Traditional Feedback Control Research Articles

Optimal Determining Air Supply Humidity for Multi-Location Demands Under Different Objectives in an Indoor Moisture Environment: A Comprehensive Method and Case Study

Within high-precision indoor environments, such as semiconductor fabrication or textile plants, humidity control is paramount for preserving product integrity and reducing energy expenditure. The non-uniform indoor air environment poses a significant challenge in achieving humidity regulation that meets the distinct requirements of various locations. Traditional feedback control mechanisms may lead to instability, overshooting, and oscillation in indoor parameters. This paper proposes a comprehensive method to address humidity assurance issues in high-precision indoor environments by establishing analytical expressions that link the demand parameters at different locations with air supply parameters. Using a case study, this paper examines several typical operational scenarios with diverse control objectives, including minimizing dehumidification energy consumption, minimizing air supply humidity adjustment values, and constraints on adjustable air supply inlets. This method enables rapid calculation of air supply humidity and regulation of humidity parameters at multiple locations within the indoor environment. It considers various locations, requirements, optimization targets, and precision, demonstrating that it can quickly determine the optimal air supply parameters based on the objective function. This method facilitates rapid adjustment and high-precision assurance of different humidity requirements at multiple locations, making it suitable for high-precision design and control of indoor humidity environments.

Model predictive control for thermal comfort and energy optimization of an air handling unit system in airport terminals using occupant feedback

The dynamic fluctuations in occupant flow within airport terminals contribute to delays in the system's response under traditional feedback control. This imbalance between supply and demand can result in discomfort and unnecessary energy consumption. While Occupant-Based Model Predictive Control (OBMPC) has gained research interest as it leverages occupant forecasts to enhance control performance, there is limited knowledge regarding its application in the air-conditioning systems of airport terminals, mainly due to the intricate dynamics of occupant flow in terminal buildings. In this study, we proposed a model predictive control strategy based on dynamic occupant flow with the goal of fast responding to occupant flow and reducing energy consumption. We integrated mathematical formulas and the Anylogic simulation software to predict occupant variations in the baggage claim hall. The model predictive control strategy of the air conditioning system was proposed with the predicted occupant flow. The proposed strategy was evaluated throughout the whole cooling season in the validated simulation model of the air handling unit system of the baggage claim hall at an airport terminal. The results demonstrated that the proposed strategy could effectively respond to variations in occupancy and achieve 10% energy savings throughout the entire cooling season, compared to conventional feedback control. Our work presents a viable solution to address system regulation lag and reduce energy consumption without jeopardizing thermal comfort.

Mechanism for High-Precision Control of Movement at Maximum Output in the Vertical Jump Task.

Human movements are governed by a tradeoff between speed and accuracy. Previous studies that have investigated the tradeoff relationship in sports movements involving whole-body movements have been limited to examining the relationship from the perspective of competition-specific movements, and the findings on whether the relationship is valid have not been unified. Therefore, this study incorporated a vertical jump task with the introduction of a condition in which landing position control was added to evaluate the essence of a sports movement that requires both speed and accuracy. Accuracy was examined using a method that quantifies the coordinates of the landing and takeoff positions using entropy. The mechanism of that tradeoff was then examined by confirming the phenomenon and analyzing the 3D vector trajectories. An increase in accuracy and a decrease in speed were observed when the landing position was the control target, even in the vertical jumping task normally performed at maximum effort, and the 3D velocity vector was characterized by the following: a reduced scalar and a more vertical direction. While the entropy from the takeoff to the landing position seemed to decrease when the accuracy of the landing position improved, the following noteworthy results were obtained given the characteristics of the vertical jump. Unlike traditional feedback control in the entropy reduction in hand movements, the trajectory is predetermined in a feedforward-like manner by controlling the initial velocity vector at takeoff, which allows the landing point to be adjusted.

H ∞-based truncated predictive control for switched nonlinear cyber physical systems subjected to actuator faults and deception attacks

ABSTRACT This paper emphasises the study of switched nonlinear cyber-physical systems (CPSs) subjected to input delay, actuator faults and deception attacks by a H ∞ -based truncated predictive control design. Particularly, the predictive controller uses delayed state information in addition to the traditional feedback control to estimate the present state for the feedback. Furthermore, the proposed control design is modelled with a truncated predictive approach which is also used to compensate the effects of actuator faults and attacks that also satisfy a predefined H ∞ index simultaneously. With the assistance of the Lyapunov stability theory, a novel set of adequate criteria are acquired in terms of LMIs which assure the asymptotical stability of the switched nonlinear CPS under prescribed predefined H ∞ performance levels. Eventually, the proposed theoretical results are validated through two numerical examples.

Laser power stabilization system based on variable universe fuzzy proportional‐integral‐differential

AbstractA variable universe fuzzy proportional‐integral‐differential (PID) algorithm was designed and first application for a laser power stabilization system. To validate the algorithm's effectiveness, it is applied to the simulation model of Simulink and the experimental system based on an acousto‐optic modulator. The results demonstrate that the variable universe fuzzy PID presents higher control precision, better robustness, and can suppress long‐term drift phenomena compared with the traditional feedback control algorithm. The algorithm design method in this paper has significant reference and guidance value for laser power stabilization system.

Global Transformer Architecture for Indoor Room Temperature Forecasting

A thorough regulation of building energy systems translates in relevant energy savings and in a better comfort for the occupants. Algorithms to predict the thermal state of a building on a certain time horizon with a good confidence are essential for the implementation of effective control systems. This work presents a global Transformer architecture for indoor temperature forecasting in multi-room buildings, aiming at optimizing energy consumption and reducing greenhouse gas emissions associated with HVAC systems. Recent advancements in deep learning have enabled the development of more sophisticated forecasting models compared to traditional feedback control systems. The proposed global Transformer architecture can be trained on the entire dataset encompassing all rooms, eliminating the need for multiple room-specific models, significantly improving predictive performance, and simplifying deployment and maintenance. Notably, this study is the first to apply a Transformer architecture for indoor temperature forecasting in multi-room buildings. The proposed approach provides a novel solution to enhance the accuracy and efficiency of temperature forecasting, serving as a valuable tool to optimize energy consumption and decrease greenhouse gas emissions in the building sector.

Process intensification in bio-jet fuel production: Design and control of a catalytic reactive distillation column for oligomerization

Biojet-fuel represents a promising avenue for decreasing CO2 emissions within the aviation industry. Among different biojet-fuel production processes, the Alcohol-to-Jet (ATJ) process stands out as highly promising. Despite its potential, the ATJ process currently faces economic challenges due to low yields and high energy usage. This study introduces a strategy for enhancing the economic feasibility of the ATJ process through process intensification, specifically by incorporating a novel reactive distillation column in the oligomerization stage. This innovative process was evaluated and compared with its conventional counterpart. The total annual cost, eco-indicator 99, and individual risk were chosen as critical parameters for assessing the improvements in cost, environmental impact, and safety brought about by the reactive column. Additionally, control studies were conducted using both Proportional-Integral (PI) and Model Predictive Control (MPC) controllers to determine the feasibility of operating the reactive distillation column. The results suggest that the reactive distillation process offers 20 % cost savings, reduces environmental impact by 50 %, and lowers risk by 22 % when compared to the conventional process. In terms of control strategies, the study found that the catalytic column can be successfully operated using both traditional feedback control and more advanced techniques, such as model predictive control.

Global asymptotic stability and projective lag synchronization for uncertain inertial competitive neural networks

In this paper, the global asymptotic stability and projective lag synchronization of second‐order competitive neural networks with mixed time‐varying delays and uncertainties are studied without converting the original system into the usual first‐order system. Firstly, according to the Lyapunov functional method, inequality technique and the designed adaptive control strategy, the algebraic criteria of stability, and projective lag synchronization are derived by adjusting the control gain parameters in the controller. The obtained sufficient conditions are simple and easy to verify. Different from the traditional feedback controller, the adaptive controller can adjust its characteristics according to the system model, and external disturbance makes the system have better control performance. Besides, unlike the existing ones, this stability and synchronization problem is directly analyzed by constructing some new Lyapunov functionals with state variables and state variable derivatives. Finally, the effectiveness and practicability of the results are verified by numerical examples.

Thermal Energy Feedback Regulation of Human Life System Based on Photon Radiation

To solve the problem of the long execution time of the traditional thermalenergy feedback regulation mechanism of human life system, a method ofanalyzing the thermal energy feedback regulation mechanism of human lifesystem based on photonic radiation was proposed. The energy responseentropy of human living system is calculated by analyzing the change ofhuman living system’s thermal energy. The photonic counter is designedby using photonic radiation technology to extract the weak optical signal ofthermal energy of the system and output energy accumulation. The quadraticprogramming algorithm is used to solve the optimal solution of thermalenergy, and the neural network model is combined with the energy accu-mulation of photon counter to realize the thermal feedback regulation. So far,the design of energy feedback regulation mechanism of human living systemhas been completed. The experimental results show that compared withthe traditional feedback control mechanism, the designed energy feedbackcontrol mechanism based on photonic radiation has a shorter implementationtime and is suitable for practical engineering.

A novel method for fusing graph convolutional network and feature based on feedback connection mechanism for nondestructive testing

• A new feedback connection mechanism is proposed to determine the contribution of features. • The contribution of features to the final result does not need to be determined by the expert’s prior knowledge. • The proposed method can distinguish a priori features from other irrelevant knowledge. • The proposed method is superior to SOTA methods in determining the contribution of features. Data-driven intelligent methods to fusing feature with a priori information have made great progress in the recent times. And there are two limitations to fusion methods. Firstly, the structure of the fusion method cannot be precisely determined. Secondly, the contribution of the final result of the fusion method cannot be confirmed. In this paper, a novel feedback connection mechanism inspired by the traditional feedback control system is proposed to solve the above-mentioned limitations. The overall structure consists of three parts. The first part is a abstract feature extractor, which consists of a multilayer graph neural networks (GCNs). The second part uses memory augmentation of long short-term memory (LSTM) to achieve initial fusion of feature and re-extraction of abstract features. In order to combine the two abstract features more effectively, the third part of the mechanism of feedback recursive update of feature fusion weights is proposed. Our proposed method is first tested for damage detection of photovoltaic (PV) modules. Another experiment is the inverse problem of self-magnetic flux leakage (SMFL) detection with stress imbalance detection on a pipeline experimental platform. The result show that the proposed method can improve the detection efficiency and analyze the contribution of feature to the detection results.

A model predictive control regulation model for radiant air conditioning system based on delay time

Radiant air conditioning (RAC) systems are widely used because of the advantage of higher thermal comfort compared to convection systems. However, RAC systems exhibit considerable hysteresis and significant time delays in heat transmission from the terminal to the interior environment. Traditional feedback control methods do not adequately account for the system's delay impact while implementing dynamic control, resulting in a mismatch between supply and demand loads in time series, an inefficient impact of interior temperature control, and energy waste. According to this study, the time-delay characteristics of the RAC system are quantified by the startup-process delay time (SPDT) and operating-phase integrated delay time (IDT). The system startup time is set by the SPDT. Based on the IDT, the prediction interval of the model predictive control (MPC) and the step time of rolling optimization are determined. On this basis, a short-term forecasting model of air conditioning operating load and the regulation model of the system using MPC are established and verified by comparing with the conventional rule-based control of constant water temperature (CRBC) and proportionally-integral-differential (PID) control. The results for the RAC system with a large time delay indicate that the stability of indoor temperature under MPC controlling is 18.8% higher than the PID control and 63.5% higher than the CRBC method. Also, the indoor temperature controlled by the MPC is closest to the set value and shows the minimum fluctuation. Furthermore, the overall energy consumption of the system is estimated for reducing by 14.89% in comparison with the CRBC and by 8.51% in comparison with the PID.

Research on Fuzzy Control System of Intelligent Irrigation Based on Soil Moisture

Smart irrigation for sustainable agricultural water supply has been a critical initiative. In this paper, a fuzzy control system is designed to realize intelligent and precise irrigation by combining fuzzy control with traditional feedback control based on the features that soil moisture has significant influence and ease of detection and adjustment in practical irrigation. The opening time of solenoid valve is decided by the control system in real time, and the deviation and deviation rate of change for soil moisture are regarded as input variables. The model of intelligent irrigation fuzzy control system is built and compared with the model of PID control by Simulink for simulation. The results show that the fuzzy control system operates stably and achieves real-time precise irrigation with faster response and stronger robustness. The issue of reduced accuracy in irrigation due to the coupling between human and environmental factors is effectively solved by this design.

Disturbance rejections and synchronization of fractional-order fuzzy complex networks

This paper deals with the problem of disturbance rejection and synchronization of fractional-order complex dynamical networks subject to nonlinear coupling strength and discontinuous nonlinear functions. Notably, the nonlinear coupling strength is linearised by using a well-known Takagi-Sugeno fuzzy approach. The considered system is transformed into a nominal form by employing the uncertainty and disturbance estimator-based control approach, which simplifies the control objective and improves the system performance. Second, the uncertainty and disturbance estimator is incorporated into the traditional feedback control scheme to reject the unknown disturbance and uncertainty. Then, the required synchronization conditions for both the discontinuous and continuous fractional-order systems are obtained by using Lyapunov stability and fractional calculus theories. Last, numerical examples are provided to illustrate the efficiency of the proposed control strategy, wherein it is shown that the system yields better satisfactory tracking performance and high robustness against possible disturbance and uncertainties and finite set of jump discontinuous nonlinear functions. Moreover, the selection of appropriate filter design is discussed for various kinds of disturbance signals.

Bifurcation control of a delayed fractional-order prey-predator model with cannibalism and disease

In this paper, the bifurcation control for a fractional-order delayed prey-predator system with disease and cannibalism is investigated. Firstly, the existence, uniqueness and non-negativity of the solutions are studies, and the stability of equilibrium points are discussed. Next, taking time delay as the bifurcation parameter, the conditions of creation for Hopf bifurcation are confirmed. Then, two feedback controllers are introduced, which successfully control the Hopf bifurcation, and bifurcation control of the two controllers are simply compared theoretically. Finally, numerical simulations show that cannibalism has an significant influence in controlling the stability of the model, and when the parameters are appropriate, the disease can be removed. Meanwhile, the feedback controllers can control the bifurcation well. In general, the control effect of time-delay feedback controller should be better than that of traditional feedback controller. However, we found that the control effect of the traditional feedback controller is better than the time-delay feedback controller, the control effect completely depends on the selection of parameters.

A feedback control principle common to several biological and engineered systems.

Feedback control is used by many distributed systems to optimize behaviour. Traditional feedback control algorithms spend significant resources to constantly sense and stabilize a continuous control variable of interest, such as vehicle speed for implementing cruise control, or body temperature for maintaining homeostasis. By contrast, discrete-event feedback (e.g. a server acknowledging when data are successfully transmitted, or a brief antennal interaction when an ant returns to the nest after successful foraging) can reduce costs associated with monitoring a continuous variable; however, optimizing behaviour in this setting requires alternative strategies. Here, we studied parallels between discrete-event feedback control strategies in biological and engineered systems. We found that two common engineering rules—additive-increase, upon positive feedback, and multiplicative-decrease, upon negative feedback, and multiplicative-increase multiplicative-decrease—are used by diverse biological systems, including for regulating foraging by harvester ant colonies, for maintaining cell-size homeostasis, and for synaptic learning and adaptation in neural circuits. These rules support several goals of these systems, including optimizing efficiency (i.e. using all available resources); splitting resources fairly among cooperating agents, or conversely, acquiring resources quickly among competing agents; and minimizing the latency of responses, especially when conditions change. We hypothesize that theoretical frameworks from distributed computing may offer new ways to analyse adaptation behaviour of biology systems, and in return, biological strategies may inspire new algorithms for discrete-event feedback control in engineering.

Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

Controlling feedback control systems in continuous action spaces has always been a challenging problem. Nevertheless, reinforcement learning is mainly an area of artificial intelligence (AI) because it has been used in process control for more than a decade. However, the existing algorithms are unable to provide satisfactory results. Therefore, this research uses a reinforcement learning (RL) algorithm to manage the control system. We propose an adaptive speed control of the motor system based on depth deterministic strategy gradient (DDPG). The actor-critic scenario using DDPG is implemented to build the RL agent. In addition, a framework has been created for traditional feedback control systems to make RL implementation easier for control systems. The RL algorithms are robust and proficient in using trial and error to search for the best strategy. Our proposed algorithm is a deep deterministic policy gradient, in which a large amount of training data trains the agent. Once the system is trained, the agent can automatically adjust the control parameters. The algorithm has been developed using Python 3.6 and the simulation results are evaluated in the MATLAB/Simulink environment. The performance of the proposed RL algorithm is compared with a proportional integral derivative (PID) controller and a linear quadratic regulator (LQR) controller. The simulation results of the proposed scheme are promising for the feedback control problems.

Machine Learning Enables Radio Resource Allocation in the Downlink of Ultra-Low Latency Vehicular Networks

Autonomous driving and intelligent transportation demand ultra-low latency and high reliability communication in future vehicular networks. Proactive wireless communication can facilitate minimal latency by open-loop communication, which discards traditional feedback control mechanisms. However, appropriate radio resource allocation in such proactive mobile networks has not been fully studied due to lacking channel state information (CSI) and the alleviation of multiple access interference (MAI) in multiple virtual cells. This paper aims to ensure the reliability of downlink communication by a novel radio resource allocation scheme in proactive vehicular networks with ultra-low latency. We regard data transmission success rate as the reliability indicator and propose a joint radio resource allocation model based on the “generalized closed-loop”, where anchor node (AN) uses the radio resource utilization information (RRUI) from the vehicle in the immediate past uplink as a guide to assist resource allocation. Subsequently, we study the radio resource allocation model solution on the vehicle side and the network side respectively. On the vehicle side, vehicles use the local or global data transmission experience to select the radio resource with the best quality as the RRUI. On the network side, according to the latest RRUI of vehicle and resource occupancy information, deep reinforcement learning is proposed to make appropriate radio resource allocation decisions. Simulations demonstrate the effectiveness of the intelligent joint radio resource allocation scheme under the cooperation between vehicles and AN. When the resource load rate reaches 40%, the joint radio resource allocation scheme can achieve a data transmission success rate of more than 98%.

Failure characteristics of the active-passive damping in the functionally graded piezoelectric layers-magnetorheological elastomer sandwich structure

The active-passive damping of the functionally graded piezoelectric layers-magnetorheological elastomer (FGPE-MRE) sandwich structures may fail due to the compressibility of the core. This work provides a unified modeling method for the failure characteristics of the active-passive damping in the FGPE-MRE sandwich structure. The unbounded condition and normal magnetic field are considered to approximate the practical applications of the active-passive damping. To clarify the failure mechanism of the active-passive damping, a novel compressible core method is proposed. To illustrate the effect of the normal magnetic field, the strain-stress relation with in-plane Lorenz force is developed by employing the Maxwell's equation. Moreover, to realize the unbounded condition of the FGPE-MRE sandwich structure, the classical boundary condition of sandwich structures is modified. To avoid the failure of the active-passive damping, the traditional feedback control law is improved by constructing a fractional negative feedback equation. Finally, numerical investigations are conducted to show the failure characteristics of the active-passive damping and the effectiveness of the proposed control law.

Reinforcement learning strategy for spacecraft attitude hyperagile tracking control with uncertainties

Spacecraft attitude tracking control is a challenge for different space moving target observation tasks, especially when uncertainties exist. Traditional controllers have poor adaptability, and iterative learning controllers are time consuming. This paper focuses on the spacecraft attitude tracking control problem with uncertainties, including an unknown inertia matrix, desired attitude and desired angular velocity. To improve the attitude tracking accuracies under uncertainties, a reinforcement learning (RL)-based sliding mode observer is designed in which the uncertainties are estimated by the learning process using RL. To guarantee the convergence of the prescribed sliding mode surface, a controller based on the combination of traditional feedback control and RL is developed. The traditional feedback controller is used to speed up the learning process and to reject the aperiodic disturbance, while RL is applied to solve the uncertainty and to reject the periodic disturbance. The proposed controller can maintain high attitude tracking accuracies for different tracking missions in which the unknown inertia matrix, the desired attitude and attitude angular velocity are different without adjusting parameters. Finally, comparison results of the numerical simulation illustrate the effectiveness of the proposed method.

Quantized intermittent control tactics for exponential synchronization of quaternion-valued memristive delayed neural networks

This article studies the global exponential synchronization (GES) of quaternion-valued memristive delayed neural networks (QVMDNNs) by quantized intermittent control (QIC). Without decomposing the original systems into usual real-valued or complex-valued ones, the discussed system is directly processed in both cases of the differential inclusion theory. Based on two QIC strategies and applying Lyapunov functional method and inequality techniques, several new delay-dependent criteria in the form of real-valued algebraic inequalities are directly derived to assure the GES of the concerned system. Compared to the traditional feedback control, the QIC strategy can reduce the control expenses and shorten time to achieve the GES. Ultimately, two examples are given to validate the effectiveness and advantages of the proposed outcomes.