Continuous Control Research Articles

The method for calculating phase angle between exciter force of vibration exciter and roller displacement

Introduction. In the process of soil compaction it is important to have information about the current density of the layer, as it enables to quickly adjust the load on the compacted material. The field methods of compaction quality assessment do not cope with this task, as they make point estimation within the pavement area. Therefore, continuous compaction monitoring systems installed on vibratory road rollers are becoming increasingly common. The systems developed by BOMAG and AMMANN require, among other things, the phase angle between the exciter force and the roller movement to calculate the compaction quality index. The phase angle is determined by the unbalance position sensor, which is very labor-intensive. In addition, continuous compaction monitoring systems include an accelerometer. The purpose of this paper is to develop an indirect method for calculating the phase angle from accelerometer readings.The method of research. In order to achieve the purpose of the work, a roller-soil single-mass model in a typical mode for vibratory rollers (periodic loss of contact) has been studied. As a result of modeling it has been found that the reaction of the compacted material has the main influence on the vertical component of a roller acceleration and practically does not affect the horizontal component. This is confirmed by the experimental data.Results. The phase angle can be determined by mutual correlation of the horizontal and vertical acceleration signals of the roller obtained with the accelerometer.Conclusion. The study proposes a new method of calculating the phase angle between the exciter force and the roller displacement, which eliminates the direct measurement of this angle. The calculation of the angle is based on the readings of a two-axis accelerometer installed on the road roller. The proposed method enables to simplify the system of continuous compaction control and reduce the labor intensity of phase angle measurement.

Bureaucratic Politicization in the Process of Changing Bureaucratic Positions After the Vacancy of the Deputy Regent of Dharmasraya Regency in 2022

Bureaucratic politicization refers to introducing bureaucratic reform in the management of regional government. The regional head and deputy regional head posts are political roles that oversee administrative positions within a region, so political influence is inherently intertwined with the bureaucracy. Dharmasraya Regency exhibits signs of bureaucratic politics, as seen by a vacancy in the position of deputy regent. This vacancy has resulted in numerous examples of non-job ASN inside the government of Dharmasraya Regency. This thesis seeks to analyze the political dynamics involved in the transition of bureaucratic positions in Dharmasraya Regency, particularly after the vacancy of the deputy regent position. This study employs a qualitative research methodology utilizing a case study approach. This thesis uses the theory of bureaucratic politicization pioneered by Barbara Geddes. The findings of this study suggest that there are indicators of the techniques employed by the regent in his role as PPK to determine bureaucratic positions. The regent also has a vested interest in the bureaucracy, as he desires a loyal and efficient bureaucratic system to ensure continued control until the end of his term. The regent's politicization of the bureaucracy is seen in the ambiguous enforcement of heavy sanctions against ASN.

A Strategy for Preparing Quantum Squeezed States Using Reinforcement Learning

AbstractA scheme leveraging reinforcement learning to engineer control fields for generating non‐classical states is proposed. It is exemplified by the application to prepare spin‐squeezed states for an open collective spin model where a linear control field is designed to govern the dynamics. The reinforcement learning agent determines the temporal sequence of control pulses, commencing from a coherent spin state in an environment characterized by dissipation and dephasing. Compared to the constant control scenario, this approach provides various control sequences maintaining collective spin squeezing and entanglement. It is observed that denser application of the control pulses enhances the performance of the outcomes. However, there is a minor enhancement in the performance by adding control actions. The proposed strategy demonstrates increased effectiveness for larger systems. Thermal excitations of the reservoir are detrimental to the control outcomes. Feasible experiments are suggested to implement this control proposal based on the comparison with the others. The extensions to continuous control problems and another quantum system are discussed. The replaceability of the reinforcement learning module is also emphasized. This research paves the way for its application in manipulating other quantum systems.

A modeling and control framework for extraction processes

Many continuously operated pharmaceutical process routes have been presented recently. Most of these cover the synthesis of the active pharmaceutical ingredient (API) or solid dosage processing. However, the API purification is also gaining attraction. One widespread and waste-intensive unit operation for purification is the liquid–liquid-extraction (LLE). In continuous manufacturing active process control is required, especially for fast processes. Control concepts must be able to react on product quality deviations caused by disturbances or inadequate process settings in real-time. In this study different control concepts for LLE were developed for an extraction column and a multistage extraction. A universally applicable process model was derived and parametrized. The impact of several control concepts including different real-time measurements was evaluated in simulation for both LLE process routes. The results show that simulation tools based on proper process models can support the selection of the most efficient process route and of suitable control concepts.

Solution of optimal reactive power dispatch by Lévy-flight phasor particle swarm optimization

Optimal reactive power dispatch (ORPD) problems are important tools for the sake of security and economics of power systems. The ORPD problems are nonlinear optimization problems to minimize the real power losses and voltage profile enhancement by optimizing several discrete and continuous control variables. This paper proposes a Lévy-flight phasor particle swarm optimization (LPPSO) for solving ORPD problems while considering real power losses and voltage profile in two standard power systems. The simulation results demonstrate that the LPPSO algorithm proves itself as an acceptable method for reaching a more optimal solution for the ORPD problems.

Designing adaptive continuous event-triggered consensus protocol for nonlinear multi-agent systems with a nonautonomous leader

This article investigates the leader-follower consensus (LFC) issue for a class of Lipschitz-type nonlinear multi-agent systems (NMASs) that include one leader and multiple followers. To enable the leader to denote a flexible noncooperative reference trajectory, it is considered that the leader's control input (LCI) exists and cannot be obtained by other followers. A novel robust distributed adaptive continuous event-triggered control protocol is developed. Through rigorous analysis, it is shown that under this control protocol, the NMASs can reach LFC and the Zeno behavior is not exhibited. Compared to existing research works, the main merits of this control protocol are as follows: 1) It is chattering-free while can completely eliminate the adverse effects brought by the LCI; 2) The communication traffic between agents is lower; 3) It can be executed in a fully distributed way (i.e., no global information is required for the design process of this control protocol). Finally, the effectiveness of the developed control protocol is validated through an example.

Microwave assisted efficient non-degenerate four-wave mixing in pulsed regime

We theoretically investigate a N-type 87Rb atomic system for efficient generation and control of a non-degenerate four wave mixing (FWM) signal in pulsed regime. The susceptibility of the atomic medium is customized as a gain profile by a weak probe pulse and two strong continuous wave control fields which allow us to generate the pulsed FWM signal. We study the propagation dynamics of the generated FWM signal inside the nonlinear medium. The FWM signal obtains the exact shape of the probe pulse and travels without changing the shape whereas, the probe pulse is absorbed inside the nonlinear medium. The conversion efficiency of this scheme without a MW field is 5.36%. However, a MW field that couples two metastable ground states enhances the conversion efficiency to 20.6%. The generation and control of such FWM signal in pulsed regime has important applications in signal processing, optical communication and information science.

Ozone Pollution in the North China Plain during the 2016 Air Chemistry Research in Asia (ARIAs) Campaign: Observations and a Modeling Study

To study air pollution in the North China Plain (NCP), the Air Chemistry Research in Asia (ARIAs) campaign conducted airborne measurements of air pollutants in spring 2016. High pollutant concentrations, with O3 > 100 ppbv, CO > 500 ppbv, and NO2 > 10 ppbv, were observed. CMAQ simulations with the 2010 EDGAR emissions capture the spatial and temporal variations in ozone and its major precursors such as NO2 and VOCs, with significant underestimation. Differences between CMAQ simulations and satellite observations reflect changes in anthropogenic emissions, decreased NOx emissions in megacities such as Beijing, but slight increases in other cities and rural areas. CMAQ also underestimates HCHO and CO, suggesting adjustments of the 2010 EDGAR emissions are necessary. HCHO/NO2 column ratios derived from OMI measurements and CMAQ simulations show that VOC-sensitive chemistry dominates the ozone photochemical production in eastern China, suggesting the importance of tightening regulations on anthropogenic VOC emissions. After adjusting emissions based on satellite observations, better model performance was achieved. Because of the VOC-sensitive environment in ozone chemistry over the NCP, the underestimation of anthropogenic emissions could be important for CMAQ simulations, while future study and regulations should focus on VOC emissions with continuous controls on NOx emissions in China.

Quality prediction and process parameter importance analysis of cord steel during continuous casting

Continuous casting process control is crucial for enhancing the quality of steel. Center segregation, a key indicator of quality, plays a significant role in ensuring the quality of cord steel. However, the complexities of the continuous casting process, such as data silos, delayed inspections, uneven sampling and non-linearity, pose challenges to making timely improvements in quality. This study introduces an ensemble learning algorithm called LOF-KPCA-XGBoost, which combines Local Outlier Factor (LOF) sample weight, Kernel Principal Component Analysis (KPCA) and eXtreme Gradient Boosting (XGBoost) for online prediction of the center segregation coefficient of continuous casting billets. The algorithm achieves an accuracy of 90% and 98% within ±3% and ±5% relative error, respectively, with an average relative error of 1.6%. A sensitivity analysis method that leverages multivariable feature fluctuations is proposed to identify the process parameters that affect billet quality. The proposed method assists steel enterprises in improving the quality of cord steel billets.

NON-ANALYTICAL DESIGN OF THE PI CONTROLLER OPTIMAL PARAMETERS FOR A CONTINUOUS LINE

The multiple motor drives are complex and coupled MIMO nonlinear systems. Due to the complexity of their mathematical models the development of effective control systems is quite complicated task. This paper presents the design of optimal control based on the quadratic optimality criterion for the central section of the continuous production line using soft computer methods when fuzzy model of the controlled system has been used. In this case, the proposed method demonstrates non-analytical design of the optimal parameters of PI controller with emphasis on minimal knowledge on the controlled system. The realized experimental measurements on a continuous line laboratory model confirmed the effectiveness and the good dynamic properties of the optimal continuous line controller and also its applicability to others MIMO nonlinear dynamic systems.

ProteoCure: A European network to fine-tune the proteome

Proteins are essential molecular actors in every cellular process. From their synthesis to their degradation, they are subject to continuous quality control mechanisms to ensure that they fulfil cellular needs in proper and timely fashion.Proteostasis is a key process allowing cells or organisms to maintain an appropriate but dynamic equilibrium of their proteome (the ensemble of all their proteins). It relies on multiple mechanisms that together control the level, fate and function of individual proteins, and ensure elimination of abnormal ones. The proteostasis network is essential for development and adaptation to environmental changes or challenges. Its dysfunctions can lead to accumulation of deleterious proteins or, conversely, to excessive degradation of beneficial ones, and are implicated in many diseases such as cancers, neurodegeneration, or developmental and aging disorders. Manipulating this network to control abundance of selected target proteins is therefore a strategy with enormous therapeutic or biotechnological potential.The ProteoCure COST Action gathers more than 350 researchers and their teams (31 countries represented) from the academic, clinical, and industrial sectors, who share the conviction that our understanding of proteostasis is mature enough to develop novel and highly specific therapies based on selective tuning of protein levels. Towards this objective, the Action organizes community-building activities to foster synergies among its participants and reinforce training of the next generation of European researchers. Its ambition is to function as a knowledge-based network and a creative exchange hub on normal and pathologic proteostasis, focusing on developing innovative tools modulating the level of specific protein(s).

Efficacy and safety of anthracycline combined with alkylating agent for treatment of advanced leiomyosarcoma.

e23558 Background: Leiomyosarcoma(LMS) accounts for 12-14% of all soft tissue sarcomas(STSs) and this STS subtype has high metastatic potential. We evaluated the efficacy and safety of epirubicin combined with temozolomide(EPI-TMZ) for treatment of advanced LMS. Methods: Oral TMZ(200 or 300 mg) was administered each day on days 1-5, and intravenous EPI(60 mg/m2) was administered on days 1-4, with even distribution over these 96h within a 21‐day cycle. After 8 cycles of intensive chemotherapy, monodrug maintenance therapy consisted of TMZ alone(dosing as in the primary therapy, most choices, stop taking medication after 1 year control), and two-drug maintenance therapy consisted of TMZ with thalidomide(only 3 patients). All patients-initiated EPI-TMZ treatment between January 2018 and January 2024. Results: We examined 31 patients who received EPI-TMZ. There were 25 females and 6 males, with average age 50.5 years old. All were unresectable patients derived from abdominal retroperitoneum, uterus, inferior vena cava, mesentery, small intestine, stomach, thighs, elbows, wrists, pancreas, nasal cavity, meantime with distant metastasis. Average Ki67 is above 40%. This was a first-line treatment in 16 patients, a second- or third-line in 13, and a fourth-line in 2. At the time of data cutoff (January 31, 2024), the median PFS was 12.6 months, 1 patient had cCR, 9 PR and 20 SD. The ORR was 32.3%(10/31) and DCR was 96.8%(30/31). The only patient who progressed after two cycles of medication had PMS2 deficiency, and studies have reported that it may have a primary resistance mechanism. Even the shortest disease control period is also 5 months, indicating this regime has significant clinical benefit. Some patients have undergone tumor reduction surgery to improve quality of life after achieving significant tumor relief. The current longest PFS is 42 months up to now and under continuous tumor control. Previous retrospective studies indicated the best mPFS was 9.2 months and the best ORR was 30.9%. 2023 ESMO declared prospective doxorubicin with trabectedin obtained mPFS of 12.19 months. The most common treatment-related adverse effects were leukopenia, neutropenia, thrombocytopenia, anemia, nausea, vomiting, fatigue, and oral mucositis. Two patients had severe febrile neutropenia, but there were no treatment-related deaths, survival data follow-up. Conclusions: EPI-TMZ is potentially effective for treatment of advanced LMS, and the adverse effects appear tolerable. EPI-TMZ may provide better outcomes than reported in previous studies of other treatments for advanced LMS, worth further prospective research.

Research on reactive voltage control strategy for substations connected to photovoltaic systems

Abstract The reactive voltage control of conventional substations is mainly realized by adjusting the switching of capacitors or reactors and changing the transformer ratio, which can only be controlled discretely. The PV inverter can carry out continuous reactive power control, and the reactive voltage control strategy of the conventional substation does not consider the reactive power control of the PV. In this paper, the nine-zone diagram control strategy of the traditional substation is used as a reference, and the reactive voltage optimization control strategy of a substation with a photovoltaic power generation system is constructed considering the characteristics that photovoltaic inverter can continuously regulate reactive power. Taking the internal power grid of an enterprise as an example, the simulation analysis verifies the effectiveness of the control strategy, which can ensure that the voltage and power factor of the grid-connected access point are qualified. At the same time, the voltage fluctuation of the grid-connected point can be reduced, and the network consumption can be reduced.

Decoupled Continuous Control Set Model Predictive Control for T-Type Three-Phase Four-Leg Three-Level Inverters Driving Constant Power Loads

Decoupled Continuous Control Set Model Predictive Control for T-Type Three-Phase Four-Leg Three-Level Inverters Driving Constant Power Loads

Reinforcement learning pulses for transmon qubit entangling gates

The utility of a quantum computer is highly dependent on the ability to reliably perform accurate quantum logic operations. For finding optimal control solutions, it is of particular interest to explore model-free approaches, since their quality is not constrained by the limited accuracy of theoretical models for the quantum processor—in contrast to many established gate implementation strategies. In this work, we utilize a continuous control reinforcement learning algorithm to design entangling two-qubit gates for superconducting qubits; specifically, our agent constructs cross-resonance and CNOT gates without any prior information about the physical system. Using a simulated environment of fixed-frequency fixed-coupling transmon qubits, we demonstrate the capability to generate novel pulse sequences that outperform the standard cross-resonance gates in both fidelity and gate duration, while maintaining a comparable susceptibility to stochastic unitary noise. We further showcase an augmentation in training and input information that allows our agent to adapt its pulse design abilities to drifting hardware characteristics, importantly, with little to no additional optimization. Our results exhibit clearly the advantages of unbiased adaptive-feedback learning-based optimization methods for transmon gate design.

Event-Based Intermittent Formation Control of Multi-UAV Systems Under Deception Attacks.

This article investigates the problem of event-based intermittent formation control for multi-UAV systems subject to deception attacks. Compared to the available research studies on multi-UAV systems with continuous control strategy, the proposed intermittent control strategy saves a large amount of computation resources. An average method is introduced in developing the event-triggered mechanism (ETM) such that the amount of unexpected triggering events induced by uncertain disturbances is greatly reduced. Moreover, such a mechanism can further decrease the average data-releasing rate, thereby alleviating the burden of network bandwidth. Sufficient conditions for multi-UAV systems with deception attacks to achieve the predefined formation are obtained with the aid of Lyapunov stability theory. Finally, the validity of the proposed theoretical results is demonstrated via a simulation example.

Output Feedback Based Adaptive Continuous Sliding Mode Control for Mechanical Systems With Actuator Faults

Output Feedback Based Adaptive Continuous Sliding Mode Control for Mechanical Systems With Actuator Faults

Target Tracking Two Degrees of Freedom State Feedback Control for Continuous Flow Microfluidic Chips Temperature Controller

Microfluidic chips represent a cutting-edge technology for manipulating fluids within micrometer-scale spaces and are gradually becoming a new favorite platform in life science research. Precise and fast zonal temperature control is essential for accelerating biological experiments. However, current multi-channel temperature controllers typically rely on multiple channel sets to achieve single set-point control, which results in discrepancies between the fluid temperature distribution and sensor temperature due to the distributed temperature field in the fluid channel. To estimate the actual temperature and implement gradient temperature control, this paper introduces an extension of the target tracking (TT) two degrees of freedom (2DOF) state feedback control (SFC) method, followed by a presentation of simulation and experimental results. Through comparisons with an enhanced PID system in both simulation and experimentation, the paper demonstrates an 8.96% reduction in the maximum temperature difference across different regions and a 27.89% decrease in the time taken to reach various temperatures. This solution effectively addresses the existing challenges in temperature control for microfluidic chips, offering a more precise and stable control within the desired temperature range.

Application of Continuous Stability Control to a Lightweight Solar-Electric Vehicle Using SMC and MPC

This paper investigates the application of contusion stability yaw control of a lightweight solar-electric vehicle. The vehicle’s customized design envelope makes it more sensitive to variations in load due to its low weight and relatively large size. To address this issue, control strategies were developed using differential motor torques to generate direct yaw moments using the vehicle’s rear in-wheel motors. This paper introduces the working conditions of solar vehicles and demonstrates the necessity of stability control. Vehicle parameters such as mass and center of gravity position are obtained to apply control to the real vehicle. The paper then describes two stability control strategies, using (i) sliding-mode control (SMC) and (ii) model predictive control (MPC). To account for the road bank angle of the test area and the impact of additional weight from a driver and passenger, a Kinematic-Based Observer is designed to estimate the vehicle’s side-slip based on measured values. To collect real-time data, a dSPACE MicroAutobox was installed on the solar vehicle. The results show the effect of the observer and controllers under different vehicle speeds and load conditions. Finally, closed-loop simulation results are presented to support the findings from the open-loop testing.

Data-based deep reinforcement learning and active FTC for unmanned surface vehicles

In this study, we investigate the active fault tolerant control(AFTC) of unmanned surface vehicles (USV) under actuator faults and environmental disturbances using deep reinforcement learning(DRL). We realize the perception of the environment and continuous control through end-to-end learning based on neural networks(NN). The contents of AFTC include 1) actuator fault estimation(FE) based on long short-term memory(LSTM) and 2) a fault tolerant control(FTC) framework based on a deep deterministic policy gradient to enable the USV to learn autonomous decision making through training. We introduce mixed noise based on Gaussian noise and Ornstein–Uhlenbeck noise into the training, which increases the exploration space and improves the generalization ability of the USV. An experiential replay method based on quantum computing is designed to distinguish the importance of samples through preparation and depreciation operations, enabling a guided learning process. Finally, we verify the universality and effectiveness of the AFTC framework by tracking straight and sinusoidal trajectories.