System Model Information Research Articles

Seismic Damage Prediction and Classification tool: Utilizing Adaptive Neuro-Fuzzy Inference System (ANFIS) and Geographic Information System (GIS)

Seismic damage prediction of wide range of buildings is an important prioritization and classification tool for implementation of seismic risk and vulnerability assessment. This study has developed a rapid visual screening (RVS) tool using Adaptive Neuro-Fuzzy Inference System (ANFIS) model and Geographic Information System (GIS). Hospital an d public-school buildings located in districts 5 and 6 have been selected for assessment. Technical calculation basis in the development of FEMA P- 154 was followed in formulating the model using the neuro-fuzzy tool package in MATLAB. Six (6) building para meters and their resulting output (final score) were inputted for training. Sub-clustering FIS model showed the lowest RMSE and R2 result out of the five (5) FIS models compared. The model showed effective performance in classifying the buildings’ damage grades having 0.2908, 0.8073 and 0.948 for RMSE, and for testing and overall coefficient of determination (R2) values, respectively. Performance me trics from the developed confusion matrix showed high range of values for sensitivity, specificity, precision, and accuracy, with maximum error rate and false positive rate of 0.11 and 0.25, respectively, in classifying the buildings’ damage grade. These validate the model’s performance and ability in classifying the damage grade of the buildings observed. The results were used to develop a digital mapping of the damage grade distribution of the selected buildings in the area using ArcGIS.

Finite-horizon optimal secure tracking control under denial-of-service attacks

The finite-horizon optimal secure tracking control (FHOSTC) problem for cyber–physical systems under actuator denial-of-service (DoS) attacks is addressed in this paper. A model-free method based on the Q-function is designed to achieve FHOSTC without the system model information. First, an augmented time-varying Riccati equation (TVRE) is derived by integrating the system with the reference system into a unified augmented system. Then, a lower bound on malicious DoS attacks probability that guarantees the solutions of the TVRE is provided. Third, a Q-function that changes over time (time-varying Q-function, TVQF) is devised. A TVQF-based method is then proposed to solve the TVRE without the need for the knowledge of the augmented system dynamics. The developed method works backward-in-time and uses the least-squares method. To validate the performance and features of the developed method, simulation studies are conducted in the end.

Model‐free adaptive control for ultracapacitor based three‐phase interleaved bidirectional DC–DC converter

AbstractThe lack of an accurate mathematical model of non‐linear systems, such as bidirectional DC–DC converters, brings certain limits to design a formidable controller for such systems. This article proposes a model‐free adaptive control (MFAC) approach for an ultracapacitor based three‐phase interleaved bidirectional DC–DC converter (BDC) to ameliorate the controller design limitations due to the demand for system model information. This article designs a pseudo gradient (PG) estimation algorithm based on the input and output (I/O) data of a three‐phase interleaved BDC and designs a control input estimation algorithm through the PG estimation value, then, the design of a model‐free adaptive controller is completed. Moreover, the Black‐box technique is applied to the system, which does not require a precise mathematical model resulting in a lower computational burden, faster dynamics, and easier implementation. Furthermore, the feasibility and effectiveness of the proposed MFAC scheme are experimentally tested and verified on an energy storage system microgrid platform.

Finite‐horizon H∞ tracking control for discrete‐time linear systems

AbstractIn this paper, model‐free finite‐horizon tracking control for discrete‐time linear systems is studied. By formulating an augmented system consisting of the considered linear system and the command generator system, an augmented time‐varying Riccati equation whose solutions can achieve the finite‐horizon tracking control is derived. Then, a time‐varying Q‐function which contains the control input and disturbance input is designed. A time‐varying Q‐function‐based method is developed to learn the solutions of the finite‐horizon tracking control problem without knowing the system model information nor any model identification methods. It is proved that the solutions of the time‐varying Q‐function‐based method converge to the optimal solutions of the augmented time‐varying Riccati equation. At last, simulation examples are provided to show the feasibility and advantages of the time‐varying Q‐function‐based method compared to the infinite‐horizon Q‐learning‐based tracking control method.

A Novel Integral Reinforcement Learning-Based H∞ Control Strategy for Proton Exchange Membrane Fuel Cell in DC Microgrids

In this paper, a novel integral reinforcement learning (IRL) based H∞ control algorithm is proposed for proton exchange membrane fuel cells (PEMFCs) to enhance its performance in DC microgrids. The proposed control algorithm produces superior dynamic and steady-state responses without requiring the system model information. The stability of the proposed control algorithm is rigorously proven. Furthermore, its superiority over the PI control, model predictive control (MPC), fuzzy PI control and adaptive MPC is analyzed. Hardware-in-the-loop experimental results are given to verify the effectiveness of the proposed control algorithm.

Output-Feedback Multi-Loop Positioning Technique via Dual Motor Synchronization Approach for Elevator System Applications

This paper devises an output-feedback multi-loop positioning technique adopting the speed observer and multi-motor synchronizer, targeting the dual (master and slave) motor elevator system applications, providing the three contributions. First, the order-reduction observer continuously extracts the speed information from the motor position measurement, independent of the system model information. Second, the order-reduction stabilizer accomplishes the speed synchronization tasks for both the master and slave motors. Third, the resultant feedback system guarantees to exponentially recover the desired first-order transfer function from the reference to the actual motor position despite the model-plant mismatches. The prototype elevator system adopting the dual motor experimentally validates the practical advantages of the proposed technique.

Repurposing Multiple-Molecule Drugs for COVID-19-Associated Acute Respiratory Distress Syndrome and Non-Viral Acute Respiratory Distress Syndrome via a Systems Biology Approach and a DNN-DTI Model Based on Five Drug Design Specifications.

The coronavirus disease 2019 (COVID-19) epidemic is currently raging around the world at a rapid speed. Among COVID-19 patients, SARS-CoV-2-associated acute respiratory distress syndrome (ARDS) is the main contribution to the high ratio of morbidity and mortality. However, clinical manifestations between SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS are quite common, and their therapeutic treatments are limited because the intricated pathophysiology having been not fully understood. In this study, to investigate the pathogenic mechanism of SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS, first, we constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via database mining. With the help of host-pathogen RNA sequencing (RNA-Seq) data, real HPI-GWGEN of COVID-19-associated ARDS and non-viral ARDS were obtained by system modeling, system identification, and Akaike information criterion (AIC) model order selection method to delete the false positives in candidate HPI-GWGEN. For the convenience of mitigation, the principal network projection (PNP) approach is utilized to extract core HPI-GWGEN, and then the corresponding core signaling pathways of COVID-19-associated ARDS and non-viral ARDS are annotated via their core HPI-GWGEN by KEGG pathways. In order to design multiple-molecule drugs of COVID-19-associated ARDS and non-viral ARDS, we identified essential biomarkers as drug targets of pathogenesis by comparing the core signal pathways between COVID-19-associated ARDS and non-viral ARDS. The deep neural network of the drug–target interaction (DNN-DTI) model could be trained by drug–target interaction databases in advance to predict candidate drugs for the identified biomarkers. We further narrowed down these predicted drug candidates to repurpose potential multiple-molecule drugs by the filters of drug design specifications, including regulation ability, sensitivity, excretion, toxicity, and drug-likeness. Taken together, we not only enlighten the etiologic mechanisms under COVID-19-associated ARDS and non-viral ARDS but also provide novel therapeutic options for COVID-19-associated ARDS and non-viral ARDS.

Spatio-temporal distribution of sensitive regions of potential vegetation in China based on the Comprehensive Sequential Classification System (CSCS) and a climate-change model

The potential vegetation can reflect climatic influence on vegetation type change, and provide a scientific reference and guide for restoration and reconstruction of vegetation ecosystems. Meteorological observation data from 1961 to 2017 and predicative data under the Representative Concentration Pathway (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) during 2030s, 2050s and 2080s, in conjunction with comprehensive sequential classification system (CSCS) model and Geographic Information System (GIS) technology, were used to analyse the spatio-temporal distribution and variation of Sensitive Regions of Potential Vegetation (SRPV) in China. Results suggested that SRPV presented a pattern of total dispersion and partial agglomeration under all scenarios. The agglomerate regions spread from north-eastern China (e.g. Inner Mongolia Plateau, Greater Xing’an Mountains, and North-east Plain) to south-western China (e.g. Loess Plateau, Qinling–Huaihe belt, Nanling Mountains, Jiangnan hills, Qinghai–Tibet Plateau, and Tarim Basin). From the 2030s to the 2080s, SRPV exhibited characteristics of expansion and migration to the north under RCP scenarios. The distributed area of SRPV increased with increased radiation emission intensity. These results both further expand the research on potential vegetation using the CSCS, and also provide reference for governments to ensure vegetation ecological protection.

Cooperative and robust object handling by multiple manipulators based on the differential equation approximator

The problem of transporting a rigid object by a team of robotic arms is studied in this article. The study aims to enable manipulators to move the object in the desired and predefined direction efficiently. It is also desired to keep the force applied to the object limited to avoid distortion of its shape. Since practical systems usually confront limitations such as lack of complete system model information and disturbances, the presented approach should confront the effects of such phenomena on the performance. To fulfill the purpose, based on the universal approximation property, a powerful approximator for estimating uncertainties and disturbances based on differential equations is presented. Then a robust approximator-controller structure is designed to solve the multi-objective issue of position, orientation, and force control in the cooperating robotic system. To confirm a stable object grasp, and transportation, Lyapunov analysis is utilized and based on a thorough mathematical analysis, it is shown that the presented approximator-controller structure assures system stability. Finally, the proposed scheme is applied to two three-degree-of-freedom (3-DOF) robot arms that cooperatively handle a common and rigid object. Various tests are performed assuming the unavailability of system model information and in the existence of perturbations, and the results are analyzed

A model-free sparse wide-area damping controller for inter-area oscillations

As the successor of local control, wide-area damping control (WADC) is considered to be an effective tool for damping inter-area oscillations. The state-of-the-art WADC approaches are either model-based or centralized, so they are subject to model inaccuracy, bad data, high communication costs, and other nontrivial design challenges. On the other hand, current data-driven WADC practices are affected by the selection of input/output measurements, need to be pre-tuned and trained offline, or lack clear physical interpretation of the power grid small-signal dynamics. In this paper, we propose a novel online purely data-driven WADC method using phasor measurement unit (PMU) data, which requires no power system model information and can damp multiple inter-area modes simultaneously using minimal control effort. To directly shape the closed-loop characteristics of poorly-damped inter-area modes and account for physical constraints on communication network, we develop a modal linear quadratic regulator (MLQR)-based sparsity-promoting optimal state feedback controller. The developed WADC strategy does not require offline training, is adaptive to the applied PMU dataset and can be mapped to the actual grid physics. Dynamic simulations on the widely studied IEEE 68-bus benchmark system verify the effectiveness of the proposed approach in spite of measurement noise, PMU losses, and communication network restrictions. Moreover, it is shown that the proposed controller can adapt to different operating conditions due to its data-driven nature. Finally, our data-driven sparse WADC has comparable performance to conventional model-based and centralized data-driven WADC, while overcoming their limitations.

Comparison of sectoral low-carbon transition pathways in China under the nationally determined contribution and 2 °C targets

National climate targets must be decomposed into key areas to guide mitigation actions. This paper presents a comparative study of China's low-carbon transition pathways at the sectoral level under the nationally determined contribution (NDC) and 2 °C targets, using the energy system model and detailed sectoral information. The results show that each sector plays different roles in terms of emission trends, mitigation potentials, technology roadmaps, investment requirements, and mitigation costs. The power sector is expected to contribute around 50% of the total mitigation. The industry sector has better cost-effective performance, with high mitigation potential and low investment requirement. By contrast, the transport and power sectors account for around 90% of total investment demand. The building and transport sectors have substantial mitigation opportunities that can be realized through technologies with negative mitigation costs. Conversely, the industry sector faces challenges in promoting carbon capture and storage, which has the highest mitigation cost. Compared with the sectoral transition pathways under the NDC target, the 2 °C scenario requires a rapid near-term decarbonization of the power sector and additional emission reductions in end-use sectors. This decarbonization is possible through comprehensive deployment of advanced low-carbon technologies as well as measures that increase investments in low-carbon infrastructure and decrease investments in fossil fuel-based technologies in the power and transport sectors. Therefore, it is important to thoroughly understand the sectoral transition pathways under different climate targets in order to coordinate inter-sectoral actions and resources in a cost-effective manner.

Dual multivariable model-free adaptive individual pitch control for load reduction in wind turbines with actuator faults

Thanks to the great advances in the installations of large wind turbines, individual pitch control (IPC) has gained mounting interest among researchers. The actuator faults, however, have become a hindrance with the individual pitch process introducing more actuator actions and the pitch system operating in complex conditions. The traditional fault-tolerant control strategies of the IPC, which are mainly based on the system modeling information, and require the process of fault diagnosis, isolation or parameter estimation. The subsequent model errors and unmodeled dynamics have somehow limited the application of the strategies. Therefore, this paper proposes a passive fault-tolerant individual pitch control scheme independent of the above process, and based on the scheme, a dual multivariable model-free adaptive control strategy with differential characteristic is constructed. The benchmark 5 MW wind turbine model provided by FAST is employed to evaluate the performance. The results show that, compared with the traditional individual pitch control strategy, the proposed scheme can achieve almost identical load control and even better power control under fault-free conditions. Once the fault occurs, the proposed scheme can quickly compensate for the faulty actuator dynamics, and finally achieve almost the same performance in power and load control as under the nominal conditions.

An Active Disturbance Rejection Control Design Method Based on Disturbance Separation

In order to further improve the stability accuracy and anti-disturbance ability of the photoelectric stabilization platform, this paper proposes disturbance separation active disturbance rejection (DSADRC) method. Disturbance separation active disturbance rejection control makes full use of the available system model information and uses disturbance separation to separate the known disturbances from the total disturbance to be estimated, and improve the estimation accuracy of expanded state observer (ESO) by reducing the load. At the same time, it gets rid of the constraints of the standard integral series type in the traditional active disturbance rejection control, and takes the actual theoretical model of the system as the compensated model of the system, thus realizing the reuse of the system controller and reducing the workload of control algorithm design. And through simulation experiments, the DSADRC method is compared with the commonly used expected frequency characteristic method (EF), linear active disturbance rejection control (LADRC), model-assisted linear active disturbance rejection (MLADRC), and the experimental results show that: The DSADRC has better dynamic response performance and disturbance suppression ability.

Off-Policy 학습 기반 LQR을 이용한 파라미터 추정 및 전력 시스템 동기 탈조 판별을 위한 외란 관측기 설계

In this paper, a reinforcement learning-based Linear Quadratic Regulator(LQR) design method has been adopted to identify unknown system parameters. The off-policy learning-based LQR can obtain the optimal control gain through an iteration technique known as policy iteration, without using the system model parameters. Augmented states, using the system output integration, can help to alleviate the rank condition on the proposed parameter estimation method. Increasing the system model information accuracy allows the disturbance observers to detect various system faults with the least amount of estimation error. The line fault detection ability of a power system for out-of-step prediction has been studied by applying the proposed parameter estimation scheme to a single machine infinite bus system. Simulation results show that both the system parameters and the external disturbance can be successfully estimated through the proposed method.

A Data-Driven Model Predictive Control for Alleviating Thermal Overloads in the Presence of Possible False Data

Effective mitigation of thermal overloads is crucial in preventing power grid outages. With large-scale adoption of phasor measurement units, new and more effective mitigation opportunities have emerged. However, the possibility of false data attacks on the measured data, or the relay status information, threatens the promise of utilizing measurements for timely mitigation of thermal overloads. False data can lead to wrong estimation of the system states and the power flow model, which may eventually lead to wrong mitigations. To address these newly introduced challenges, in this article, a data-driven model-predictive control method is introduced for mitigation of the thermal overloads, which is resilient to false data injection attacks. This is achieved by constructing a data-driven power flow model from the trustworthy system measurements, which is independent from system topology and model parameters. Hence, the power flow model becomes immune to wrong system model information. To eliminate the adverse impacts of false data on the measurements, the actual system states are recovered from the historical trustworthy data if an attack is detected. Case studies demonstrate that the developed model predictive control methodology effectively identifies the optimal mitigations in a finite prediction horizon.

Data-Driven Congestion Control of Micro Smart Sensor Networks for Transparent Substations

Micro smart sensors and sensor networks are the key bases for building a fully visible, perceptible and controllable transparent substation in power grid. However, the massive deployment of micro smart sensors often leads to network congestion and directly affects the quality of information transmission. In practice, control design for micro sensor networks is often disturbed by factors such as nonlinearity, time delay and time-varying parameters, and the mathematical model is inaccurate. To solve these problems, this paper proposes a Koopman operator based data-driven congestion control scheme without using any system model information, based on model predictive control (MPC) and extended state observer (ESO). Firstly, according to the Koopman operator theory, a data-driven linear Koopman model for micro smart sensor networks is derived, only using the input/output data. Then an ESO based MPC scheme is designed to obtain the optimal packet dropping probability in the micro smart sensor node buffer area. Specifically, ESO performs real-time online estimation of the modeling errors, such as time-varying parameters, time delay and the external disturbances. Then the estimated modeling errors are viewed as total disturbances and are compensated in MPC, so that the queue length in the node buffer can quickly stabilize at the set value. Extensive simulations are conducted to verify the accuracy of Koopman model, and the effectiveness of the proposed control system design.

Model-free demand response scheduling strategy for virtual power plant considering risk attitude of consumer

Driven by the modern advanced information and communication technologies, distributed energy resources has the great potential for energy supply within the framework of virtual power plant(VPP). Meanwhile, demand response(DR) is becoming increasingly important for enhancing the VPP operation and mitigating the risks associated with the fluctuation of renewable energy resources(RESs). In this paper, we proposed an incentive-based DR program for VPP to minimize the deviation penalty from participating in power market. Markov decision process(MDP) with unknown transition probability is constructed from the VPP's prospective to formulate the incentive-based DR program, in which the randomness of consumer behavior and RES generation are taken into consideration. Furthermore, a value function of prospect theory(PT) is developed to characterize consumer's risk attitude and describe the psychological factors. A model-free deep reinforcement learning(DRL)-based approach is proposed to deal with the randomness existing in the model and adaptively determine the optimal DR pricing strategy for VPP, without requiring any system model information. Finally, the results of cases demonstrate the effectiveness of the proposed approach.

An Improved Model-Free Predictive Current Control With Advanced Current Gradient Updating Mechanism

Model-based predictive control that highly depends on system parameters is widely investigated. In contrast, model-free predictive current control (MFPCC) can be performed based on input or output measured data, rather than on any system model information, hence eliminating the influence of parameter uncertainties. In such a strategy, the current gradients due to each of the possible voltage vectors are stored and used to predict future currents. Current gradient knowledge therefore provides a significant foundation for MFPCC. In conventional MFPCC, however, the stagnation existing in the current gradient update always impacts the reliability of current gradients and further worsens the control performance. In this article, an improved MFPCC with an advanced current gradient updating mechanism is proposed. In the proposed strategy, to guarantee the reliability of the current gradients, two contiguous measured current gradients due to the applied voltage vectors are used to estimate the current gradients for all the possible voltage vectors. This simple method takes advantage of the mathematical relationships between the voltage vectors. In this way, all the current gradients can be obtained within one control period, effectively reducing the stagnation effect in conventional MFPCC. The proposed MFPCC scheme is evaluated on a permanent magnet synchronous machine drive setup to demonstrate its effectiveness.

Nonlinear Multiple Models Adaptive Secondary Voltage Control of Microgrids

This article proposes a model-free secondary voltage control (SVC) for microgrids (MG) using nonlinear multiple models adaptive control. Firstly, a linear robust adaptive controller is designed to guarantee the voltage stability in the bounded-input-bounded-output (BIBO) manner so as to meet the operation requirements of MGs. Secondly, a nonlinear adaptive controller is developed to improve the voltage tracking performance with the help of artificial neural networks (ANNs). A switching mechanism for coordinating such two controllers is designed to guarantee the closed-loop stability while achieving accurate voltage tracking. By an online identification based on the input and output data of MGs, the proposed method does not resort to any apriori information of system model and primary control, thus exhibiting good robustness, ease of deployment and disturbance rejection.

Self‐triggered stabilizing controllers for linear continuous‐time systems

SummarySelf‐triggered control is an improvement on event‐triggered control methods. Unlike the latter, self‐triggered control does not require monitoring the behavior of the system constantly. Instead, self‐triggered algorithms predict the events at which the control law has to be updated before they happen, relying on system model and past information. In this work, we present a self‐triggered version of an event‐triggered control method in which events are generated when a pseudo‐Lyapunov function (PLF) associated with the system increases up to a certain limit. This approach has been shown to considerably decrease the communications between the controller and the plant, while maintaining system stability. To predict the intersections between the PLF and the upper limit, we use a simple and fast root‐finding algorithm. The algorithm mixes the global convergence properties of the bisection and the fast convergence properties of the Newton‐Raphson method. Moreover, to ensure the convergence of the method, the initial iterate of the algorithm is found through a minimization algorithm.