Control Method Research Articles

A time-efficient nonlinear control method for the hyperchaotic finance system synchronization

Irregular and complex behavior in the financial system can disrupt stability and smooth economic growth. It causes randomness within the system, generating chaos; hindering synchronization behaviour. Achieving smooth and rapid synchronization between two coupled hyperchaotic finance (HF) systems with lessened fluctuation of input and output signals is vital for continuing financial stability and fostering economic growth, a challenge addressed in this article. The paper proposes a novel time-efficient nonlinear control (TENLC) technique and investigates HF systems synchronization using the drive-response system (DRS) arrangement. The proposed TENLC strategy realizes fast and smooth synchronization behaviour between two coupled HF systems, reducing closed-loop state-variable trajectory oscillations. The controller is designed to retain the nonlinear components within the closed-loop system and does not depend on the system's parameters, simplifying the design and analysis process. The Lyapunov stability technique confirms the closed-loop's global stability at the origin. Proofs of mathematical analysis and computer-based simulation results validate the theoretical findings, showing that the presented TENLC strategy converges the state error trajectories to zero in a short transient time with lessened fluctuations for all signals. The comparative computer-based simulation analysis confirms that the presented TENLC approach outperforms other synchronization control techniques.

Distributed Estimator-Based Event-Triggered Neuro-Adaptive Control for Leader-Follower Consensus of Strict-Feedback Nonlinear Multiagent Systems.

This article investigates the leader-follower consensus problem for strict-feedback nonlinear multiagent systems under a dual-terminal event-triggered mechanism. Compared with the existing event-triggered recursive consensus control design, the primary contribution of this article is the development of a distributed estimator-based event-triggered neuro-adaptive consensus control methodology. In particular, by introducing a dynamic event-triggered communication mechanism without continuous monitoring neighbors' information, a novel distributed event-triggered estimator in chain form is constructed to provide the leader's information to the followers. Subsequently, the distributed estimator is utilized to consensus control via backstepping design. To further decrease information transmission, a neuro-adaptive control and an event-triggered mechanism setting on the control channel are codesigned via the function approximate approach. A theoretical analysis shows that all the closed-loop signals are bounded under the developed control methodology, and the estimation of the tracking error asymptotically converges to zero, i.e., the leader-follower consensus is guaranteed. Finally, simulation studies and comparisons are conducted to verify the effectiveness of the proposed control method.

A Model Predictive Control Method With Adaptive Weighting Factors for Enhancing Performance of Modular Multilevel Converters

A Model Predictive Control Method With Adaptive Weighting Factors for Enhancing Performance of Modular Multilevel Converters

Energy Consumption Optimization for the Cold Source System of a Hospital in Shanghai - Part II: Operation Control Strategy Using EnergyPlus

Background: Energy consumption is a common problem in hospital buildings, which consume twice that of other public buildings. Therefore, it is of great significance to study the control strategy for the efficient operation of the cold source system. Objective: The study aimed to explore an efficient operation control strategy for cold source system, and new technologies and patents have emerged for the same. This work, utilizing EnergyPlus, modelled and analyzed the cold source system in a Shanghai hospital to optimize its operation. Methods: The accuracy of the simulation was verified by comparing it with experimental data. Based on the simulation results, the factors influencing the energy efficiency of the cold source system were analyzed, and then the operation control strategy of the cold source system was obtained. The XGBoost was used to fit the simulation results, and the operation strategy under full operating conditions was obtained. Results: The simulated results indicated the average energy saving rates during the summer season of the chillers, the chilled water pumps, the cooling water pumps, and the cooling towers to be 6.5%, -4.0%, 38.3%, and 5.4%, respectively, under the optimal operation control strategy. The average system Coefficient of Performance (COP) of the cold source system was 5.9, and the total energy consumption was 957016.3 kW·h, which was 7.1 % energy saving compared to that under the original operation. Conclusion: The conclusions of this study could provide references for the hospital buildings’ cold source system and group control method. This study has important practical significance for the efficient control strategy of cold source systems.

Nonsingular Fixed-Time Asymptotic Consensus for Nonlinear Nonstrict-Feedback MASs.

In this article, a fuzzy adaptive fixed-time asymptotic consistent control scheme is developed for a class of nonlinear multiagent systems (NMASs) with a nonstrict-feedback (NSF) structure. In the control process, a fixed-time consistency control method without control singularity is proposed by combining fuzzy logic systems (FLSs) with good approximation capability, fixed-time stability theory, and plus power integration techniques. Then, by using Barbalat's Lemma, the asymptotic stability of tracking errors and the boundedness of the controlled systems are successfully achieved, which means that the tracking errors can converge to zero in a fixed time. Finally, the effectiveness of the designed control scheme is demonstrated by a simulation example.

Data-Driven Adaptive Predictive Control Method With Autotuned Weighting Factor for Nonlinear Systems Using Triangular Dynamic Linearization

Data-Driven Adaptive Predictive Control Method With Autotuned Weighting Factor for Nonlinear Systems Using Triangular Dynamic Linearization

The Use of Some Herbal Essential Oils Against Galleria mellonella Larvae and Testing of Bacillus thuringiensis Bacterium Isolated from Galleria mellonella Under Laboratory Conditions

Larvae of wax moths cause great damage in honey bee hives and especially in stored honeycombs. Biological control methods are especially important in the control of wax moth in warehouses, as they do not harm the bee, the product and the environment. This study was carried out to determine the effect of 5%, 10%, 25%, 45% and 55% of peppermint, thyme, nettle seed, and walnut herbal oils and GB1 Bacillus sp. (OR227363) against wax moth larvae (Galleria mellonella) (L1-L3) under laboratory conditions. For each group of fifteen larvae, four herbal oil and one bacterial trials were conducted and two control groups were formed. The trials were conducted in glass jars and the larvae were kept in an oven at 25°C temperature/75% relative humidity. Each jar was checked every day for two weeks and dead/viable larvae were recorded and the dead ones were removed from the jar. As a result of the dose trials, it was determined that the best dose was 2.835x109 cfu/mL for bacteria and 5% concentration of thyme and walnut oil for herbal oil. According to the data obtained, it is thought that GB1 Bacillus sp. isolate can be used as an alternative control method against wax moth larvae.

H ∞ consensus control via intermittent communication for nonlinear multi-agent systems with unknown parameters and external disturbances

The consensus control problem is investigated for time-delayed nonlinear leader-following multi-agent systems, in which the unknown parameters and external disturbances are considered. Instead of using only one control method, a control scheme combining periodically intermittent control with H ∞ performance is employed. By manipulating the algebraic graph theory and constructing appropriate Lyapunov–Krasovskii functionals, sufficient solutions assuring the exponential H ∞ consensus of the considered systems are established in the form of linear matrix inequalities. The communication graph among the followers is directed, which removes the restrictions on undirected graph or balanced graph in the literature. Simulation analyses are finally presented to verify the theoretical results.

Bayesian Method for Quality Control with Weibull Distribution

Weibull distribution is one of the continous probability density function. This distribution is known as a flexible distribution. One of it’s flexibility is can be transformed into other distributions such as exponential distribution depends on parameter selected. Similar to other distribution, Weibull distribution also characterized by cumulative distribution function, mean, variance and moment generating function. One of the well-known application of this distribution is in the field of quality control utilizing reliability data and the well-known tool in quality control is control chart. Therefore, reliability data is not follow normal distribution then Shewhart control chart cannot be applied. To solve this problem, control chart can be formed with the control limit is obtained by Bayesian method. In applying Bayesian method, prior distribution of shape parameter is assumed to be uniform distribution and variables for reliability is assumed to be invers of Weibull distribution. The prior distribution is combine with likelihood function then posterior distribution is obtained which in then used as the control limits by find it’s mean.

Experimental study on a novel split thermoelectric cooler of big temperature difference for combined cooling and heating supply

The improvement in maximum temperature difference is very important for thermoelectric cooler potential applications under severe and complex conditions. Currently, the maximum temperature difference of commercial single-stage thermoelectric modules represented is usually below 70 K and the energy efficiency is not high. In this paper, 12 kinds of split thermoelectric modules are developed according to different semiconductor and connector structures, and their performance is compared by experiments. The results show that the temperature difference of novel split thermoelectric cooler can exceed 200 K, which is more than 180 % higher than the currently commercial module (70 K), and it also has good performance under high ambient temperature. The new thermoelectric module can achieve the application of cold and hot ends energy on long-distance different occasions and has a module coefficient of performances of 2.68 and 1.29 under temperature differences of 57.43 K and 197.73 K, respectively. Meanwhile, a new asymmetric design method for independent control of hot and cold end temperatures is presented. It can achieve independent heat flux density design for cold and hot ends, and its performance can be further enhanced by regulating the temperature of connector.

Economic model predictive control for building HVAC system: A comparative analysis of model-based and data-driven approaches using the BOPTEST Framework

Model Predictive Control (MPC) has demonstrated its capability to significantly enhance energy efficiency while ensuring indoor comfort. This study focuses on the development and fair comparison of two Economic Model Predictive Control(EMPC) frameworks with the goal of optimizing demand response for utility cost reduction and ensuring thermal comfort in residential buildings. The first framework, known as RC-MPC, utilizes a reduced-order thermal Resistance–Capacitance network model to represent the thermal dynamics of the building. It formulates the optimization problem as a Linear Programming (LP) task, which is solved using CPLEX. The second framework, known as ANN-MPC, employs an Artificial Neural Network model capture the thermal behavior of the building, It addresses a non-convex optimization problem through projected stochastic gradient descent. To evaluate the performance of these frameworks, experiments are conducted using a single-zone dwelling within the BOPTEST framework. The results demonstrate that both approaches effectively reduce operational costs and enhance comfort, achieving comparable performance and outperforming the baseline Rule-Based Control (RBC) method. The discomfort reductions range from 30% to 95%, and energy expense savings range from 17% to 34%. Furthermore, the study examines the impact of various MPC hyperparameters. The findings highlight that a proper control horizon and formulating the objective function accurately are crucial for achieving optimal control performance. Additionally, tightening the comfort range is found to be important in managing the comfort violations, and the choice of solver can influence both the control performance and computational efficiency.

Adaptive Iterative Learning Tracking Control for Nonlinear Teleoperators with Input Saturation

Addressing input saturation, external disturbances, and uncertain system parameters, this paper investigates the position tracking control problem for bilateral teleoperation systems with a time delay communication channel. Based on a composite energy function, we propose an adaptive iterative learning control (AILC) method to achieve the objective of position tracking under the alignment condition. This extends the existing research on the control of nonlinear teleoperation systems with time delay. The saturation constraint property of the Softsign function ensures that no state of the system exceeds its constraints. The controller learns to simultaneously deal with the uncertainty of system parameters online, reject external disturbances, and eliminate positional errors along the time and iteration axes. All signals in the system for any constant time delay are proved to be bounded. Ultimately, the performance of the proposed controller is further verified through numerical simulations.

Integrated battery thermal and energy management for electric vehicles with hybrid energy storage system: A hierarchical approach

Battery cooling is crucial for electric vehicles’ thermal safety, energy consumption, and battery life in hot climatic conditions. For electric vehicles with battery/supercapacitor hybrid energy storage system, battery cooling is deeply coupled with load power split from the electrical-thermal-aging perspective, leading to challenging thermal and energy management issues. This paper proposes a hierarchical multi-horizon model predictive control (MH-MPC) method to optimize battery cooling and energy management simultaneously. First, the electrical-thermal-aging coupling relationship between battery cooling and energy management is systematically analyzed. Then, by decoupling a centralized MH-MPC, an upper-level MH-MPC is designed to optimize the battery capacity loss cost and battery cooling cost by generating optimal compressor power, then a lower-level MH-MPC tends to minimize the battery capacity loss cost by allocating the total load power demand. The prediction horizon and sampling time are determined. Numerical results show that, compared with the centralized method, the proposed hierarchical method provides a lower battery capacity loss for long-term driving with only about 20% computation burden. Compared with standalone energy management without battery cooling, the total cost can be reduced by 12%–16% under long-term driving. Compared with optimizing energy management with Bangbang cooling, the battery degradation and total costs can be reduced by 15%–52% under short-term driving without deteriorating long-term performance.

Coordinated voltage control for large-scale wind farms with ESS and SVG based on MPC considering wake effect

The wake effect reduces the wind speed at downstream wind turbines (WTs), making it necessary for the central controller to collect wind power generation data from each WT. However, wind farms (WFs) face a more complex problem in maintaining the voltage stability at the WT terminal while following the transmission system operator (TSO) instructions due to the information collection as well as the possible data loss during transmission. Therefore, this study proposes a coordinated control method for WTs and multiple power sources based on model predictive control under wake disturbance conditions, aiming to reduce the average voltage deviation in WT terminals and go close to the rated voltage and ensure effective compliance with TSO commands in large-scale WFs. Accordingly, the Jensen wake model was utilized to accurately calculate the available active and reactive power limits for each WT. Energy storage systems and static Var generators were modeled to coordinate and maintain the voltage in all WT terminals within the feasible range, providing peak shaving and valley filling support to reduce wind energy waste and shortfall, thereby enhancing the economic and operational reliability of WF. Further, the effectiveness of the proposed method was validated in MATLAB/Simulink.

City-Level Sugar-Sweetened Beverage Taxes and Youth Body Mass Index Percentile

Levying excise taxes on sugar-sweetened beverage (SSB) distributors, which are subsequently passed on to consumers, is a policy implemented to reduce the high prevalence of cardiometabolic disease and generate public health funding. Taxes are associated with lower SSB purchases and consumption, but it is unknown whether they are associated with weight-related outcomes in youth. To determine the association of SSB excise taxes with youth body mass index (BMI) trajectories. This cohort study was conducted from 2009 to 2020, including 6 years before tax implementation and 4 to 6 years after tax implementation. The California cities of Albany, Berkeley, Oakland, and San Francisco, which implemented SSB excise taxes, were compared against 40 demographically matched control cities in California. Participants included Kaiser Permanente members aged 2 to 19 years at cohort entry (baseline) with continuous residence in selected cities with at least 1 pretax and 1 posttax BMI recorded in their electronic health record. Data analysis was performed from January 2021 to May 2023. Implementation of SSB excise taxes. Centers for Disease Control and Prevention age-specific and sex-specific BMI percentiles and percentage of youth with overweight or obesity before tax implementation through 4 to 6 years after implementation were compared with control cities. Statistical analysis was conducted using the difference-in-differences (DID) method. A sensitivity analysis used the synthetic control method. A total of 44 771 youth (mean [SD] age at baseline, 6.4 [4.2] years; 22 337 female [49.9%]) resided in the cities with SSB taxes; 345 428 youth (mean [SD] age, 6.9 [4.2] years; 171 0168 female [49.5%]) resided in control cities. There was a -1.64-percentage point (95% CI, -3.10 to -0.17 percentage points) overall difference in the mean change of BMI percentile between exposure and control cities after SSB tax implementation. There was no significant overall difference in the percentage of youth with overweight or obesity or youth with obesity compared with control cities. All DID estimates were significant for youth residing in exposure cities in terms of BMI percentile (age 2-5 years in 2017, -2.06 percentage points [95% CI, -4.04 to -0.09 percentage points]; age 6-11 years in 2017, -2.79 percentage points [95% CI, -4.29 to -1.30 percentage points]), percentages of youth with overweight or obesity (age 2-5 years, -5.46 percentage points [95% CI, -8.47 to -2.44 percentage points]; age 6-11 years, -4.23 percentage points [95% CI, -6.90 to -1.57 percentage points]), and percentages of youth with obesity (age 2-5 years; -1.87 percentage points [95% CI, -3.36 to -0.38 percentage points]; age 6-11 years, -1.85 percentage points [95% CI, -3.46 to -0.24 percentage points]). Compared with control cities, changes in mean BMI percentiles were significant for male (-1.98 percentage points; 95% CI, -3.48 to -0.48 percentage points), Asian (-1.63 percentage points; 95% CI, -3.10 to -0.16 percentage points), and White (-2.58 percentage points; 95% CI, -4.11 to -1.10 percentage points) youth. Compared with control cities, White youth in exposure cities had improvements in the percentage with overweight or obesity (-3.73 percentage points; 95% CI, -6.11 to -1.35 percentage points) and the percentage with obesity (-2.78 percentage points; 95% CI, -4.18 to -1.37 percentage points). In this cohort study, SSB excise taxes were associated with lower BMI percentile among youth. Policymakers should consider implementing SSB excise taxes to prevent or reduce youth overweight and obesity and, ultimately, chronic disease, particularly among children younger than 12 years.

Machine learning based feedforward voltage control for buck converters in envelope tracking applications

Tracking the voltage envelope of 5G base station power supplies poses a significant long-term challenge. To achieve fast and accurate envelope tracking, an efficient and high-frequency power electronics circuit and an excellent voltage controller are essential. This paper presents a novel machine learning (ML) based feedforward control method for precise voltage reference tracking. The method is exemplified using a standard buck converter in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM). A feedforward neural network (FNN) is trained to capture non-ideal and non-linear effects in real circuits. An automated data collection system, developed with MATLAB and Simulink, facilitates data acquisition and model training process. The trained FNN predicts the duty cycle for the buck converter to generate the targeted output voltage and output power. A simulation model is built to validate the effectiveness of the FNN controller in envelope tracking applications.

Design of intelligent cruise control system using fuzzy-PID control on autonomous electric vehicles prototypes

Electric vehicles provide a solution for using alternative fuels, namely, electricity. Electric vehicles are used for short distances and intercity travel over long distances, increasing the risk of accidents. Cruise Control is a technology embedded in vehicles to maintain stable speeds; this system will automatically adjust the vehicle's speed when motion changes cause changes in vehicle speed. This study aims to apply lidar sensors to detect distance in the Intelligent Cruise Control (ICC) system using the Fuzzy-PID control method. Testing results were obtained at safe distance inputs of 5, 6, and 7 meters with various object distances. All the tests were carried out; the response systems were obtained with an average settling time of 5 seconds and an average overshoot of 1.53%. Therefore, the proposed Fuzzy-PID method works well for controlling Intelligent Cruise Control systems in autonomous electric vehicle prototypes.

Uniaxial carbon nanotube fiber reinforced dielectric elastomer actuator with self-sensing

Compared to the isotropic dielectric elastomer (DE), uniaxial fiber reinforced DEs have larger deformation capabilities in specific directions, showing great application prospects. However, there are still significant challenges in achieving self-sensing without introducing additional sensing components. Inspired by the control method of biological muscles, in this article, we used carbon nanotube (CNT) fibers as reinforcing fibers that resistance changes are highly sensitive to tensile strain and exhibit a linear relationship within a small strain range. Based on the developed uniaxial reinforced strain behavior model of DEs, a shear lag model was adopted to introduce an interface transition layer between the DEs and CNT fiber to consider the strain difference caused by the difference in elastic modulus. The relationship between CNT fiber strain and DE strain was established, and the influence of CNT fiber mechanical properties on self-sensing performance was explored. The verification results under 0.05 Hz, 20 kV/mm signals show that the model has 4 % error, providing new ideas for the self-sensing function of uniaxial fiber reinforced DEs.

Research on gravity compensation control of BPNN upper limb rehabilitation robot based on particle swarm optimization

AbstractA four‐degree‐of‐freedom upper limb exoskeleton rehabilitation robot system with a gravity compensation device is constructed. The objective is to address the rehabilitation training needs of patients with upper limb motor dysfunction. A BP neural network adaptive control method based on particle swarm optimization is proposed. First, the degrees of freedom of the human body are analyzed, and a Lagrange method is employed to construct a dynamic model. Second, a particle swarm optimization back propagation neural network adaptive control algorithm based on particle swarm optimization is presented. Subsequently, the range of motion of the upper limbs is analyzed with reference to muscle anatomy and a three‐dimensional motion capture system. And the robot structure design is analyzed in detail. Finally, simulation experiments were conducted, and the results demonstrated that the proposed method exhibited high effectiveness and accuracy.

Leveraging Pumped Storage Power Plants for Innovative Stability Enhancement of Weakly Interconnected Power Systems

The hybrid AC/DC grid, based on a significant share of renewable energy sources, is gradually becoming an essential aspect of the modern energy system. The integration of intermittent renewable generators into contemporary energy systems is accompanied by the decommissioning of power plants containing synchronous generators. Consequently, this leads to a reduction in system inertia and an increase in the risk of stability disruption. The abrupt disconnection of the primary generator or power line can result in an unanticipated mismatch between power generation and consumption. This discrepancy can trigger substantial and swiftly evolving alterations in power distribution, angular speed, load flow, and the frequency of generators. The risks of an energy system collapse can be mitigated through automation, enabling rapid adjustments to generation and load capacities, as well as power flows, in the electrical network. This article justifies the utilisation of a power control method for high-voltage power line interconnections. The technology of hydro storage power plants and measurements of voltage phasors are employed. The potential for easing power flow restrictions and realising substantial economic benefits is supported by the results obtained using simplified dynamic model of the Baltic power system and Nord Pool electricity market model.