platform are open-source. GoFish facilitates experimental automation, allowing for customization of high-throughput protocols and the efficient acquisition of rich behavioral data. We hope this platform proves to be a useful tool for the research community, facilitating refined, reproducible behavioral experiments on aquatic species in comparative cognition, behavioral ecology, and neuroscience.

Investigation of Innovative High-Response Piezoelectric Actuator Used as Smart Actuator–Sensor System

This paper presents an experimental analysis of a high-response piezoelectric actuator system for the modular design of hydraulic digital fluid control units. It focuses on determining static and dynamic characteristics, forming the basis for developing a smart Industry 4.0 component that incorporates both actuator and sensor function. The design process examines the main challenges, advantages, disadvantages, and working principles to define parameters that impact the actuator’s behaviour and performance. The new piezoelectric actuator system features three piezoelectric stack actuators in series, enabling simultaneous actuation and sensing by applying and measuring the electrical voltage at each piezo element. The experimental setup and test methodology are explained in detail, revealing that the new design, combined with an appropriate open-loop or closed-loop control method, offers superior actuator stroke control, high stroke resolution, and a high-dynamic step response. This paper proposes a concept of a smart piezo actuator system focused on I4.0 and an actuator administration shell, integrated with 5G and RFID technology, which will allow automatic plug-and-play functionality and efficient interconnection, communication, and data transfer between the hydraulic valve and the piezoelectric actuator system.

Detection of Broken Bars in Induction Motors Operating with Closed-Loop Speed Control

Rotor bar breakage in induction motors is often detected by analysing the signatures in the stator current. However, due to the alteration of the current spectrum, traditional methods may fail when inverter-fed motors operate with closed-loop control using a cascade structure to regulate the speed. In this paper, the potential of zero-sequence voltage analysis to detect this fault is investigated, and a new index to quantify the severity of the fault based on this signal is proposed. Signals from motors operating under different control strategies and signals from motors powered from the mains are considered to verify the robustness of the proposed fault severity index. As a result, in all the analysed conditions the value of the proposed index for the healthy motor is found to be approximately 0.010, while for the faulty machine it is between 0.110 and 0.252.

Adaptive Nonsingular Fast Terminal Sliding Mode Control for Shape Memory Alloy Actuated System

Due to its high power-to-weight ratio, low weight, and silent operation, shape memory alloy (SMA) is widely used as a muscle-like soft actuator in intelligent bionic robot systems. However, hysteresis nonlinearity and multi-valued mapping behavior can severely impact trajectory tracking accuracy. This paper proposes an adaptive nonsingular fast terminal sliding mode control (ANFTSMC) scheme aimed at enhancing position tracking performance in SMA-actuated systems by addressing hysteresis nonlinearity, uncertain dynamics, and external disturbances. Firstly, a simplified third-order actuator model is developed and a variable gain extended state observer (VGESO) is employed to estimate unmodeled dynamics and external disturbances within finite time. Secondly, a novel nonsingular fast terminal sliding mode control (NFTSMC) law is designed to overcome singularity issues, reduce chattering, and guarantee finite-time convergence of the system states. Finally, the ANFTSMC scheme, integrating NFTSMC with VGESO, is proposed to achieve precise position tracking for the prosthetic hand. The convergence of the closed-loop control system is validated using Lyapunov’s stability theory. Experimental results demonstrate that the external pulse disturbance error of ANFTSMC is 8.19°, compared to 19.21° for the comparative method. Furthermore, the maximum absolute error for ANFTSMC is 0.63°, whereas the comparative method shows a maximum absolute error of 1.03°. These results underscore the superior performance of the proposed ANFTSMC algorithm.

Bio-inspired MPPT and advanced control for grid-connected solar photovoltaic systems with flyback converter

ABSTRACT This paper presents an enhanced approach for grid-connected photovoltaic (PV) systems using a flyback converter and Sovereign Butterfly Optimization for advanced Maximum Power Point Tracking (MPPT). The focus is on improving energy extraction efficiency from PV panels by dynamically adjusting system parameters. A flyback converter ensures reliable grid integration, while the Butterfly Optimization Algorithm, inspired by butterfly foraging, provides real-time MPPT to rapidly and accurately track the maximum power point, even under varying environmental conditions. A novel voltage harmonic mitigation technique processes three-phase currents through dq0 frames for closed-loop control, reducing harmonics and optimising voltage precision. The DC/DC flyback converter stabilizes AC conversion, minimizing high-frequency harmonics. Additionally, a Subsystem Synchronization Control Strategy synchronizes active and non-active grid currents, improving system stability. Simulations show a 99.3% tracking accuracy, 2% power oscillation around the maximum power point, and a Total Harmonic Distortion (THD) of 1.78%, meeting IEC 61,727 standards.

Control of hole rolling on 3D Servo Presses

Innovative press concepts with multiple degrees of freedom such as the 3D Servo Press (3DSP) allow the implementation of incremental forming processes, thus the production of previously unfeasible workpiece geometries. This publication demonstrates that elliptical double-sided collars can be formed out of sheet metal through closed-loop control of the ram pose and controlled ram tilting. For this purpose, a new tool has been developed that enables the process of flexible hole rolling on a 3DSP. We show that the geometry of produced parts can be influenced by adapting the tool path trajectories and present a compensation approach that ensures the highly accurate insertion of the collars into the sheet metal. The geometries as well as the resulting courses of the collar height over the circumference of the hole, are analysed using experimental and FEM-based simulation results.

Non-linear bending analysis and control of graphene-platelets-reinforced porous sandwich plates with piezoelectric layer subjected to electromechanical loading

Piezoelectric materials as the controlling element have been widely utilized to produce intelligent engineering structures, while these smart structures may fail to realize effective control of composite structures with large deformations. However, investigations on such issues are less reported in published literature, as an accurate and efficient model is required to well forecast the geometrically nonlinear behaviors of smart sandwich structures. As a result, a novel sinusoidal Legendre global-local higher-order shear deformation plate theory (SLHSDT) has been developed to accurately capture geometrically nonlinear behaviors of piezoelectric sandwich plates. The proposed model can fulfill the compatible conditions of transverse shear stresses and contain transverse normal strain, which can ensure precision in predicting electromechanical behaviors. The multi-patch isogeometric analysis (IGA) method for sandwich plates partially bonded with piezoelectric layers is proposed to overcome C1-continuity between patches for the first time. Moreover, the Newmark-β method and Newton-Raphson technique are attempted to solve the nonlinear equations. The present model has been utilized to investigate electromechanical behaviors of laminated structures with piezoelectric layers, which has been compared with the published results. In addition, experiments on macro fiber composite (MFC) integrated sandwich plates have been also carried out in the present work, which can effectively verify the performance of proposed model. Subsequently, the proposed model is employed to study electromechanical behaviors of the five-layer piezoelectric sandwich plates containing internal pores and graphene platelets. Then, influences of the porosity coefficient and GPLs weight fraction on the nonlinear electromechanical behaviors of sandwich plates are investigated. Eventually, the active control on nonlinear behaviors of piezoelectric porous sandwich plates with GPLs reinforcement is studied by using a closed-loop control system, and an effective approach slowing down large deformation has been proposed by selecting an appropriate distribution of GPLs along the thickness direction.

Quality of Service-Aware Multi-Objective Enhanced Differential Evolution Optimization for Time Slotted Channel Hopping Scheduling in Heterogeneous Internet of Things Sensor Networks.

The emergence of the Internet of Things (IoT) has attracted significant attention in industrial environments. These applications necessitate meeting stringent latency and reliability standards. To address this, the IEEE 802.15.4e standard introduces a novel Medium Access Control (MAC) protocol called Time Slotted Channel Hopping (TSCH). Designing a centralized scheduling system that simultaneously achieves the required Quality of Service (QoS) is challenging due to the multi-objective optimization nature of the problem. This paper introduces a novel optimization algorithm, QoS-aware Multi-objective enhanced Differential Evolution optimization (QMDE), designed to handle the QoS metrics, such as delay and packet loss, across multiple services in heterogeneous networks while also achieving the anticipated service throughput. Through co-simulation between TSCH-SIM and Matlab, R2023a we conducted multiple simulations across diverse sensor network topologies and industrial QoS scenarios. The evaluation results illustrate that an optimal schedule generated by QMDE can effectively fulfill the QoS requirements of closed-loop supervisory control and condition monitoring industrial services in sensor networks from 16 to 100 nodes. Through extensive simulations and comparative evaluations against the Traffic-Aware Scheduling Algorithm (TASA), this study reveals the superior performance of QMDE, achieving significant enhancements in both Packet Delivery Ratio (PDR) and delay metrics.

Comparison of Configurable Modular Two-Level and Three-Level Isolated Bidirectional DC–DC Converters for Super-Capacitor Charging in DC Shore Power Systems

Compared to the AC counterpart, the DC shore power system provides a significant advantage of efficient power supply from renewable sources to ships and onshore loads. Super-capacitors serve as key energy storage units in such a system to buffer the power fluctuations and collect the regenerative energy. However, the ultra-wide voltage range of super-capacitors imposes a significant challenge in the topology selection and efficiency optimization of the interfacing isolated bidirectional DC–DC converter. To tackle this challenge, this paper analyzes and compares two promising converter topologies, which are a configurable modular two-level dual-active bridge (CM-2L-DAB) and a three-level dual-active bridge (3L-DAB). To facilitate an ultra-wide voltage range, extended phase-shift (EPS) modulation in conjunction with the topology reconfiguration is analyzed for the CM-2L-DAB, while a hybrid modulation scheme is proposed for the 3L-DAB. A unified design approach is provided for both topologies, which also yields to the power loss modeling. On this basis, the CM-2L-DAB and 3L-DAB are thoroughly compared in terms of the modulation schemes, current stress, soft-switching operation, power conversion efficiency, material usage, closed-loop control scheme, and reliability. A prominent conclusion can be drawn that the CM-2L-DAB provides a higher efficiency than the 3L-DAB over the whole voltage range, but it relies on additional relays to reconfigure its topology which results in lower reliability and dynamic performance than the 3L-DAB.