Multi-step Control Research Articles

Real time aggregation control of P2H loads in a virtual power plant based on a multi-period stackelberg game

Power to Heat (P2H) technology is seen as an effective solution for integrating stochastic wind power. Particularly in real-time electricity markets, high-precision wind power forecasts enable the load to better accommodate its fluctuations. Nonetheless, the shorter time scales of real-time trading pose challenges for efficiently aggregating a large, heterogeneous, and dispersed loads and fully harnessing the thermal storage capabilities of loads to follow wind power fluctuations across multiple periods. Against this backdrop, this paper proposes an aggregation control approach for P2H loads with considering multi-period Stackelberg game strategy within the framework of a load-oriented virtual power plant (VPP). For multi-period real-time trading, a Stackelberg game model based on a dynamic price ceiling is established with considering dynamic supply-demand balance between wind power and P2H loads; To enhance load aggregation effectiveness, a dynamically updated temperature-power aggregation model is developed by employing model order reduction, variables continuousization, and linear superposition on second-order equivalent thermodynamic model; To improve load decomposition efficiency, a hierarchical and multi-step control strategy encompassing the continuousization of discrete variables is utilized to simplify the problem. Finally, a simulation example is presented using actual data from a power grid in northern China to validate the effectiveness of the proposed method.

A Car-Following Model Considering Missing Data Based on TransGAN Networks

Car-following behavior is closely related to the longitudinal control of the vehicle, affecting the safety of the vehicle and traffic flow stability. In order to interact with the preceding vehicle, the target vehicle usually collects the driving data of the preceding vehicle. However, data acquisition devices often face malfunctions caused by various unpredictable disruptions, resulting in missing value problems. This may cause the target vehicle to make wrong control decisions. Given this situation, a new car-following(CF) model considering missing data based on Transformer-Generative Adversarial Networks (TransGAN) is proposed. Firstly, Transformer Network with multi-head attention is used to deeply extract the potential features from incomplete vehicle state data, which can filter important information from the input and focus on these, while capturing long distance dependencies. Secondly, a Generative Adversarial Network is constructed. The Generator generates the future multi-step control states of the target vehicle based on the features extracted by Transformer Network. The Discriminator with a fully connected network is applied to simultaneously ensure the generation accuracy. Finally, our proposed model was trained and tested on a publicly available NGSIM I-80 dataset. Compared with other existing advanced works, our model can fit the actual control states of the target vehicle with higher accuracy under different data missing rates of the preceding vehicle, which demonstrates that the proposed method effectively improves the robustness of vehicle longitudinal car-following control under missing data.

Appointed-Time Dynamic Average Consensus: A Holistic Planning and Multi-Step Control Framework

Most existing solutions to the dynamic average consensus (DAC) problem assume access to the full dynamic of reference signals or their derivatives. Such constraints are more stringent and impose restrictions on use. Hence, we revisit the DAC problem in this paper and present its solution by developing a holistic planning and multi-step control (HPMSC) framework. Here, the HPMSC allows the agents to achieve DAC in an appointed settling time without involving any other parameters, which is called appointed-time dynamic average consensus (AT-DAC). Under the HPMSC framework, a sampling-data-based AT-DAC controller is designed, which guarantees that agents can track the average value of dynamic reference signals at a pre-appointed time. Compared with the existing results on DAC, a nice highlight of this paper is the implementation of zero-error DAC with weaker conditions imposed on the reference signals, without the need to know the upper bounds on their derivatives and without additional common function conditions. Finally, the performance validation of theoretical results is demonstrated by a numerical simulation.

Delay Compensation Control Strategy for Electric Vehicle Participating in Frequency Regulation Based on MPC Algorithm

With the continuous increase of renewable energy, the power system will face the problem of insufficient frequency response capability. Electric vehicles (EVs) combine the characteristics of mobile energy storage and controllable loads and can provide capacity support in frequency control. However, as a distributed frequency regulation resource, there is inevitably a communication delay in the process of electric vehicles participating in frequency regulation, which will affect the stability of the system frequency. Therefore, this paper proposes a frequency regulation delay compensation control strategy based on model predictive control (MPC). Firstly, the model predictive control is utilized to design an electric vehicle frequency response model controller and establish a frequency regulation model of the power system containing electric vehicles; then, by establishing the selection rules of the control signal, the multi-step control signal is dynamically adjusted during the rolling optimization of the predictive model to form a delay compensation mechanism considering delay and packet loss; finally, multi-scenario simulation is conducted, and the results show that the proposed strategy can roll-optimize the auxiliary frequency regulation function of electric vehicles, significantly reduce the influence of communication time delay, and improve the system frequency stability.

Stochastic model predictive control based on multi‐step control strategy for discrete nonlinear systems

Stochastic model predictive control (SMPC) is a popular approach to control uncertain systems by incorporating the probabilistic distribution of the uncertainty into the controller design. However, the performance of the SMPC deteriorates rapidly when the system becomes nonlinear. In this article, an efficient SMPC is derived after decomposing the discrete nonlinear systems into a linear stochastic model and a bounded model mismatch. The proposed controller includes a linearized-model-based SMPC and an RMPC for the model mismatch. The multi-step control strategy is incorporated to reduce the conservativeness by introducing more degrees of freedom. The feasibility of the recursiveness of the proposed controller is thoroughly discussed. The control performance is illustrated by examples involving both a numerical nonlinear stochastic system and a wind-blade pitch system.

New Technology and Experimental Research on Thick-Walled Tube Fatigue Impact Loading Precision Separation

Traditional separation methods for thick-walled metal tubes include turning and sawing, which suffer from wasted raw material and low efficiency. In view of this, this paper proposes a new process of using impact load to promote crack generation and tube separation. Based on the principles of radial repeated impact load, stress concentration effect and fatigue fracture, the rapid initiation and stable expansion of tube fatigue crack are promoted. In addition, the crack initiation mechanism of the tube V-notch root cracks under radial repeated load when the tube is in a restrained state. For the experimental study of the GCr15 steel tube, a multistep decline frequency time tube separation control curve with an initial frequency from 4 Hz to 31 Hz and termination frequency from 1 Hz to 8.5 Hz was designed, and the precision tube separation device is loaded by pneumatic fatigue shock to achieve tube precision separation. In addition, a tube fracture quality evaluation method is proposed. According to the test results, the stress concentration effect of V-notch can significantly reduce the average stress in the process of tube fatigue separation and accelerate the generation of microcracks. Under the continuous action of repeated impact load, the loading method of multistep decline can effectively control the rapid crack initiation and stable expansion of the GCr15 tube V-notch root crack. Moreover, the tube final fracture region has relatively small defects, which can obtain good fracture quality.

Multistep Model Predictive Control for Electrical Drives—A Fast Quadratic Programming Solution

Due to its merits of fast dynamic response, flexible inclusion of constraints and the ability to handle multiple control targets, model predictive control has been widely applied in the symmetry topologies, e.g., electrical drive systems. Predictive current control is penalized by the high current ripples at steady state because only one switching state is employed in every sampling period. Although the current quality can be improved at a low switching frequency by the extension of the prediction horizon, the number of searched switching states will grow exponentially. To tackle the aforementioned issue, a fast quadratic programming solver is proposed for multistep predictive current control in this article. First, the predictive current control is described as a quadratic programming problem, in which the objective function is rearranged based on the current derivatives. To avoid the exhaustive search, two vectors close to the reference derivative are preselected in every prediction horizon. Therefore, the number of searched switching states is significantly reduced. Experimental results validate that the predictive current control with a prediction horizon of 5 can achieve an excellent control performance at both steady state and transient state while the computational time is low.

The use of augmented rotor inflow to predict rotorcraft responses in hover and low-speed manoeuvres

AbstractThe rotorcraft is a complex dynamical system that demands specialist modelling skills, and a high level of understanding of the aeromechanics arising from the main rotor wake and aerodynamic couplings. One such example is the difficulty predicting off-axis responses, particularly in hover and low-speed flight, associated with induced velocity variation through the rotor disk resulting from the rotor wake distortions. Various approaches have been developed to deal with this phenomenon but usually demand prerequisites of high levels of expertise and profound aerodynamic knowledge. This paper presents a new and practical approach to capturing this wake distortion through an augmented rotor inflow model. The proposed model is coupled with a nonlinear simulation using the FLIGHTLAB environment, and comparisons are made between the simulation results and flight test data from the National Research Council of Canada’s Advanced System Research Aircraft in hover and low speed. Results show good predictability of the proposed nonlinear model structure, demonstrated by its capability to closely match the time responses to multi-step control inputs from flight test. The results reported are part of ongoing research at Liverpool and Cranfield University into rotorcraft simulation fidelity.

Control of LPV Modeled AC-Microgrid Based on Mixed H2/H∞ Time-Varying Linear State Feedback and Robust Predictive Algorithm

This paper presents a robust model predictive control (RMPC) method with a new mixed H2/<inline-formula> <tex-math notation="LaTeX">$\text{H}\infty $ </tex-math></inline-formula> linear time-varying state feedback design. In addition, we propose a linear parameter-varying model for inverters in a microgrid (MG), in which disturbances and uncertainty are considered, where the inverters connect in parallel to renewable energy sources (RES). The proposed RMPC can use the gain-scheduled control law and satisfy both the H2 and <inline-formula> <tex-math notation="LaTeX">$\text{H}\infty $ </tex-math></inline-formula> proficiency requirements under various conditions, such as disturbance and load variation. A multistep control method is proposed to reduce the conservativeness caused by the unique feedback control law, enhance the control proficiency, and strengthen the RMPC feasible area. Furthermore, a practical and efficient RMPC is designed to reduce the online computational burden. The presented controller can implement load sharing among distributed generators (DGs) to stabilize the frequency and voltage of an entire smart island. The proposed strategy is implemented and studied in a MG with two DG types and various load types. Specifically, through converters, one type of DGs is used to control frequency and voltage, and the other type is used to control current. These two types of DGs operate in a parallel mode. Simulation results show that the proposed RMPCs are input-to-state practically stable (ISpS). Compared with other controllers in the literature, the proposed strategy can lead to minor total harmonic distortion (THD), lower steady-state error, and faster response to system disturbance and load variation.

A novel Multi-step Model Predictive Control design for three-phase T-Type inverter in grid-connected mode

A novel Multi-step Model Predictive Control design for three-phase T-Type inverter in grid-connected mode

Formation of hierarchical Bi2MoO6/ln2S3 S-scheme heterojunction with rich oxygen vacancies for boosting photocatalytic CO2 reduction

Employing photocatalytic technology to promote CO2 photoreduction into carbon fuels is of great significance to the field of renewable energy. Herein, the hierarchical Bi2MoO6@In2S3 heterostructured nanotubes rich in surface oxygen vacancies (SOVs) are synthesized by a multi-step control strategy for visible-light-driven CO2 reduction. The combination of density functional theory (DFT) calculations and in-situ X-ray photoelectron spectroscopy (ISI-XPS) confirms the existence of the S-scheme charge transfer mechanism and SOVs, which will accelerate the separation of charges and ameliorate the redox capability. Additionally, the unique hierarchical hollow architectures also effectively promote the efficiency of light utilization and provide abundant reactive sites. Consequently, the optimized Bi2MoO6-SOVs@In2S3 heterogeneous nanotubes display remarkable activity under visible light irradiation, with the yield rate and selectivity for CO-generation are 28.54 μmol g-1h−1 and 94.1%, respectively. This work provides new opportunities for the design of hierarchical hollow heterojunctions with an S-scheme charge transfer mechanism for effective photocatalytic CO2 reduction.

Fast and Deterministic Fabrication of Sub-5 Nanometer Solid-State Pores by Feedback-Controlled Laser Processing.

Nanopores are single-molecule sensors capable of detecting and quantifying a broad range of unlabeled biomolecules including DNA and proteins. Nanopores’ generic sensing principle has permitted the development of a vast range of biomolecular applications in genomics and proteomics, including single-molecule DNA sequencing and protein fingerprinting. Owing to their superior mechanical and electrical stability, many of the recent studies involved synthetic nanopores fabricated in thin solid-state membranes such as freestanding silicon nitride. However, to date, one of the bottlenecks in this field is the availability of a fast, reliable, and deterministic fabrication method capable of repeatedly forming small nanopores (i.e., sub 5 nm) in situ. Recently, it was demonstrated that a tightly focused laser beam can induce controlled etching of silicon nitride membranes suspended in buffered aqueous solutions. Herein, we demonstrate that nanopore laser drilling (LD) can produce nanopores deterministically to a prespecified size without user intervention. By optimizing the optical apparatus, and by designing a multistep control algorithm for the LD process, we demonstrate a fully automatic fabrication method for any user-defined nanopore size within minutes. The LD process results in a double bowl-shaped structure having a typical size of the laser point-spread function (PSF) at its openings. Numerical simulations of the characteristic LD nanopore shape provide conductance curves that fit the experimental result and support the idea that the pore is produced at the thinnest area formed by the back-to-back facings bowls. The presented LD fabrication method significantly enhances nanopore fabrication throughput and accuracy and hence can be adopted for a large range of biomolecular sensing applications.

Abstract 205: Astraea: A first-in-class biomarker database integrating genomic, transcriptomic, and tumor microenvironment properties for precision oncology

Abstract Along with advances in precision oncology, checkpoint inhibitors and targeted therapies have substantially improved outcomes for cancer patients. However, many patients still demonstrate a limited response to these therapies due to many biological factors, including genetic heterogeneity, unique molecular profiles, and the complex features of the tumor microenvironment (TME). Therefore, the selection of personalized effective treatment requires a comprehensive source of therapy response biomarkers, enabling precision medicine strategies for therapy selection. Here, we present a first-in-class automated biomarker analysis database, Astraea, that comprehensively describes genomic, transcriptomic, and TME biomarkers across a wide array of cancers. Automated daily literature reviews of the therapeutic efficacy of biomarkers provided the foundation of Astraea. To date, the database contains a total of 4,116 published biomarkers associated with genomic events, the TME, and targeted proteomic, transcriptomic, and gene signatures. To ensure accuracy of the final inclusion of biomarkers in the database, a multi-step quality control process was implemented that includes an automatic validation step and manual review. After selection, each biomarker is organized into a unique profile in the database which includes assay specifics, the biomarker-associated cancer type, therapy, primary study design, and statistical analysis. Data available from The Cancer Genome Atlas (TCGA) was then used to aggregate interrelated biomarkers into 25 biologically meaningful clusters, with the most prominent clusters identified as components of the TME (i.e., cytotoxic T cells, B cells, fibroblasts) and proliferation rate signatures. The aggregation enabled an easier interpretation and understanding of potentially actionable molecular findings as well as insight into unique neoplastic drivers. To apply Astraea in a clinical setting, we then developed a platform to match therapies to patients based on 1) identified biomarkers prioritized according to level of evidence, including both number of associated publications, statistical strength of individual studies, and cohort size and 2) therapies scored according to supporting biomarkers and associated relevance (resistance/response). By providing comprehensive, up-to-date biomarker identification and matching through utilization of a large automated multi-platform database, this technique aids in the identification and application of biomarkers unique to each patient. Taken together, our results show that Astraea, accompanied by a multi-step personalized cancer therapy-matching platform, could improve precision medicine strategies and help optimize therapeutic decisions. Citation Format: Azamat Gafurov, Ivan Mamichev, Elena V. Vasileva, Georgy D. Sagaradze, Maria S. Shitova, Grigorii Nos, Nikita Kotlov, Jessica H. Brown, Alexander Bagaev, Nathan Fowler. Astraea: A first-in-class biomarker database integrating genomic, transcriptomic, and tumor microenvironment properties for precision oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 205.

A material stress test study on occurrence of leakage and material failure of peritoneal dialysis (PD) catheters

Peritonitis is a common complication of peritoneal dialysis (PD). Our root cause analysis allowed to attribute some cases to leakage of the PD catheter. Accordingly, a clinically based stress test study on potential material damage issues of PD catheters was performed, focusing on material damage caused by cleaning, de- and attachment procedures during dialysate changes and on the individual storage methods of PD catheters between dialysate changes. PD catheters were exposed to both chemical stress by repeating dialysate-flow and physical stress simulating de- and connecting, fixation, pressure, flexing, folding etc.—simulating standard clinical daily routine of 8–10 years PD catheter usage. Potentially by normal usage caused damages should be then detected by intraluminal pressure, light- and electron microscopy. The multi-step visual control showed no obvious damages on PD catheters nor any leakage or barrier indulgence. Our tests simulating daily routine usage of PD catheters for several years could not detect any material defects under chemical or physical stress. Hence, we presume that most PD catheter damages, as identified cause for peritonitis in some of our patients, may be due to accidental, unnoticed external damage (e.g. through scissors, while changing dressings) or neglecting PD catheter handling specifications.

Reduced-Order Observer-Based Leader-Following Formation Control for Discrete-Time Linear Multi-Agent Systems

Formation control of discrete-time linear multi-agent systems using directed switching topology is considered in this work via a reduced-order observer, in which a formation control protocol is proposed under the assumption that each directed communication topology has a directed spanning tree. By utilizing the relative outputs of neighboring agents, a reduced-order observer is designed for each following agent. A multi-step control algorithm is established based on the Lyapunov method and the modified discrete-time algebraic Riccati equation. A sufficient condition is given to ensure that the discrete-time linear multiagent system can achieve the expected leader-following formation. Finally, numerical examples are provided so as to demonstrate the effectiveness of the obtained results.

Construction of hollow TiO2/CuS nanoboxes for boosting full-spectrum driven photocatalytic hydrogen evolution and environmental remediation

The plasma semiconductor Cu2-xS has disclosed enormous potential in the field of broad-spectrum driven photocatalyst design due to its unique LSPR effect in the NIR region. Nevertheless, ineffective carrier separation and severe photocorrosion hinder the further functional applications of this promising material. Here, the TiO2@CuS double-shell nanoboxes were successfully constructed via a multistep control strategy involving template participation, and were firstly employed to broad-spectrum photocatalytic hydrogen evolution and pollutant elimination. The hollow core-shell nanocubes with LSPR effect have prominent advantages in broad-spectrum light response, improved photocharge separation and effective photocorrosion inhibition. Besides, the heterogeneous cavity structure also provides a larger specific surface area and ameliorates the light efficiency. Consequently, the optimized TiO2@CuS double-shell nanoboxes exhibit superior activity for H2 production under full-spectrum/NIR light irradiation (2467 and 173 μmol g−1 h−1, respectively), and maintain high activity in the photodegradation of stubborn pollutants. This work will motivate hollow photocatalysts designed to effectively utilize the spectrum and protect the sulfide core.

Adaptive Algorithm for Controlling the Soft Vertical Landing of an Unmanned Return Spacecraft. I.

A new adaptive algorithm for multistep terminal control (MSTC) of a soft landing of an unmanned return spacecraft (URS) is proposed that is based on more accurate analytical solutions of differential equations with identification of real parameters of the URS and the atmosphere.

The Multi-step Transformation Control and Exponentially Stability for Linear Discrete-time Systems with Additional Control Delay

Background: Few results about the controller of linear discrete-time system with control delay are reported. Methods: By means of the multi-step transformation with memory, the linear discrete-time systems with additional control delay can be transformed to the equivalent linear discrete-time systems without control delay, and the dimension of the transformed system is not increased. By designing the optimal controller of the finite horizon optimal controller of linear discrete time systems without time delay, the controller of linear discrete time systems with additional control delay can be obtained. At the same time, by designing the optimal controller of the infinite horizon optimal controller of linear discrete time systems without time delay, the controller of linear discrete time systems with additional control delay can be obtained as well. Results: The corresponding finite horizon optimal controller has proved to render the closed-loop system exponentially stable. And the corresponding infinite horizon optimal controller has proved to render the closed-loop system exponentially stable when the open-loop system is either controllable or stabilizable. Finally, two examples are used to verify the theoretical results of this paper. Conclusion: The controller design and the exponentially stability of discrete-time linear system with additional state delay will be investigated in the future.

Control Optimization of Stochastic Systems Based on Adaptive Correction CKF Algorithm

Standard cubature Kalman filter (CKF) algorithm has some disadvantages in stochastic system control, such as low control accuracy and poor robustness. This paper proposes a stochastic system control method based on adaptive correction CKF algorithm. Firstly, a nonlinear time-varying discrete stochastic system model with stochastic disturbances is constructed. The control model is established by using the CKF algorithm, the covariance matrix of standard CKF is optimized by square root filter, the adaptive correction of error covariance matrix is realized by adding memory factor to the filter, and the disturbance factors in nonlinear time-varying discrete stochastic systems are eliminated by multistep feedback predictive control strategy, so as to improve the robustness of the algorithm. Simulation results show that the state estimation accuracy of the proposed adaptive cubature Kalman filter algorithm is better than that of the standard cubature Kalman filter algorithm, and the proposed adaptive correction CKF algorithm has good control accuracy and robustness in the UAV control test.

Optimal command control of flight paths of aerospace system hypersonic first stage in conditions of atmospheric disturbances

The paper deals with disturbed motion of the hypersonic first stage of an aerospace system in accelerated climb. Disturbances are deviations of atmospheric density from the values of a standard model. A multistep control process is accepted. The command angle-of-attack schedule is specified at each step of control using the method of Pontryagins maximum principle. The nominal control program is applied at the first step. We present the results of modeling disturbed motion with command control of the angle of attack for maximum rarified and maximum dense atmosphere. The angle-of-attack constraint is violated for rarified and dense atmosphere at the end of the accelerated flight segment when a boundary value problem is solved. If command control obtained as a result of solving the boundary value problem is maintained the target terminal speed and altitude conditions are fulfilled. The finite value of the flight path inclination angle for rarefied atmosphere is 10% lower than the target one, while for dense atmosphere it is 12% higher.