Dynamic Control Model Research Articles

Modeling dynamic control model of a two-link crane-manipulator

The problem of controlling a two-link crane-manipulator depends on the level of complexity of the system. When using a two-link manipulator, two problems need to be solved. The first problem is that you need to define the control parameters in such a way that the given position of the manipulator changes according to the corresponding trajectory. The second problem is that in order to achieve the desired position of the manipulator, it is necessary to correctly parameterized the mathematical model of the control system. One of the problems that affects the accuracy of manipulator control is the correct construction of adequate dynamic control models.
 In this article was propose to consider a direct method of developing a dynamic model of the crane-manipulator. It was propose to apply a mathematical model, which is built on the principles of theoretical mechanics by applying the Lagrange equations of the second kind.
 To modeling a dynamic model of a two-link crane-manipulator, the kinetic and potential energies of the component systems of the manipulator were determined, and on the basis of the Lagrange equation of the second kind, dynamic equations of motion were obtained.
 The dependences between the capturer coordinates and the generalized coordinates were also determined. These dependencies helped to establish the control equations that allow controlling the movement characteristics of the two-link manipulator.

Control-Oriented Low-Order Approximation and Reconstruction of Yaw-Excited Wind Turbine Wake Dynamics

The estimation and reconstruction of complex fluids like wind turbine wake are challenging problems, which require approximating high-dimensional, nonlinear dynamic systems with a limited number of physical measurements. Current works mainly focus on the reduced-order approximation for fluid dynamics like dynamic mode decomposition, or linear stochastic estimation for static mapping. In this article, the problem of yaw-controlled wind turbine wake dynamics approximation and reconstruction are addressed. A Koopman-linear flow estimator is designed, which forms a control-oriented linear dynamic state-space model. The full wake flow is first approximated on its dynamic performances and then reconstructed from low-order physical states. The novelty mainly yields on the added yaw excitation that a control-oriented dynamic model is obtained. The Kalman filter is also involved that measured data in future time could be embedded while the measurement noise is filtered. At last, static and dynamic estimation tests verify the effectiveness of the proposed estimator.

COVID-19 and Rumors: A Dynamic Nested Optimal Control Model

Unfortunately, the COVID-19 outbreak has been accompanied by the spread of rumors and depressing news. Herein, we develop a dynamic nested optimal control model of COVID-19 and its rumor outbreaks. The model aims to curb the epidemics by reducing the number of individuals infected with COVID-19 and reducing the number of rumor-spreaders while minimizing the cost associated with the control interventions. We use the modified approximation Karush–Kuhn–Tucker conditions with the Hamiltonian function to simplify the model before solving it using a genetic algorithm. The present model highlights three prevention measures that affect COVID-19 and its rumor outbreaks. One represents the interventions to curb the COVID-19 pandemic. The other two represent interventions to increase awareness, disseminate the correct information, and impose penalties on the spreaders of false rumors. The results emphasize the importance of interventions in curbing the spread of the COVID-19 pandemic and its associated rumor problems alike.

A pressure-node based dynamic model for simulation and control of aircraft air-conditioning systems

The aircraft air-conditioning system, which consumes the engine bleed air to provide stable and comfort environment for passengers, has a high demand of system reliability and energy efficiency. For analyzing system performance and control effect under a wide range of operating scenarios, this paper presents a dynamic model of the aircraft air-conditioning system. A pressure-node based method is proposed to decouple the system architecture, and a modelling framework is established with reflecting the interdependencies between component-level modules. Dynamic sub-models including pressure nodes, heat exchangers and the air cycle machine are built to predict all major dynamics in the system. The model is verified by the measured data of an airborne testing. The predicted temperatures show good agreement with the measured data, of which the average deviations at the compressor outlet and system outlet are 4.60 °C and 3.49 °C, respectively. The effectivity of the proposed model is investigated under various conditions, including different control signal inputs, standard pull up/pull down conditions and an entire flight case. The simulation results indicate that the model can be successfully applied for a wide range of aircraft operating scenarios, and provide insight towards control design and fault detection of the aircraft air-conditioning system.

In-Depth Analysis and Systematic Literature Review on Risk Based Access Control in Cloud

Security in Cloud is one of the most foremost and critical feature, which can ensure the confidence of the Scientific community on Cloud environment. With the dynamic and ever changing nature of the Cloud computing environment, static access control models become obsolete. Hence, dynamic access control models are required, which is still an emergent and underdeveloped domain in Cloud security. These models utilize not only access policies but also contextual and real-time information to determine the access decision. Out of these dynamic models the Risk-based Access control model, estimates the security risk value related to the access request dynamically to determine the access decision. The exclusive working pattern of this access control model makes it an excellent choice for dynamically changing environment that rules the cloud’s environment. This paper provides a systematic literature appraisal and evaluation of risk-based access control models to provide a detailed understanding of the topic. The contributions of selected articles have been summarized. The security risks in cloud environment have been reviewed, taking in the account of both Cloud Service Provider and Cloud Customer perspectives. Additionally, risk factors used to build the risk-based access control model were extracted and analyzed. Finally, the risk estimation techniques used to evaluate the risks of access control operations have also been identified.

A semi-active control method for magnetorheological dampers to suppress the milling vibration of thin-walled workpieces

Thin-walled workpieces are widely used in the aerospace field, and milling is the main processing method of such components. However, thin-walled workpieces are prone to vibrate during milling because of their weak stiffness, and the vibrations significantly affect the processing quality. Therefore, it is important to develop a novel milling vibration suppression method for thin-walled parts. The present work focused on the milling vibration control of a thin-walled workpiece using a semi-active magnetorheological (MR) damper. The forward dynamic model and inverse control model of the MR damper were first established, and its actuating position on the workpiece was then determined by modal analysis. The dynamic model of the MR damper at this position was built based on the proposed dynamic parameter acquisition method and used in the following semi-active control algorithm design. The effectiveness of this algorithm was verified by MATLAB/Simulink simulations. Finally, six groups of milling experiments were carried out using the non-damper, the rigid rod, the damper applied with 1-, 2-, and 3-A currents, and the semi-active control damper to validate the practical effect of the proposed algorithm. It was found that the designed semi-active control MR damper outperformed the constant-current control dampers or the rigid rod. In comparison to the non-damper experiment, the root mean square (RMS) values of milling forces in x- and y-directions decreased by 45.12% and 48.58%, respectively, and surface roughness decreased by 40.22%.

Control-oriented modeling and exploration of hygrothermal characteristics of air-cooled proton exchange membrane fuel cell

Achieving high output performance of air-cooled proton exchange membrane fuel cells relies on appropriate temperature and water content management. However, the open-cathode structure of the fuel cell stack leads to the coupling of thermal-water controls and makes the development of management strategies more challenging. This paper studies the effects of temperature and water content on the system performance theoretically and experimentally. The results show that there exists an optimum temperature at a specific current, at which we can optimize the stack output voltage. We analyzed the stack voltage changes and various voltage losses under three anode purge strategies. The results reveal that the losses of stack activation and mass transfer increase significantly when the purge valve closes for a long time, 20 min for the system under this study. With electrochemical impedance spectroscopy technology, we found that the voltage drops rapidly and irreversibly when the stack activation loss exceeds a certain limit, defined as the protective activation resistance. A control-oriented dynamic model based on water transport, electrochemistry, and heat balance theories was developed and verified by experiments. The relationship curves of the current-optimal temperature and the current-protective activation resistance were obtained from the experiment results, which can be used as the controller’s reference input and upper limit, respectively. Finally, we proposed an automatic purge strategy for air-cooled PEMFC based on electrochemical impedance spectroscopy.

Control-oriented dynamic modeling and thermodynamic analysis of solid oxide electrolysis system

Solid oxide electrolysis cell (SOEC) is a potential technology for increasing renewable energy penetration by turning excess power into hydrogen-based chemical energy, due to its high efficiency. In light of the intermittency of renewable energy, SOEC has to operate in a dynamic-changing environment. Therefore, dynamic modeling and thermodynamic analysis of solid oxide electrolysis systems are crucial for control design and efficient operation. In order to address various voltage losses, this paper develops a dynamic model of the solid oxide electrolysis system, in which the stack voltage is derived based on the electrochemical mechanism. The stack temperature is described by considering the dynamics of convective and radiative heat transfer between the layers. Dynamics of balance of plant (BOP) are modeled based on the mass and energy conservation laws. The model accuracy is verified by the polarization curve under different temperatures. The effects of temperature and current density on the electrolytic voltage are discussed. Three different operation modes (endothermic, thermoneutral and exothermic) are discussed in terms of different current density regions, where the balance between the electrochemical loss and reaction heat consumption varies. Steady-state energy and exergy flow diagrams are used to compare the energy consumption of different components in terms of different inlet temperatures, aiming to determine the optimal conditions with maximum system efficiency. The electrolyzer is revealed as the component with the highest energy consumption and exergy destruction. Dynamic simulation of the stack temperature is carried out in response to various control inputs and disturbances. Dynamic simulation exhibits the response rapidity of variables, strong couplings and different variation trends (monotone, overshoot or initial inverse response), due to the different time scales of electrochemical reaction, gas flow and heat transfer. This study lays a solid foundation for system optimization and dynamic control design.

Modeling Dynamic Responses to COVID-19 Epidemics: A Case Study in Thailand.

Quantifying the effects of control measures during the emergence and recurrence of SARS-CoV-2 poses a challenge to understanding the dynamic responses in terms of effectiveness and the population’s reaction. This study aims to estimate and compare the non-pharmaceutical interventions applied in the first and second outbreaks of COVID-19 in Thailand. We formulated a dynamic model of transmission and control. For each outbreak, the time interval was divided into subintervals characterized by epidemic events. We used daily case report data to estimate the transmission rates, the quarantine rate, and its efficiency by the maximum likelihood method. The duration-specific control reproduction numbers were calculated. The model predicts that the reproduction number dropped by about 91% after the nationwide lockdown in the first wave. In the second wave, after a high number of cases had been reported, the reproduction number decreased to about 80% in the next phase, but the spread continued. The estimated value was below the threshold in the last phase. For both waves, successful control was mainly induced by decreased transmission rate, while the explicit quarantine measure showed less effectiveness. The relatively weak control measure estimated by the model may have implications for economic impact and the adaptation of people.

Pressurizer system dynamic model for transient control in PWR

Abstract The pressurizer system of pressurized water reactor (PWR) maintains the reactor coolant system pressure during steady-state operation and limits pressure changes during transients. The in/out surge transients will cause pressure variations and they are controlled by either the spray system or the heater system. The spray system is actuated when the pressure exceeds a preset value. The heater system is initiated when the pressure falls below a preset value. The fundamental understanding and a reliable modeling of the pressurizer system behavior under steady state and transient conditions are needed to simulate overall nuclear power plant behavior. In the present study, an algorithm using Python 3.7 is developed to represent the dynamic behavior of the pressurizer system under steady-state and during in/out surge transients. Moreover, RELAP5 code is used to simulate the pressurizer system during the prescribed transients. The analysis and assessment results demonstrate satisfactory control performance during the in/out surge transients that guarantee the safety of PWR during operation. Also, the comparison between Python algorithm and RELAP5 model illustrates the capability and effectiveness of the Python algorithm for dynamic simulation and control.

Application of Nanoflexible Photoelectric Devices in Welding Tooling Equipment Systems

With the continuous breakthrough of science and technology, all aspects related to science and technology are constantly updated and changing. Photoelectric devices, also known as photosensitive devices, work on principles based on the photoelectric effect. The application of photoelectric devices in our lives is also diverse. This paper aims to study the flexibility of nanophotoelectric devices, which is a new generation of photoelectric device product technology, making photoelectric devices have more outstanding utility compared with the past. In the traditional sense, photoelectric devices will have their own hardware defects, and the update of nanoflexible photoelectric device technology will solve the above shortcomings. The above breakthroughs in key technologies have improved the hardware of welding tooling in the industrial field. The technique of welding tooling is widely used in the industrial field. The introduction of nanoflexible photoelectric devices into the welding tooling can overcome the defects of conventional processes. This paper proposes the application of nanotechnology in the industrial field. A parameter testing system and a model of dynamic matrix predictive control are established to predict and control the parameters of the experiment throughout the process. The experimental object of this paper is nanoflexible optoelectronic devices; meanwhile, the concentration of nanomaterials in the nanophotoelectric device plays a decisive role in the efficiency of the device, then the final experimental results show that when the concentration of nanomaterials is 8.5 ∗ 10, the efficiency of the optoelectric components is the best of the four groups of experiments.

Dynamic event-triggered security control for networked T-S fuzzy system with non-uniform sampling

This paper is focused on the security control problem for the dynamic event-triggered networked T-S fuzzy control system with non-uniform sampling and stochastic network attacks. Different from existing research, the signals from sensor are sampled in a non-uniform period. To improve communication, a novel dynamic event-triggered method is proposed, which can adjust the trigger parameters dynamically according to the changes of external output. Then, a T-S fuzzy dynamic output feedback control model with event-triggered mechanism and network attacks is constructed. Furthermore, by introducing a set of slack matrix, the asynchronous premise variable problem caused by event-triggered method is solved, and some sufficient conditions that insure the asymptotic stability with an H∞ performance requirement of the established closed-loop system can be obtained by utilizing Lyapunov function method and stochastic analysis technique. Moreover, the gains of the fuzzy dynamic output feedback controllers are found by solving the linear matrix inequalities (LMIs). Finally, two practical examples are given to support the merits and effectiveness of the proposed method.

Validation of a dynamic non-linear grinding circuit model for process control

A step-wise algebraic routine is used to fit a dynamic non-linear model, specifically developed for process control, to steady-state process data of an industrial single-stage grinding mill circuit. Step-test data from the industrial plant is used to validate the response of the non-linear model. The results indicate that the model provides a qualitatively accurate response of the main process variables. Because the non-linear model parameters can be calculated from steady-state data, it provides an advantage over classical system identification methods as it does not require an expensive and disruptive step-test campaign to develop linear transfer function models. The model is ideal for model-based predictive process control.

High-potential control for durability improvement of the vehicle fuel cell system based on oxygen partial pressure regulation under low-load conditions

In the state-of-the-art high-power self-humidifying proton exchange membrane fuel cell (PEMFC) systems for vehicles, the high potential and low water production at idle or low load conditions strongly cause corrosion and decay of key materials and thus reduce durability. Therefore, the control technology of system-level durability requires an innovative design. Cathode recirculation is beneficial in alleviating the above unfavorable factors from the perspective of regulating oxygen and vapor partial pressure. This paper presents a pioneering study on the dynamics and control of cathode recirculation in vehicle high-power self-humidifying PEMFC system under low load conditions. First, a control-oriented dynamic model of the vehicle PEMFC system with a cathode recirculation loop is developed and the steady-state and dynamic performance is verified with experimental data from a 120 kW system. Active control of the intake component is achieved by re-feeding the reacted cathode gas to the air compressor outlet through a recirculation pump. On this basis, a high-potential controller based on oxygen partial pressure regulation is designed in combination with the dynamics of cathode recirculation. Results show that the designed dynamic fuzzy logic segmented proportional integral derivative controller with feedforward compensation achieves the optimal high-potential control effect by managing the oxygen partial pressure under variable low load conditions. It not only has excellent anti-disturbance ability but also effectively reduces the dynamic response time, transient overshoot, and steady-state error to satisfy the rapid and stable voltage output. Finally, the concomitant effect of humidification brought by the implementation of the optimal high-potential controller is analyzed, and the results show that the proton membrane is completely humidified.

Dynamic permission access control model based on privacy protection

Access control technology is one of the key technologies to ensure safe resource sharing. Identity authentication and authority distribution are two key technologies for access control technology to restrict unauthorized users from accessing resources, and only authorised legal users can access resources. However, user privacy protection and frequent permission changes are two thorny issues that need to be solved urgently by access control technology. In this paper, a dynamic access control model based on privacy protection is proposed to deal with these problems. Compared with existing access control technologies, the main advantages of this paper are as follows: (1) Encrypt and hide the attributes of entities, and use attribute-based identity authentication technology for identity authentication, which not only achieves the purpose of traditional identity authentication, but also ensures the attributes and privacy of entities are not leaked; (2) Binding resource access permissions with entity attributes, dynamically assigning and adjusting resource access control permissions through changes in entity attributes, making resource access control more fine-grained and more flexible. Security proof and performance analysis show that the proposed protocol is secure under the hardness assumption of the discrete logarithm problem and the decision bilinear Diffie–Hellman problem. Compared with the cited references, this model has the advantages of low computational complexity, short computational time, and low communication overhead.

Cooperative Control of Multistation Passenger Inflows in Case of Irregular Large-Scale Passenger Flows

This study focuses on the large passenger flow control problem, after an operation interruption occurs, to develop a methodology that can efficiently control the passenger inflows of multiple stations and avoid overcrowding inside stations. An early-warning model for irregular large-scale passenger flows (ILSPF) and a dynamic ILSPF control model are proposed. The early-warning model is developed to predict passenger flows in the future with historical data and detect when to start control measures in actual time. The ILSPF cooperative control model focuses on cooperatively controlling the passenger inflows of multiple stations to ensure passenger safety in vehicles and stations, as well as maximize the number of passengers transported and minimize the passengers’ total waiting times. An improved particle swarm optimization algorithm was designed to determine an optimal solution, and a case study on the Chengdu metro in China was carried out to examine the performance of the model. The obtained results verify the effectiveness of the model and algorithm and prove that ILSPF control can regulate the passenger inflow demand, better match the passenger demand and capability on the line, increase the total number of passengers transported, and balance the proportion of passenger boarding at each station.

Economical heat recovery dynamic control and business model for supermarket refrigeration system coupled with district heating system

Large amounts of waste heat during the cooling process of supermarket refrigeration systems (SRS) would be released. A heat recovery strategy potentially contributes to reducing the supermarket’s heating costs related to buying heat from a district heating system (DHS). This paper explores the techno-economic feasibility of heat recovery for a real SRS integrated with a heat recovery unit (HRU) in terms of designing a dynamic heat recovery control (HRC) and business models. A cost-effective HRC is firstly developed for HRU to optimally manipulate the amount of heat recovered, thereby minimizing the real-time heat recovery cost. Furthermore, the business models of heat recovery under a long-term operation are proposed based on two transactional strategies between the SRS and DHS. A field test of the dynamic heat recovery for a remote SRS in Copenhagen Nordhavn area is conducted which demonstrates the proposed HRC algorithm can have a benefit of 0.49€ from a 3-h heat recovery operation. Moreover, the one-year operation of the SRS is also simulated which proves the developed two business models of heat recovery can achieve significant savings of 93% and 41% in energy costs.

Switch Semi-Active Control of the Floating Raft Vibration Isolation System

Switch semi-active control is introduced to improve the performance of the floating raft vibration isolation system on isolating vibration in the vicinity of the resonance frequency. A switch-controllable linear damper based on the electromagnetic damping principle is developed, whose characteristic parameters are obtained by experiment. The dynamic model of a semi-active control floating raft vibration isolation system is established, and two switch control algorithms are proposed. Algorithm 1 aims to minimize the kinetic energy of the raft, and Algorithm 2 balances minimizing the kinetic energy of the raft and minimizing the input energy of the system. The simulations of the original system, the system with the controllable damper, and the system with the damper controlled by the algorithms are carried out. The vibration acceleration responses of the foundation of the systems are used to evaluate the vibration isolation effect. The results show that the switching semi-active control using Algorithms 1 and 2 can significantly suppress the vibration of the system near the resonance frequency. Finally, the test platform of the switch’s semi-active controlled floating raft vibration isolation system is built, and the simulation results are verified by the experimental result.

Dynamic Production Control and Optimization under Low Carbon Economy considering Delivery Time Demand.

Production control plays an important role in the efficient use of production resources. To promote energy-saving and environmental protection, low carbon emissions, and sustainable development of enterprises, it is necessary to explore the production control scheme that facilitates the manufacturing of low-cost products. However, the existing studies have not incorporated expected delivery time into the preparation of production control scheme. With the purpose of improving the low carbon economy, this paper integrates the quantitative data of the dynamic production control process to reduce the overall cost and solves the problems of collaborative operation between dynamic production control and delivery time demand, aiming to promote the green and sustainable development of production control technologies of the manufacturing industry. To solve the problems more scientifically, the following works were done: firstly, a dynamic production control model was established under the constraint of low carbon emissions. Next, the corporate manufacturing information was synthetized with the data on production, logistics, and delivery, and a collaborative optimization model was established for the dynamic production control and delivery time demand under low carbon economy. The effectiveness of our model was demonstrated through experiments.

Sensitivity analysis and Bayesian calibration of a dynamic wind farm control model: FLORIDyn

FLORIDyn is a parametric control-oriented dynamic model suitable to predict the dynamic wake interactions between wind turbines in a wind farm. In order to improve the accuracy of FLORIDyn, this study proposes to calibrate the tuning parameters present in the model by employing a probabilistic setting using the UQ4WIND framework. The strategy relies on constructing a surrogate model (based on polynomial chaos expansion), which is then used to perform both global sensitivity analysis and Bayesian calibration. For our analysis, a nine wind turbine configuration in a yawed setting constitutes the test case. The results of sensitivity analysis offer valuable insight into the time-dependent influence of the model parameters onto the model output. The model parameter tied to the turbine efficiency appear to be the most sensitive parameter affecting the model output. The calibrated FLORIDyn model using the Bayesian approach yield predictions much closer to the measurement data, which is equipped with an uncertainty estimate.