Control Method For Systems Research Articles

An effective PID control method of air conditioning system for electric drive workshop based on IBK-IFNN two-stage optimization

A proportional-integral-derivative (PID) controller is a commonly used method for controlling air conditioning systems in electric drive workshops. However, traditional PID controllers have several drawbacks, such as poor control performances, weak adaptive abilities, and bad anti-interference capabilities, which render them unsuitable for the strict environmental requirements of electric drive workshops. Therefore, to compensate for the above deficiencies, first, this study presents a two-stage PID optimization control method, which includes optimizing the fuzzy neural network in the first stage and optimizing the PID controller parameters in the second stage. After that, a two-stage optimization algorithm based on an improved black-winged kite with an improved fuzzy neural network (IBK-IFNN) is designed to adapt the proposed control model. Finally, co-simulation experiments and applications are conducted in the electric drive workshop of an automobile manufacturer to validate the effectiveness of the proposed method. The results demonstrate that the proposed method not only improves the convergence speed and search capability of the IBK-FNN algorithm but also outperforms other controllers in terms of its control performance, adaptive ability, anti-interference capability, and comprehensive score.

An Optimized Multi-Level Control Method for Wireless Power Transfer System Using the Particle Swarm Optimization Algorithm

A Wireless Power Transfer (WPT) system, known for its contactless power delivery, is extensively used for power supply in spacecraft applications. Achieving efficient and stable power transfer necessitates the integration of DC/DC converters on both the primary and secondary sides of WPT systems for power conversion and control. Traditional efficiency optimization methods primarily focus on impedance matching within the wireless power resonance network, often neglecting the overall efficiency optimization of multi-stage DC-DC and WPT systems. This oversight results in suboptimal overall system efficiency despite optimal efficiency in the wireless transmission segment. Additionally, the time-varying nature of mutual inductance and load parameters during power transmission in WPT systems presents challenges for maximum efficiency tracking and power control. This paper introduces a multi-level coordinated control efficiency optimization method for WPT systems utilizing the particle swarm optimization (PSO) algorithm. This method takes into account the transmission losses across all power conversion units within the WPT system, establishing a mathematical model for the joint optimization of overall system transmission efficiency and power. The PSO algorithm is then employed to solve this optimization model using estimated mutual inductance and load values. By adjusting the DC/DC converters on both sides, the method ensures optimal overall system efficiency and consistent power transmission. Experimental results indicate that under varying load and mutual inductance conditions, a Series–Series (SS) compensated WPT system using this method achieves a 200 W power output with maximum efficiency tracking, a power output error of 0.63%, and an average transmission efficiency of 86.2%. This demonstrates superior power transmission stability and higher efficiency compared to traditional impedance matching methods.

Data-enhanced convolutional network based on air conditioning system start/stop time prediction

Most enterprise workshop operators frequently adjust the start/stop time of air conditioning systems based on indoor and outdoor temperatures and humidity to accommodate changing demand and weather conditions. However, relying on personal subjective experience for these adjustments often leads to operational delays or energy waste due to the lack of precision in determining optimal timing. Predicting air conditioning system start and stop times is crucial for energy consumption and savings in HVAC systems. Traditional data-driven methods have been insufficient in this regard, as they mainly focus on feature mapping and overlook the dynamic coupling relationships of process variables, resulting in subpar predictions. In response to this challenge, the paper introduces a novel approach known as the Periodicity and Long-Term Convolutional Neural Network (PLCNN). This method converts one-dimensional regression prediction data into two-dimensional data containing time series features to capture the dynamic coupling characteristics of the air conditioning system while maintaining the independent variation relationships of features. Experimental results using real factory floor data have demonstrated the superior performance of the PLCNN method. Specifically, this method achieved a 14.96% lower error rate compared to the traditional method and an 8.18% improvement compared to the deep learning method. Moreover, the implementation of the PLCNN method in the optimal control of air conditioning systems led to a significant 19.43% reduction in total monthly energy consumption. In conclusion, the proposed method offers a promising alternative to traditional approaches to forecasting and provides a solution to the common challenges encountered in traditional prediction tasks.

A study of automatic output power control methods for stand-alone photovoltaic power generation systems

Abstract The conventional power generation system’s output power automatic control structure is generally unidirectional, and the control coverage is small, leading to the increase in the random error obtained in the final test. Therefore, the design and research of this article are proposed. According to the current control demand, the generation system shall be stabilized first, and the power compensation shall be calculated. The multi-step mode shall be adopted to improve the control coverage and design the multi-step automatic control structure. According to this, it is constructed, and the automatic control processing is realized using jump balance. The test results show that compared with the traditional DFIG-DRWT system power output automatic control method and the traditional three-phase asynchronous generation system power output automatic control method, the random error finally obtained by the designed independent photovoltaic power station output power automatic control method is relatively effective. This indicates that the application effect and coverage of the designed power automatic control method are high, that controllability has been significantly improved, and that control flexibility has also been strengthened.

Self-triggered consensus resilient control for multi-agent systems against sensor deception attacks based on a single parameter learning method

This paper presents a self-triggered consensus resilient control method for nonlinear multi-agent systems (MASs) under sensor deception attacks. A single parameter learning method is integrated into backstepping technique to simplify design procedure. The neural networks are utilized to compensate for unknown dynamics of the MASs. Moreover, a self-triggered mechanism is presented for MASs to refrain from continuously monitoring triggering conditions and conserve communication resources. The designed controller can resist sensor deception attacks and guarantee that all signals of the MASs are uniformly bounded. An expository simulation example reveals the virtue of the presented method.

An enhanced sensitivity‐based combined control method of battery energy storage systems for voltage regulation in PV‐rich residential distribution networks

AbstractCommercial off‐the‐shelf (OTS) photovoltaic systems coupled with battery energy storage units (PV‐BES) are typically designed to increase household self‐consumption, neglecting their potential for voltage regulation in low voltage distribution networks (LVDNs). This work proposes an enhanced sensitivity‐based combined (ESC) control method for voltage regulation, using BES control as level 1 and reactive power compensation as level 2. A centralized controller manages charging/discharging intervals, while local inverters handle real‐time power rates and reactive power, ensuring effective LVDN voltage regulation. The BES set points are obtained concerning the measured local bus voltage and according to enhanced sensitivity coefficients. The enhancement algorithm ensures that the full capacity of BES is utilized and that there is adequate capacity during charging and discharging time intervals. The proposed method, tested on 8‐bus and 116‐bus LV test feeders, outperforms OTS and an adaptive decentralized (AD) control method by completely preventing overvoltage issues, minimizing various changes in the direction of BES power, and reducing voltage deviation without significantly affecting consumers' grid dependency.

Emergency frequency control method for power system containing wind generation based on coordination of frequency-autonomous-response, constant-power-control-switching and generator-tripping or load-shedding

Wind generators have the ability to quickly and flexibly control power, improving the freedom degree in emergency frequency control within the power system. The emergency frequency support can be provided to the grid by autonomously emulating synchronous generators that respond to frequency changes, constant-power-control-switching (CPCS) with advanced power reserves, generator-tripping (GT) or load-shedding (LS). However, these control methods are not always effectively coordinated, the frequency control potential of wind generators is not fully utilized due to the difficulty in quantifying security conditions and frequency control capability. Therefore, A novel concept, termed synthetic autonomous regulating speed proposed as a means of characterizing the frequency-autonomous-response (FAR) capability of the WG. A method for quantifying frequency security conditions of the power system containing WGs is proposed by establishing the dynamic frequency security domain under the joint effect of FAR of the WG and the SG, CPCS of the WG, and GT or LS. Thus, an improved emergency frequency control method based on the coordination between FAR, GT or LS, and CPCS of the wind generator is provided. Case studies demonstrate that this method can effectively reduce the amounts of GT or LS and CPCS while ensuring frequency security.

Design and development of an advanced gas storage device and control method for a novel compressed CO2 energy storage system

Compressed CO2 energy storage (CCES) has advantages over compressed air in energy density and efficiency. Compared to air, CO2 needs to be in a closed-loop cycle in the CCES. The development of CCES is constrained by low-pressure CO2 storage. Storage density can be increased by adsorption without changing pressure. However, the control of the flow rate during adsorption/desorption has rarely been studied experimentally. In this paper, an adsorption tower for CCES is proposed and the regulation of the desorption flow rate is experimentally investigated. Using temperature-swing adsorption, the storage and release can be regulated without reducing the energy storage capacity. During the desorption process, the temperature of the exhaust gas can also be maintained stably. The adsorbent with a volume of 51.81 L was able to release 6121.9 g of CO2 when heated to 200 °C. Under the same pressure conditions, the effective storage density of the tower can reach 118.2 g/L, which is 67.5 times higher than the gaseous density (1.75 g/L, 1 bar, 303 K). The desorption flow rate is linearly related to the forward speed of the front in the adsorption tower. The flow rate can be regulated by controlling the superficial gas velocity and the desorption temperature. This method can match the flow rate of the adsorption tower with the gas consumption of the compressor, providing a novel solution for CCES.

Optimization of solid oxide electrolysis cells using concentrated solar-thermal energy storage: A hybrid deep learning approach

The Solid Oxide Electrolysis Cell (SOEC) represents a cutting-edge solution for the conversion of CO2 and H2O into syngas, offering significant economic and environmental benefits. However, the process requires substantial high-temperature heat inputs, traditionally supplied by electricity. This study introduces a novel approach leveraging concentrated solar radiation as a renewable heat source for SOEC, addressing the challenge of its inherent fluctuations through the integration of Thermal Energy Storage (TES) systems. We propose a hybrid model that combines multi-physics simulation with a deep learning algorithm, enabling rapid optimization of the electrolysis process under real-time direct normal irradiance conditions. Our findings demonstrate that the inclusion of TES within the system architecture results in a remarkable 53 % reduction in temperature variation rate at the SOEC inlet, ensuring operational stability and efficiency. Furthermore, by fine-tuning capacity parameters, we have developed a control strategy that harmonizes efficiency with safety performance. The robustness of our system is underscored by its resilience to step changes, achieving a 75 % reduction in temperature fluctuations. This research contributes a pioneering method for the real-time optimization and control of SOEC systems, harnessing the power of TES to drive sustainable energy conversion with enhanced reliability and economic viability, facilitating precise and swift predictive capabilities even under dynamic operating conditions.

Emergency control methods for power systems based on improved deep reinforcement learning

Abstract In order to achieve fast and accurate transient stability analysis and emergency control, this paper proposes a transient stability emergency control method based on improved deep reinforcement learning. In order to fully explore the temporal and spatial variation trend of transient response, a multi-dimensional feature containing information such as transient situation energy is constructed, and the deep reinforcement learning model is transformed based on the time-space graph neural network. On this basis, an emergency control model is constructed, and the power grid knowledge is integrated into the emergency control decision-making scheme to reduce the exploration of invalid decision-making and improve the performance of the model. The effectiveness of the proposed method is verified in the IEEE-39 system.

Research on trajectory tracking control method for agricultural robot permanent magnet synchronous motor servo system based on improved EMD threshold wavelet filtering

In agricultural robots, trajectory tracking can be affected by disturbances such as road slopes or bumps, leading to sudden changes in path curvature and reduced control accuracy. To address this issue, we propose a servo motor drive control method based on an improved Empirical Mode Decomposition (EMD) threshold. A mathematical model integrating the drive and control of the servo motor is established, and the driving performance of the servo motor is analyzed. By detecting the speed and position of the servo motor, the specified three-phase current values for motor drive control are derived. The actual current of the motor is collected using Hall current sensors and denoised with an improved EMD threshold wavelet filtering method. Within the servo motor drive control model, the system calculates the difference between the given three-phase current values and the actual motor current, and this difference is then used to adjust the motor control via an input linear amplifier, ensuring integrated drive control. Simulation and experimental results show that the proposed trajectory tracking control method for the agricultural robot’s permanent magnet synchronous motor servo system exhibits high control accuracy, strong anti-interference ability, and good performance, making it more suitable for practical applications.

Iterative control framework with application to guidance and attitude control of rockets

Traditional control methods for nonlinear dynamical systems are predicated on verification of complex mathematical conditions related to the existence of a positive-definite Lyapunov function whose value must strictly decrease with time. Rigorous verification of Lyapunov conditions can be extremely difficult in real-world systems with high-dimensional and complex dynamics. In this paper, we present a novel control logic that can be readily applied to a general class of nonlinear systems irrespective of the complexities in their dynamics. The Iterative Control Framework (ICF) is designed to guarantee the convergence of the closed-loop system state to zero without a priori verification of Lyapunov-like conditions. The underlying computational routine runs in the background in real time and reconfigures the control vector at each time step in such a way that when the control input is applied to the system, the system trajectory reaches closer to the desired state. The technique is applicable to a broad class of complex nonlinear systems but is particularly suitable for systems inherently admitting control action of short duration such as missiles, rockets, satellites, and space vehicles. In this work, we focus on the application of ICF to guidance and attitude control of rockets and missiles where actuation is provided via single-use thrusters.

A fast fixed-time backstepping control method for systems with mismatched disturbances

In this paper, a fast fixed-time backstepping control approach is developed for systems with mismatched disturbances. A novel fast fixed-time disturbance observer (FFTDO), which is convergent within fixed time regardless of initial conditions, is designed to efficiently estimate and compensate the disturbances. On the basis of FFTDO, a novel fast fixed-time backstepping control scheme is proposed, which can guarantee fast fixed-time convergence and high steady-state precision. In addition, a novel fixed-time filter is used to circumvent the problem of “explosion and complexity,” and to ensure overall fixed-time convergence. The fixed-time stability of the closed-loop system under the proposed controller is proved by the Lyapunov stability theory. The simulation results are carried out to confirm the feasibility and superiority of the proposed control strategy.

Objective Sliding Mode Control for the Flotation Motor Replacement Process Based on a Stepwise Stochastic Configuration Network

Flotation motors face constant vibration and friction in the working process, resulting in wear and often need to be replaced, reducing production efficiency. In order to ensure the efficient operation of flotation equipment, we designed a manipulator to assist the work. The manipulator model is difficult to be accurate, and the diversity of external information brings great challenges to the precise control of the manipulator. Therefore, an Objective Sliding Mode Control (OSMC) strategy combined with Stepwise Stochastic Configuration Networks (SSCNs) is developed to solve the above problems while reducing the oscillation of the system. The SSCN is distinguished by its innovative node selection mechanism for the hidden layer, which operates in two distinct phases: initially, ‘single time’, where weights and biases are randomly assigned through a uniform distribution, and subsequently, ‘double time’, where these parameters are adjusted based on a normal distribution centered around the values selected in the first phase. This approach enhances the system’s robustness by incorporating a disturbance observer based on SSCN, designed to accurately estimate and compensate for system uncertainties. The simulation model realistically integrates these uncertainties and disturbances, demonstrating the effectiveness of the proposed OSMC in satisfying the stringent control requirements of mechanical arms under uncertain conditions. This advancement offers a promising new method for the precise control of robotic systems in challenging environments.

An artificial intelligence-assisted digital microfluidic system for multistate droplet control

Digital microfluidics (DMF) is a versatile technique for parallel and field-programmable control of individual droplets. Given the high level of variability in droplet manipulation, it is essential to establish self-adaptive and intelligent control methods for DMF systems that are informed by the transient state of droplets and their interactions. However, most related studies focus on droplet localization and shape recognition. In this study, we develop the AI-assisted DMF framework μDropAI for multistate droplet control on the basis of droplet morphology. The semantic segmentation model is integrated into our custom-designed DMF system to recognize the droplet states and their interactions for feedback control with a state machine. The proposed model has strong flexibility and can recognize droplets of different colors and shapes with an error rate of less than 0.63%; it enables control of droplets without user intervention. The coefficient of variation (CV) of the volumes of split droplets can be limited to 2.74%, which is lower than the CV of traditional dispensed droplets, contributing to an improvement in the precision of volume control for droplet splitting. The proposed system inspires the development of semantic-driven DMF systems that can interface with multimodal large language models (MLLMs) for fully automatic control.

Robust fractional-order load frequency control for hybrid power system concerning high wind penetration

Hybrid power systems concerning wind farms have witnessed significant dynamic characteristic changes due to unpredicted generation output and integration mode conversion between frequency-sensitive wind plants and non-scheduled ones. This work presents a robust fractional-order load frequency control (LFC) method for a multi-area hybrid power system in the presence of wind penetration uncertainties. To characterize the injection effect of wind farms, the power system is described by a model with an uncertain wind penetration factor in a pre-estimated range. Then, an enhancement of Levy’s identification method is developed to convert this model into a fractional-order reduced structure to design the load frequency controller, which is in a form of fractional-order PID with a filter for ease of engineering implementation. The closed-loop stability is analyzed by the stability boundary locus (SBL) method under different wind penetration levels. It is shown that superior frequency response performance and system robustness can be obtained if the controller is designed in terms of the model with the worse-case wind penetration. Illustrative examples including a three-area hybrid power system under a variety of wind penetration variations are given to show the effectiveness of the proposed method.

A Novel Impedance-Based Parallel Cooperative Control Method for Front and Rear Landing Gear Hydraulic Systems of UAVs

Cargo handling issues affect the ability of large heavy-duty Unmanned Aerial Vehicles (UAVs) to transport cargo and limit the development of large UAVs. Compared to conventional landing gear, hydraulically controlled landing gear can tilt the drone within a specified angle, facilitating smoother loading and unloading of goods. Therefore, it is important to study the hydraulic landing gear control system for a UAV to make the UAV’s tilt possible. In this paper, an impedance-based parallel cooperative control method for front and rear landing gear hydraulic systems of large heavy-duty UAVs is presented, which can achieve UAV tilting within a reasonable angle during the loading and unloading of cargoes by large, heavy-duty UAVs. This paper establishes the physical model of the UAV’s landing gear, the mathematical model of the hydraulic system, and the kinematic model of the airframe. Through kinematic analysis, the correlation between each hydraulic dive unit’s (HDU’s) extension length in the landing gear and the UAV’s tilt angle is established. This paper introduces a two-fold based-loop parallel control technique, featuring angle based-loop control for the UAV’s front and position based-loop control for its rear landing gear. It aims to enable the UAV to freely tilt for loading and unloading cargo at a predetermined angle, by measuring the UAV’s tilting angle, the HDU’s force exerted on the landing gear, and its positional parameters. Ultimately, the practicality of this technique is confirmed through simulations and experiments.

Lyapunov-based prescribed-time stabilisation control of quantum systems

This paper addresses the prescribed-time stabilisation control problem for quantum systems characterised by a single control input. A control law based on the relative state distance is formulated. Through the application of prescribed-time Lyapunov control techniques, the stabilisation of the quantum system to one of the eigenstates of the internal Hamiltonian within a prescribed-time is established. Notably, in contrast to existing quantum system control methods, our proposed approach ensures stabilisation within a specified time, allowing for the flexible selection of the settling-time upper bound according to preference, independent of the initial conditions.

Efficient predictive control method for ORC waste heat recovery system based on recurrent neural network

The model predictive control performs well in regulating operating parameters and ensuring system safety when the organic Rankine cycles are operating with variable heat sources. However, traditional nonlinear predictive control based on the organic Rankine cycle mechanism model involves significant computational complexity, making it challenging to quickly find a control solution. This limitation hinders its application in the organic Rankine cycle for rapid response control. To address this issue, a fast model predictive control method is proposed in this work. A recurrent neural network model is well-trained using the input–output data of the organic Rankine cycle process, and it is used as a surrogate model in the design of the model predictive controller for the control of organic Rankine cycle operating parameters. The formulated optimal control problem is then transformed into a mixed integer linear programming problem, which can obtain high-quality and fast solutions during the control process. Through comparison with recurrent neural network-based nonlinear predictive control and pseudo-sequential method-based fast nonlinear predictive control, the results show that the designed controller can effectively accomplish the control of organic Rankine cycle operating parameters with smaller overshoot. Moreover, its average control solution time is shorter by 89.59% and 93.27% respectively while the total net output power of the system during the control process is 0.54% and 1.3% higher than that of the other two controllers. It exhibits superior control performance, even under variable waste heat conditions.

Predefined time quasi-sliding mode control with fast convergence based on a switchable exponent for nonlinear systems

This paper proposed a new predefined time nonsingular sliding mode control (SMC) method for nonlinear systems. Firstly, based on the definition of predefined time stability (PTS), a new sufficient condition is designed to ensure that the system states converge to the origin within a predefined time. The design of a simple variable exponent not only guarantees PTS, but also enables adaptive adjustment when the system states are far away from and near the equilibrium point. And compared with traditional methods, the proposed Lemma 2 enhances the control effect and achieves faster convergence whether the system states are far from or near to the equilibrium point. Secondly, based on the proposed stability condition, a new nonsingular SMC method is designed to ensure that the tracking error converges to an arbitrarily small region within a predefined time. Finally, the proposed method is verified to have good performance through simulation and physical experiments.