Control Performance Research Articles

Improved intelligent model predictive controller for the nuclear power reactor system

Abstract Over the last two decades, extensive research has been conducted to enhance the control performance of reactor power. Various methodologies have been suggested and implemented to achieve optimal power control in nuclear reactors. However, due to the diverse characteristics and inherent uncertainties in the models, devising optimal controllers for nuclear systems remains a complex task. To address this, numerous approaches have been adopted to ensure controllability and resilience, aiming for an optimal nuclear power reactor controller. The Model Predictive Control (MPC) algorithm has garnered significant attention as a viable approach to boost operational efficiency and overall system utility. In this research, Particle Swarm Optimization (PSO) method and MPC controller are combined together to form a novel algorithm termed PSO-MPC, aiming to amplify the system’s performance and overcomes the local minima problem that basically happens when using MPC controller. First, MPC controller is applied to the nuclear reactor model, then the suggested technique PSO-MPC also is applied to the system and a comparison of the outcomes using both techniques is done. The results demonstrate enhanced system response using the innovative technique.

Distributed model predictive control for asynchronous multiagent systems with self‐triggered coordinator

AbstractThis paper investigates a distributed model predictive control strategy for asynchronous nonlinear multiagent systems (MASs) with external disturbances via self‐triggered generators and prediction horizon regulators. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enhance the robustness of the system. Then, the triggering interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade‐off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the MASs is achieved under asynchronous communication. Finally, the recursive feasibility and closed‐loop stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach.

Shaking Table Test Studies on Adaptive-Passive Variable Frequency Gas-Spring DVA for Structural Seismic Response Mitigation

The control performance of a passive dynamic vibration absorber (DVA) is known to be sensitive to its frequency deviation. This study upgrades the conventional device and proposes an adaptive-passive variable frequency gas-spring DVA (APVF-GSDVA) for controlling the structural seismic response, while an adaptive control algorithm is developed that requires only a single input signal. The DVA’s vibration frequency is precisely modulated by adjusting the gas pressure in the gas-spring, aligning it with the inherent frequency of the multi-degree-of-freedom (MDOF) structure. The working principle and control algorithm of the APVF system is introduced and derived in detail, the corresponding gas-spring DVA and implementation are designed, and the configuration of the entire control system is realized. An adaptive variable frequency test scheme is designed and the frequency retuning function of the prototype APVF-GSDVA device installed on an MDOF structure is experimentally validated. The seismic response control effectiveness of the retuned DVA is demonstrated by comparing the detuned DVA, highlighting the necessity of implementing adaptive frequency adjustment of the gas-spring DVA. Experimental findings demonstrate that APVF-GSDVA accurately identifies the inherent frequency of the MDOF structure in ambient excitation, it then adjusts the pressure of the gas-spring according to the frequency-pressure relationship, optimally retuning the DVA. A retuned GSDVA can effectively suppress the structural seismic responses, compared to a detuned DVA, its control effectiveness on peak and RMS responses can be improved by a maximum of nearly 13.5% and 53.3% at a frequency deviation of 10%. Furthermore, results from two “detuning-retuning” test paths with distinct seismic excitations indicate that the retuned DVA exhibits superior control effectiveness, underscoring the significance of the adaptive frequency adjustment function in passive DVAs.

Exact Solution of Nonlinear TVMD Based on Maximizing Damping Enhancement Effect for Acceleration Response Control

The tuned viscous mass damper (TVMD) exhibits superior control effect and energy dissipation capability compared to the viscous damper (VD). Existing literature has primarily focused on the control of absolute acceleration response in structures by linear TVMD, with limited research on nonlinear damper for controlling structural performance and efficiency. This study proposes a methodology for determining the optimal parameters of linear TVMD. The closed-form solution of the nonlinear TVMD is derived through stochastic linearization based on the linear TVMD, aiming to minimize the damping ratio of TVMD by utilizing the damping enhancement effect of the system. For lightly damped structures, the closed-form solution remains a reliable method for accurately approximating TVMD design parameters. Compared to traditional fixed-point theory, the TVMD developed through the proposed methodology demonstrates superior control performance, especially in terms of absolute acceleration, with a smaller damping ratio and minimal deviation from the optimal state of the TVMD. Additionally, the study investigated the influence of the damping index on the optimal parameters and the efficacy of the TVMD in vibration control. It is observed that reducing the damping index can improve structural performance, particularly in long-period structures with small mass ratios, although it may reduce the efficiency of the TVMD. Notably, TVMDs with lower mass ratios maintain higher efficiency regardless of the structural natural period. The damping index does not affect the optimal frequency ratio or the mean square response. Ultimately, the effectiveness of the optimization method is displayed through the examination of response spectra following 22 real seismic events, showcasing its adaptability across various structural types. The comparison of the root-mean-square response between the VD and TVMD across different periods shows consistent results, with a maximum deviation of only 10%. The TVMD is highly effective at regulating the structural response, excelling particularly in controlling the absolute acceleration of long-period structures, where it reduces maximum absolute acceleration by 20% more than displacement. The proposed exact solution rapidly and precisely identifies the design parameters of nonlinear TVMD by the desired specifications, thereby offering theoretical guidance for practical engineering applications.

Attitude Control System for Quadrotor Using Robust Monte Carlo Model Predictive Control

Monte Carlo Model Predictive Control (MCMPC) is a kind of non-linear Model Predictive Control (MPC) that determines control inputs using the Monte Carlo method. The Monte Carlo method used in MCMPC can handle discontinuous phenomena, and there have been reports of its application in control systems for quadrotors, where the objective is to maintain the stability of the attitude during collisions. However, as with conventional MPC, concerns remain about control performance degradation due to modeling errors and external disturbances such as unexpected wind gusts. In this study, we propose a novel Robust Monte Carlo Model Predictive Control (RMCMPC), which improves the robustness of MCMPC by considering the hypersurface known from the Sliding Mode Control (SMC) theory. The proposed RMCMPC is applied to a quadrotor attitude control system, and its effectiveness is validated through numerical simulations.

A Novel Enhanced Fire Hawks Optimization Approach for Improving the Efficiency of Converter‐Based Controllers in Switched Reluctance Motors

ABSTRACTSwitched reluctance motors (SRMs) have gained popularity in various industrial applications due to their advantages, including structural simplicity, high reliability, low cost, and operational stability over a wide speed range without relying on rare‐earth permanent magnet materials. However, these motors exhibit drawbacks such as weak torque density, low efficiency, and significant torque ripple, particularly in high‐speed operation. An efficient converter‐based control approach is proposed to manage speed and torque variations in SRM motors, addressing these issues. A multilevel converter (MC) is introduced as a fundamental component. The novel multilevel converter (NMC) accommodates SRMs with varying numbers of phases and exhibits quick demagnetization and excitation behaviors, enabling independent operation of each phase even during conduction overlaps. Subsequently, an effective controller is developed for the SRM motor, combining proportional integral derivative (PID) control with enhanced fire hawks optimization (EFHO). The EFHO optimizes the PID gain values to enhance controller performance. The converter operation minimizes torque ripple and facilitates speed management. The EFHO technique is a fusion of fire hawks optimization (FHO) and the sine cosine algorithm (SCA). This amalgamation improves the updating process of FHO by incorporating SCA. The proposed methodology is implemented in MATLAB and evaluated through various metrics, including SRM motor current, voltage, speed, and torque, under electric vehicle (EV) load conditions. Performance comparisons are conducted with traditional optimization algorithms such as the whale optimization algorithm (WOA) and ant colony optimization (ACO). The results validate the effectiveness of the proposed methodology in achieving improved SRM motor control and performance.

Synthesis and characterization of waterborne polyurethane‐based marine antifouling coating modified via glycerol laurate

AbstractMarine biofouling is a major international challenge since it is responsible for several environmental problems and economic losses. Developing a novel antifouling coating with good water resistance, adhesion, and controlled degradation performance is crucial to solving the problem. Herein, a series of waterborne polyurethane emulsions were synthesized using the prepolymerization chain expansion and self‐emulsification method, with varying contents of glycerol laurate (GL) as the post‐chain extender. The findings demonstrated a notable enhancement in the controllable degradation performance, adhesion, marine antifouling properties, and water resistance with an increase in the concentration of GL. In particular, the film exhibited the most optimal overall properties when the introduction amount of GL was 2 wt%. The absorption rate of the artificial seawater (ASW) reached a state of equilibrium when GWPU2 was immersed in ASW for 60 days, with a final value of approximately 18.5%. The release rates of the GWPU2 antifouling coatings were observed to be the lowest at approximately 120 days, with a rate of approximately 37.59 μg/cm2/day. The long‐term antifouling capability of the GWPU resins was demonstrated in marine field tests, wherein they exhibited sustained antifouling activity for a period exceeding 9 months. In particular, GWPU2 exhibited noteworthy antifouling performance.

Terminal Sliding Mode Control of Microgrid Inverter Systems

ABSTRACTTo enhance the power quality of microgrid inverters and reduce the influence of changes in inductance parameters and external disturbances on the direct power control of the inverter system, a terminal sliding mode control strategy with a variable exponential power reaching law has been proposed. The designed new reaching law comprises a variable exponential term and an enhanced power term. The variable exponential term contains an arctangent function, and the power term coefficient is replaced by a function about the sliding mode. Therefore, the convergence rate can be adaptively adjusted based on different stages of the system, ensuring a faster rate of convergence throughout the process of approaching the sliding mode surface. To weaken the effects of changes in inductance parameters, a power disturbance observer is designed to estimate the internal disturbances induced by the filtered inductance in the system. Subsequently, a sliding mode control law containing disturbance observations is derived. Moreover, a variable exponential terminal sliding surface is designed to adjust the convergence rate of system errors on the sliding surface in stages, thereby enhancing the control performance of the system. The simulation results show that the new reaching law has faster convergence rate and better dynamic performance. The convergence speed of the system error can be accelerated by the designed variable exponential terminal sliding surface. The sliding mode control strategy with the variable exponential power reaching law is applicable to the power control system of three‐phase inverters in microgrids, thereby significantly enhancing the dynamic performance and robustness of the system.

Optimization of chemical engineering control for large-scale alkaline electrolysis systems based on renewable energy sources

In the context of renewable energy, dynamic control is crucial for large-scale alkaline water electrolysis systems. Under traditional Proportional-Integral-Derivative (PID) control, the system's temperature and impurity levels may exceed safe limits during sudden load changes, compromising system safety. Therefore, this study proposes a control strategy based on mathematical models of system temperature and impurities, including Model Predictive Control (MPC) for temperature and Optimal Curve Tracking (OCT) for impurities. The MPC controller offsets the disturbances in temperature caused by current fluctuations through state prediction, while the OCT controller ensures that impurity levels remain within safe limits. Real-time application of these control strategies is achieved through MATLAB-PLC communication, validating controller performance. Experimental results show that the MPC controller exhibits excellent dynamic response and stability in controlling the stack temperature, with a maximum temperature peak deviation of only 2.9 K and a temperature fluctuation range of 5.9 K. This represents a reduction of 1.6 K in peak deviation compared to traditional PID controllers and significantly decreases the temperature fluctuation range. The OCT controller also demonstrates good safety and stability in controlling hydrogen to oxygen (HTO) impurity levels, consistently maintaining the HTO content at the set upper limit of 1.5%, thereby expanding the safe operational range of the system and reducing the need for frequent adjustments to the system setpoints.

Engineering applications of artificial intelligence a knowledge-guided reinforcement learning method for lateral path tracking

Lateral Control algorithms in autonomous vehicles often necessitates an online fine-tuning procedure in the real world. While reinforcement learning (RL) enables vehicles to learn and improve the lateral control performance through repeated trial and error interactions with a dynamic environment, applying RL directly to safety-critical applications in real physical world is challenging because ensuring safety during the learning process remains difficult. To enable safe learning, a promising direction is to make use of previously gathered offline data, which is frequently accessible in engineering applications. In this context, this paper presents a set of knowledge-guided RL algorithms that can not only fully leverage the prior collected offline data without the need of a physics-based simulator, but also allow further online policy improvement in a smooth, safe and efficient manner. To evaluate the effectiveness of the proposed algorithms on a real controller, a hardware-in-the-loop and a miniature vehicle platform are built. Compared with the vanilla RL, behavior cloning and the existing controller, the proposed algorithms realize a closed-loop solution for lateral control problems from offline training to online fine-tuning, making it attractive for future similar RL-based controller to build upon.

Performance and Robustness of Physiological Controllers With Integrated Pressure Sensors on Inflow Cannula.

The control of left ventricular assist devices (LVADs) requires sensors and/or estimators to account for the physiological state of the patient and apply advanced controllers. Sensor characteristics are a challenge when using implantable pressure sensors because they influence the quality of physiological control and the robustness of the controlled system. The objective of this work is to investigate the performance and robustness of LVAD controllers that operate based on LVAD integrated pressure sensors. Four pressure-based LVAD controllers are tested with a HeartMate 3 that has an integrated pressure sensor. Controller sensitivity as well as robustness to sensor drift and noise are evaluated based on controller response to large changes in preload and afterload. A fail-safe strategy for sensor failure used also to investigate the reliable operation of the LVAD in realistic conditions. All tested controllers are sensitive to drifting pressure signals, which can lead to unstable behavior. The results show that two controllers are robust to noise. The other two controllers show high deviation and oscillation in cardiac output even for small noise. Additionally, a noticeable difference of the controller's response between simulated and measured pressure input was observed, indicating the need for a robust controller design. Reliable operation even in the event of a sensor failure was achieved on the basis of a fail-safe control system.

Meta-learning and proximal policy optimization driven two-stage emergency allocation strategy for multi-energy system against typhoon disasters

To achieve resilience improvement of the multi-energy system against typhoon disasters, this study designs a novel two-stage optimization framework that considers the emergency allocation of distributed resources under typhoon disasters to fully exploit the potential of distributed resources for resilience enhancement. Firstly, we formulate the emergency allocation of distributed resources as a Markov decision process. Then, a meta-learning-driven proximal policy optimization method is utilized to solve it. Different from that the existing reinforcement learning methods always ignore the unpredictable change caused by typhoon and keep multi-energy system dynamics invariant, limiting its control performance. The proposed method embeds meta-learning to fine-tune the pre-trained allocation policy to new tasks with high adaptability and few interactions. Finally, comparison results with other benchmark methods are carried out and shows that the proposed method can learn the appropriate resource allocation policy for multi-energy system and achieve better resilience enhancement, yielding fast application efficiency and good generalization ability for emergency fault conditions.

Optimization and evaluation of tuned inerter-based dampers for mitigating coupled responses of offshore wind turbines

To achieve lightweight vibration control in offshore wind turbines (OWTs), this study proposes the utilization of two types of tuned inerter-based dampers (TIBDs), namely, the tuned viscous mass damper (TVMD) and tuned inerter damper (TID), to mitigate the coupled responses of an OWT induced by wind and wave loads. First, a multi-degree-of-freedom (MDOF) lumped mass model is established to represent an OWT equipped with a TIBD. Then, a novel structural control module for TIBDs is developed and seamlessly integrated into the modular numerical analysis code OpenFAST. This enables fully coupled aero-hydro-servo-elastic simulations of the OWT-TIBD system. Using the established MDOF model, a TIBD parameter optimization design method is proposed to minimize the peak of the non-dimensional displacement frequency response function. Additionally, a parametric study is performed to investigate the effect of inertance and span height on the control performance. Finally, the performance of the TIBD in attenuating both fatigue and extreme loads is evaluated through nonlinear fully coupled time-domain simulations using OpenFAST and compared to traditional tuned mass dampers (TMDs). The results showed that a lightweight and small damper displacement TIBD is capable of effectively dampening OWT vibrations, yielding a better mitigation effect than that provided by a bulky TMD. Overall, the reduced installation requirements and the commendable control efficiency of a TIBD highlight its potential as a promising candidate for passive control devices in OWT applications.

Voltage control strategy of output bus of grid-connected port of energy router based on energy feedback

Abstract The remaining power conversion ports are equivalent to constant power loads for the grid-connected port of the voltage source. The negative impedance characteristics of the constant power loads will worsen the voltage source characteristics of the grid-connected port and even cause system instability. According to the influence mechanism of constant power load on the DC bus voltage control performance during grid-connection, a new voltage control strategy based on energy feedback is proposed in this paper. The simulation results show that the proposed control strategy can more effectively reduce the negative impedance characteristics of the energy router’s constant power load and improve the bus voltage’s control performance compared with the existing control strategies.

Study of Access Control Techniques on the Blockchain-enabled Secure Data Sharing Scheme in Edge Computing

Background: The pervasive adoption of edge computing is reshaping real-time big data analysis, smart city management, intelligent transportation, and various other domains. Its appeal lies in its distributed nature, decentralization, low latency, mobile support, and spatial awareness. However, this ubiquity exposes data to security threats, jeopardizing privacy and integrity. Consequently, access control assumes paramount importance in securing data sharing within edge computing and blockchain technologies. Methods: This research addresses this critical issue by conducting a comprehensive study on access control techniques within the context of edge computing and blockchain for secure data sharing. Our methodology commences with an exhaustive review of relevant articles, aiming to identify and expound upon gaps in existing research. Subsequently, we perform a meticulous analysis of access control methods, mechanisms, and performance metrics, seeking to establish a holistic understanding of the landscape. Results: The culmination of this research effort is a multifaceted contribution. We distill insights from a diverse range of access control schemes, shedding light on their nuances and effectiveness. Our analysis extends to evaluating the performance metrics vital for ensuring robust access control. Through this research, we also pinpoint critical research gaps within traditional access control methods, creating a foundation for innovative approaches to address the evolving challenges within edge computing and blockchain environments. Conclusion: In conclusion, this research venture paves the way for secure data sharing in edge computing and blockchain by offering a thorough examination of access control. The findings from this study are anticipated to guide future developments in access control techniques and facilitate the evolution of secure, privacy-conscious, and efficient data sharing practices in the dynamic landscape of digital technology.

Decoupled robust backstepping tracking control for variable stiffness actuated robot with input saturation

Due to the energy storage and release capability introduced by stiffness adjustment, a variable stiffness actuator is essential to achieve human-like energy efficiency for robots. However, it is not trivial to control the strongly coupled and nonlinear system, especially with highly dynamic stiffness variation. In this work, decoupled and robust command filtered backstepping tracking controllers for position and stiffness are proposed. Furthermore, a disturbance observer is introduced to estimate the lumped disturbances caused by directly decoupled dynamics and unmodeling errors. Since the input control torques can be easily saturated due to limited deformation of elastic elements, anti-windup compensation is introduced into the tracking control laws to reduce its negative influence. Thus, by combining the command filtered backstepping controller, disturbance observers, and anti-windup compensation, the stability proof of the proposed composite controller is provided. The tracking control performance is validated through simulations of multi-DoF variable stiffness actuated robots.

Optimization of a novel biomimetic vortex generator structure based on cavitation intensity and stability control

Cavitation phenomena in the fields of hydraulic machinery and underwater submersibles have brought numerous negative impacts, such as vibration noise and mechanical damage. To suppress cavitation intensity and reduce the negative effects of cavitation instability, a biomimetic vortex generator (BVG), arranged on the surface of hydrofoils, is proposed in this study. The renormalization group k-ε turbulence model with density correction and the Ffowcs Williams and Hawkings acoustic model are employed. The cavitation intensity and cavitation stability of the BVG hydrofoil at different arrangement densities are analyzed. Cavitation control performance can be enhanced by reducing the spacing between structures on the hydrofoil. However, an excessively high BVG arrangement density may cause large-scale cavities to collapse prematurely, promoting the formation of small-scale cavities. This exacerbates cavitation instability, intensifies high-frequency pressure oscillations, and consequently amplifies noise. To mitigate performance degradation in hydrofoils caused by high-density BVG arrangements, the effects of structural height on the reentrant jet and surface vortices are analyzed. As a result of BVG structural optimization, cavitation intensity is further reduced, and cavitation stability is improved. Compared to the baseline hydrofoil, the time-averaged vapor volume over three cavitation cycles is reduced by 22.68%, the overall sound pressure level at receiver F decreases by 5.7 dB, and the dominant frequency of S3h2 hydrofoil cavitation decreases by 1.87 Hz. Ultimately, the optimization of the BVG structure enhances cavitation stability and significantly reduces high-frequency noise caused by pressure fluctuations on the hydrofoil surface.

Ankle torque variance is a better indicator of balance control performance than plantar perceptual sensitivity threshold.

We explored whether ankle torque variability or plantar perceptual threshold explains human balance control more effectively. We hypothesized that ankle torque variance is a better indicator of center of pressure (COP) velocity variance than plantar perceptual sensitivity. Two conditions were tested: loaded (23-kg vest added) and unloaded, as loading should diminish plantar sensitivity and increase COP velocity variability. We created a linear feedback model to assess the noise change in the sensorimotor loop induced by loading. Plantar sensitivity was quantified using a psychophysical approach while participants stood barefoot. A linear motor applied a force impulse on the participant's heel. A "yes-no" method of limits was selected to identify plantar sole sensory thresholds in both conditions. We observed reduced plantar sensitivity in loaded compared with unloaded conditions. In the loaded condition, participants exhibited greater COP velocity variance, with significant positive Pearson's correlations confirming a substantial association between ankle torque and COP velocity variances for both loaded [variance accounted for (VAF): r2 = 44.56%, P = 0.018] and unloaded conditions (VAF: r2 = 58.83%, P = 0.004). No significant correlation existed between COP velocity variance and plantar sensitivity threshold for both loaded (VAF: r2 = 0.002%, P = 0.99) and unloaded conditions (VAF: r2 = 21.81%, P = 0.35). The model confirmed an ∼88% rise in sensorimotor loop noise in the loaded condition. Ankle torque variance assesses the precision of nonperceptual and perceptual detection mechanisms in evaluating whole body motions and the accuracy in converting sensory cues into ankle torque.NEW & NOTEWORTHY Plantar cutaneous information contributes to balance control by modulating motor commands, but plantar perceptual sensitivity is a suboptimal indicator of balance performance. Multiple sensory cues encode whole body dynamics, guiding sensorimotor mechanisms to minimize body sway variability. Ankle torque variance is proposed as a superior measure for explaining balance control performance and evaluating the sensorimotor loop's functioning in balance control.

A new DC bus voltage control strategy for energy routers based on distributed coordination optimization

Abstract The types of power ports of energy routers at the side of the distribution network platform are complex and have different characteristics, which makes the operation conditions of energy routers complex and changeable. Although many pieces of literature have proposed more relevant control strategies to effectively realize the efficient and stable operation of energy routers under the two basic working conditions of grid-connected and isolated islands. However, when considering various complex operating conditions such as start-up, overload/overcharge, sudden failure of distribution network, grid-connected, and inter-island switching, the reliable operation of the existing energy routers is still faced with great challenges. In this paper, a DC bus voltage control strategy based on distributed coordination optimization is proposed for typical operating conditions of energy routers. Simulation results show that this strategy can effectively achieve the DC bus voltage control performance of energy routers under various burst operating conditions and strengthen the robustness of output voltage of energy routers under isolated islands.

Path tracking and energy efficiency coordination control strategy for skid-steering unmanned ground vehicle

The skid steering unmanned ground vehicle (SUGV) plays an important role in extremely harsh environments. Improving the autonomous control capability and energy efficiency of SUGV is urgently needed. This article presents a skid steering-based path tracking control strategy. In the upper controller, an improved model-free sliding mode controller (APMS) is used to calculate the yaw moment for tracking control. On the lower controller, the Snow Ablation Optimizer (SAO) is used to distribute the output torque of the drive motors, taking longitudinal force, yaw moment and energy consumption into account. Finally, the designed controller is validated through simulation under different operating conditions. The results show that the proposed coordination controller achieves good control performance, increases energy efficiency and at the same time ensures tracking accuracy.