Collaborative Control Strategy Research Articles

A Data-Driven Control Strategy for Urban Express Ramp.

Expressway, as the main artery of urban traffic, realizes the smooth operation of the whole urban road network through reasonably balancing the traffic flow. However, due to the lack of reasonable and effective traffic control, the safety and congestion of expressways are becoming more and more serious. The development of intelligent network technology provides a new idea to solve the control problem of expressways. In this paper, a data-driven ramp control model of urban expressway is constructed. The interaction of traffic information is realized through intelligent network connection technology. The cooperative control strategy of VSL and RM is adopted. The mutual feedback of VSL and RM is realized based on the improved METANET model. The simulation experiment based on VISSIM secondary development shows that the collaborative control strategy under the intelligent network environment could make the vehicle travel time reduced by 20.59% and the speed difference between adjacent sections of the expressway mainline by 34.07%, which realized the coordinated control of the mainline and the on-ramp under the intelligent network environment, alleviate the expressway traffic congestion, reduce the traffic pressure, and improve the efficiency of the road network.

Aspect of join ingress authority for civic directory

T Nowadays, Cloud computing is a major and emerging platform that provides various IT related services. The two major factors which inhibit the growth of cloud computing are privacy and security. Hackers can break into public or private cloud environment and steal the data. They can use it with malicious intent such as either use or sell the sensitive information obtained from the business cloud environment. Hence, user’s safety depends upon security level in cloud service providers. For an enterprise, there are enormous amount of data is stored and most of them are sensitive information which can affect huge number of people or an entire business organization. Networks are the medium through which all tasks are performed. For access control, being one of the classic research topics, various schemes have been proposed and implemented. In existing key policy, when the key which provides access to the data falls into the wrong hands, they can use it with malicious intent such as steal or alter the data or sell the sensitive information obtained from the business cloud environment. Using our proposed algorithm, we can able to provide a collaborative access control scheme for public cloud storage. This system is implemented using encryption method called Attribute Based Encryption. Encrypting data ensures that even if the data falls into the wrong hands, it is useless as long as its keys remain secure. Even when the hacker gets access to the key, he cannot access the data as only the assigned users can access the data using the key.

Human–Robot Collaborative Control in a Virtual-Reality-Based Telepresence System

In this paper, we develop a robotic telepresence system to provide remote users with immersive embodiment in local environments through a custom-designed mobile robot. The proposed telepresence system uses a virtual reality (VR) device to connect a remote user to the robot. Three dimensional visual data from a RGB-D camera are rendered for real-time stereoscopic display in the VR device, which forms a deeply-coupled human machine system and creates an immersive experience of telepresence. Based on a user study, it is found that better user experience can be achieved by allowing the robot to track the speaker while being aware of the intention of the remote user. To this end we propose a human-robot collaborative control framework based on human intention recognition and sound localization. The intentions of head movement of the remote user are inferred based on the motion of the VR device using hidden Markov models. The speaker is tracked through sound source localization using a microphone array. A collaborative control scheme is developed to fuse the control from the robot and the remote user. Experiments are conducted in both one-to-one and one-to-two remote conversation scenarios. The results show that the proposed system can significantly improve the immersiveness and performance of robotic telepresence systems, therefore greatly enhancing the user experience of such telepresence systems.

Collaborative control of lateral stability and braking performance of vehicles during braking-in-turn maneuver

The active safety control of vehicles during braking-in-turn maneuver involves longitudinal and lateral dynamic control. The lateral stability and braking performance of vehicles can be ensured by properly coordinating the longitudinal and lateral forces of tires. In this study, a control system with a three-layer structure is used to achieve the above-mentioned purpose. The expected yaw rate and sideslip angle are adopted to calculate the direct yaw moment to guarantee the lateral stability of vehicles in the motion tracking layer. Considering the minimization of tire workload usage and braking force deviation as optimization objectives, torque allocation control is achieved for the direct yaw moment with lateral stability and the upper bound of longitudinal force (UBLF) of tires as constraint in the torque allocation layer. In the braking hydraulic pressure control layer, the hydraulic pressure in the wheel cylinder is adjusted according to the expected braking force of the wheel. This study proposes a method for determining the UBLF based on the optimal slip ratio (UBLF_OSR), which cannot only avoid obtaining the lateral force of tires but also directly restrict the distribution of tire force. The control system performance is analyzed on the basis of MATLAB/AMESim co-simulation. Results show that the proposed collaborative control strategy of lateral stability and braking performance ensures the lateral stability and braking performance during braking-in-turn maneuver.

Adaptive Multilevel Collaborative Passenger Flow Control in Peak Hours for a Subway Line

Due to contradiction of large-scale passenger demand and limited transportation capacity, the passengers who cannot be transported away in time accumulate and congest in stations. To ensure travel safety, improve travel efficiency, and ameliorate waiting environments for passengers, this paper proposes an adaptive multilevel collaborative passenger flow control strategy integrating the control of station entrance and station hall. An integer linear programming model is constructed, which aims at minimizing the total passenger waiting time and taking the safe capacity of each key area of all stations as the necessary constraints. The model is applied in two scenarios with different scales of passenger demand in the morning peak of the Batong line. The results show that the proposed model can adaptively activate the appropriate control level, limit the amount of accumulated passengers in each key area of the station within its safe capacity, and shorten the total passenger waiting time.

Flexible operation of supercritical coal-fired power plant integrated with solvent-based CO2 capture through collaborative predictive control

This paper presents a controller design study for the supercritical coal fired power plant (CFPP) integrated with solvent-based post-combustion CO 2 capture (PCC) system. The focus of the study is on the steam drawn-off from turbine to the re-boiler, which is the key interaction between the CFPP and PCC plants. The simulation study of a 660 MW supercritical CFPP-PCC unit model has shown that the impact of re-boiler steam change on the power generation of CFPP is more than 100 times faster than that on the PCC operation. Considering this finding, a collaborative predictive control strategy is proposed for the CFPP-PCC system where the re-boiler steam flowrate is manipulated for the CFPP load ramping and then gradually set to the required value for CO 2 capture. The PCC is thereby exploited as an energy storage device, which can quickly store/release extra energy for the CFPP in addition to the primary function of carbon emission reduction. The simulation results show that the proposed collaborative predictive controller can effectively improve the load ramping performance of CFPP without much performance degradation on the PCC operation. • Collaborative predictive control strategy for the integrated CFPP-PCC system. • PCC exploited as auxiliary measure for power adjustment in addition to CO 2 capture. • Impact of re-boiler steam change on CFPP is 100 times faster than that on the PCC. • Dynamic control operating simulation of the supercritical CFPP-PCC process. • Performance of different CFPP-PCC control schemes is assessed.

An ensemble deep learning approach for driver lane change intention inference

With the rapid development of intelligent vehicles, drivers are increasingly likely to share their control authorities with the intelligent control unit. For building an efficient Advanced Driver Assistance Systems (ADAS) and shared-control systems, the vehicle needs to understand the drivers’ intent and their activities to generate assistant and collaborative control strategies. In this study, a driver intention inference system that focuses on the highway lane change maneuvers is proposed. First, a high-level driver intention mechanism and framework are introduced. Then, a vision-based intention inference system is proposed, which captures the multi-modal signals based on multiple low-cost cameras and the VBOX vehicle data acquisition system. A novel ensemble bi-directional recurrent neural network (RNN) model with Long Short-Term Memory (LSTM) units is proposed to deal with the time-series driving sequence and the temporal behavioral patterns. Naturalistic highway driving data that consists of lane-keeping, left and right lane change maneuvers are collected and used for model construction and evaluation. Furthermore, the driver's pre-maneuver activities are statistically analyzed. It is found that for situation-aware, drivers usually check the mirrors for more than six seconds before they initiate the lane change maneuver, and the time interval between steering the handwheel and crossing the lane is about 2 s on average. Finally, hypothesis testing is conducted to show the significant improvement of the proposed algorithm over existing ones. With five-fold cross-validation, the EBiLSTM model achieves an average accuracy of 96.1% for the intention that is inferred 0.5 s before the maneuver starts.

Collaborative Control and Allocation Method of RSC and GSC for DFIG System to Suppress High-Frequency Resonance and Harmonics

When doubly fed induction generator (DFIG) system connects to the parallel compensation grid with harmonics, high-frequency resonances (HFR) occur due to the insufficient phase margin, meanwhile the output power quality of the DFIG system is degraded due to the grid harmonics. For solving the two problems simultaneously, the collaborative control strategy which allots different targets to rotor side converter (RSC) and grid side converter (GSC) is proposed in this article, in which the HFR damping controller is planted at RSC or GSC, while the other converter will be responsible for improving the power quality of the DFIG system. The core of the collaborative control strategy is how to select RSC or GSC to achieve HFR damping, which is investigated by analyzing the impedance characteristic of the DFIG system after planting the HFR damping controller. With the collaborative control strategy, the excellent performance of HFR damping and harmonics suppression can be guaranteed for the DFIG system under grid harmonics, meanwhile the DFIG system can operate flexibly with different targets, i.e., sinusoidal output current, smooth output power. Finally, theoretical analysis and experimental results for different DFIG systems verify the correctness and availability of the proposed collaborative control strategy.

High-Precision Coordinated Position Control of Integrated Permanent Magnet Synchronous Linear Motor Stations

In multi-station linear reciprocating processing systems, the positioning accuracy and the cooperative precision of the motion stations directly affect the production quality of products. This paper aims to enhance both the positioning and the coordination accuracy of the motion stations from the integrated permanent magnet synchronous linear motor (IPMSLM) stations during the reciprocating motions, especially in the presence of load disturbance. Firstly, the adaptive control strategy based on pole placement principle with online parameter identification is proposed for single IPMSLM station. The least squares identification algorithm with forgetting factor is employed to calculate the mathematic model online, so as to solve the problem that the IPMSLM stations cannot be accurately modeled with the existence of parameter perturbation and load disturbance. Secondly, in order to improve the coordination performance of the IPMSLM stations, the cooperative error equation is proposed, and a ring coupling cooperative (RCC) control strategy based on Lyapunov stability theorem is designed. The accuracy and effectiveness of this proposed collaborative control strategy is verified by experiments under no-load and spring-load conditions. Experimental results show that for the proposed adaptive and coordinate control strategy, the positioning error and the coordination error during the reciprocating motion can be guaranteed uniformly within $\pm 8~\mu \text{m}$ and $\pm 10~\mu \text{m}$ , respectively.

Control Strategy of Vehicle Anti-Rollover Considering Driver’s Characteristic

In the process of vehicle anti-rollover, the driver’s behavioral characteristics have a great influence on the vehicle rollover stability. However, the existing researches on anti-rollover rarely consider the driver in the human-vehicle-road closed loop system, which may directly affects the rollover dynamics characteristics of the vehicle. Aiming at this problem, this work analyzes the influence of different driver parameters on vehicle dynamics and establishes a driver model considering the vehicle rollover stability. Based on this, a collaborative control strategy of vehicle anti-rollover considering the driver’s characteristics is designed to assist the driving stability requirements of different types of drivers. It includes the upper layer of supervision decision control and the lower layer of cooperative execution control. The supervisory decision control layer sets different rollover constraint boundaries for different types of drivers, and makes six control decision modes combining driver input and vehicle path tracking error. Aiming at different types of drivers, the lower cooperative execution control layer constrains the front wheel angle by the active steering system to reduce yaw rate and the risk of rollover. Moreover, it reasonably distributes the braking force of four tires by the active braking system to reduce the path tracking error while preventing rollover. The simulation results show that, the proposed cooperative control strategy can provide different control effects for different drivers, and ensure the effective anti-rollover control of the vehicle and achieve good path tracking performance.

Collaborative control of thermostatically controlled appliances for balancing renewable generation in smart grid

As more and more renewable generation resources are integrated into the smart grid, they are likely to cause supply–demand imbalance such as overgeneration due to their uncertain and variable nature, resulting in poor power quality and unreliable power delivery. To balance overgeneration, this paper presents a collaborative control strategy of thermostatically controlled appliances in the timescale of minutes. The proposed strategy includes four parts: state awareness, real‐time analysis, scientific decision‐making, and precise execution. A responsive task‐type model is designed to identify the executable task type of an appliance in the state awareness part. A multiobjective 0–1 programming method is used to solve the optimal control combination in the scientific decision‐making part. And a thermostat set‐point control rule is used to generate the control signals of appliances in the precise execution part. In order to illustrate the advantage of the proposed strategy, four case studies are designed, and the advantage of the strategy is validated by the simulation results of the case studies. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Investigation on two-stage vibration suppression and precision pointing for space optical payloads

For space optical payloads, it is difficult to achieve vibration suppression and high precision pointing due to broadband range of micro-vibration. This paper thus presents one two-stage vibration suppression and precision pointing platform, which synthesizes the integrated vibration isolation of the transmission path and the payload end. The proposed two-stage platform consists of one piezoelectric-based Stewart platform and a fast steering mirror (FSM) subsystem. Based on the two-stage platform, the collaborative control strategy is presented. The Stewart platform is used to suppress the high-frequency structural vibrations by employing the least mean square (LMS) acceleration feedback algorithm. The FSM is applied to compensate the low-frequency jitters of optical axis and achieve high precision pointing control according to CCD measure information. In addition, the Stewart platform can offload the FSM and increase the pointing range of the system. Finally, the experimental prototype is developed and the experiments are implemented. The experimental results demonstrate the feasibility of the collaborative control scheme and further validate the proposed two-stage vibration suppression and precision pointing platform.

An Attribute-Based Controlled Collaborative Access Control Scheme for Public Cloud Storage

In public cloud storage services, data are outsourced to semi-trusted cloud servers which are outside of data owners' trusted domain. To prevent untrustworthy service providers from accessing data owners' sensitive data, outsourced data are often encrypted. In this scenario, conducting access control over these data becomes a challenging issue. Attribute-based encryption (ABE) has been proved to be a powerful cryptographic tool to express access policies over attributes, which can provide a fine-grained, flexible, and secure access control over outsourced data. However, the existing ABE-based access control schemes do not support users to gain access permission by collaboration. In this paper, we explore a special attribute-based access control scenario where multiple users having different attribute sets can collaborate to gain access permission if the data owner allows their collaboration in the access policy. Meanwhile, the collaboration that is not designated in the access policy should be regarded as a collusion and the access request will be denied. We propose an attribute-based controlled collaborative access control scheme through designating translation nodes in the access structure. Security analysis shows that our proposed scheme can guarantee data confidentiality and has many other critical security properties. Extensive performance analysis shows that our proposed scheme is efficient in terms of storage and computation overhead.

A discrete-event simulation model to test multimodal strategies for a greener and more resilient wood supply

Increasing occurrences of natural disturbances, including windstorms and high snow cover, and supply chain risks lead to severe irregularities in wood harvest and transport. To overcome resulting supply difficulties, innovative multimodal systems via rail terminals are promising options offering the potential to increase buffer capacity, improve supply chain resilience, and reduce greenhouse gas emissions. Therefore, a train terminal is included in a virtual simulation environment spanning the entire wood supply chain from forest to industry to test, analyze, and evaluate a complex multimodal system in different scenario settings. Furthermore, the simulation model provides intuitive decision support through animation and a cockpit of key performance indicators, facilitating hands-on workshops with supply chain managers. Results show the advantage of a combination of unimodal and multimodal transport in the wood supply chain of the observed case-study region. This combination proves to be resilient and outperforms other tested supply chain strategies by avoiding both bottlenecks and ill-timed plans and reducing carbon dioxide (CO2) emissions. Furthermore, workshops conducted with industry experts indicate that adapting collaborative supply chain control strategies by means of a participatory simulation environment enhances the development of advanced risk management and therefore improves supply chain resilience, efficiency, and sustainability.

Efficient Coupling Diffusion of Positive and Negative Information in Online Social Networks

The increasing popularization of large-scale online social networks (OSNs) facilitates information sharing. Users are able to diffuse positive and negative information independently owing to the high openness of the OSNs. Due to the intervention of user’s emotions and social relationships, the positive and negative information diffusion exhibits a complicated dynamic coupling diffusion process, in which the negative information diffusion can cause social panic and confusion. However, prior works mainly focus on the diffusion of single type of information. To fill this gap, this paper aims to investigate the dynamic diffusion process of the positive and negative information and control the negative information diffusion timely. Specifically, we first establish a coupling diffusion model to characterize the dynamic diffusion process under the coexistence of positive and negative information, then derive the critical condition for the negative information diffusion and certify the stability of the diffusion model. Furthermore, we propose two collaborative control strategies to persuade and guide users to diffuse the positive information simultaneously. Then, the issue of minimizing the total system costs is transformed to an optimal control problem. Finally, we prove the existence and uniqueness of optimal control solutions and obtain the dynamic distribution of optimal control strategies over time to minimize system costs. The experimental results obtained from two real-world datasets verify the effectiveness of our model and the high efficiency of the collaborative control strategies.

Evolution of the vertical structure of air pollutants during winter heavy pollution episodes: The role of regional transport and potential sources

Knowledge of vertical distribution of air pollutants is important for understanding the mechanisms underlying haze pollution. In this paper, we characterize the vertical structure of air pollutants based on ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements in the wintertime in Hefei, East China. Both NO2 and aerosol show exponentially decreasing profile with high concentrations concentrated close to the surface, suggesting the major emissions are at ground level. SO2 profiles extend to slightly higher altitudes than NO2, which might be due to the SO2 emissions from elevated point sources (e.g., power plants). Elevated HCHO was observed in the middle layer during noon time, probably indicating that the larger effect of the photochemical formation of atmospheric HCHO rather than direct HCHO emissions. Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) models were used to evaluate the potential sources of Hefei's air pollution in the lower, middle and upper boundary layer. The results revealed that the major regional source of NO2 and aerosol were located in the northern and eastern part of Anhui and the transport mainly occured in the lower layer. Transported SO2 from the northwestern regions in the middle layer made great contribution to SO2 in Hefei. HCHO was mostly produced locally while the transport of HCHO from potential source areas was negligible. To further explore the potential source contributions affecting haze pollution, PSCF and CWT analysis for heavy haze episodes suggested that the potential source areas for aerosol were almost located in the northern region, the northwestern region, and the YRD region. Our findings provide a more comprehensive understanding for regional transport through the study on potential sources at different altitudes, which are useful for designing collaborative air pollution control strategies on a regional scale.

Improved Human–Robot Collaborative Control of Redundant Robot for Teleoperated Minimally Invasive Surgery

An improved human–robot collaborative control scheme is proposed in a teleoperated minimally invasive surgery scenario, based on a hierarchical operational space formulation of a seven-degree-of-freedom redundant robot. Redundancy is exploited to guarantee a remote center of motion (RCM) constraint and to provide a compliant behavior for the medical staff. Based on the implemented hierarchical control framework, an RCM constraint and a safe constraint are applied to the null-space motion to achieve the surgical tasks with human–robot interaction. Due to the physical interactions, safety and accuracy of the surgery may be affected. The control framework integrates an adaptive compensator to enhance the accuracy of the surgical tip and to maintain the RCM constraint in a decoupled way avoiding any physical interactions. The system performance is verified on a patient phantom. Compared with the methods proposed in the literature, results show that the accuracy of both the RCM constraint and the surgical tip is improved. The compliant swivel motion of the robot arm is also constrained in a defined area, and the interaction force on the abdominal wall becomes smaller.

Feedforward design for output synchronization of nonlinear heterogeneous systems with output communication

Output synchronization of nonlinear heterogeneous networked systems has been well investigated within a two-step framework. A reference model is designed for each agent and they achieve consensus through a proper collaborative control strategy. Then, a local regulation controller is constructed to drive each agent’s output to the desired trajectory generated by its reference model. The local regulation controller was studied mainly based on the internal model principle. As the two steps occur concurrently, a small gain condition must be satisfied between them when the setting is on output communication that allows only agent outputs to be transmitted over network. In this paper, we consider a novel feedforward design that does not rely on internal model but a reference model of a sufficiently high relative degree. With such a design, local regulation can be separately achieved and hence the small gain condition is no longer required, which much simplifies the design when agent dynamics do not contain uncertainties.

A Master-Slave Collaborative Control Strategy of High Voltage DC Circuit Breakers

High voltage DC circuit breaker is the key equipment in DC grid since its rapid and reliable breaking is a critical precondition for safe and reliable operation of system. In the paper, breaking of hybrid high voltage DC circuit breaker is analyzed first. Then to solve the problem about response time and system reliability, a master-slave collaborative control strategy of high voltage DC circuit breaker is proposed, it regards the grid-level protection as master control, and the inner protection is slave control. Making full use of the advantages of both two controls, the breaking is rapid and reliable. What’s more, DC circuit breaker could still cut off the fault current in case of malfunction of one of these two protection. Finally, the simulation results in PSCAD/EMTDC show that the proposed control strategy is correct and effective, it shortens the fault response time and improves system reliability.

A Collaborative Fuzzy Control Strategy of Aggregated Regenerative Electric Heaters for Real-Time Energy Balance in Micro-Grid

As more and more renewable generation resources such as photovoltaic and wind-power are connected to the micro-grid in smart grid, it is prone to bring challenges to balance supply and demand for the uncertain and variable nature of renewable generation resources. In this paper, we propose a collaborative fuzzy control strategy (CFCS) of aggregated regenerative electric heaters (REHs) to balance renewable energy generation and maintain the real-time balance between supply and demand. First, we describe the system architecture of aggregated REHs for accommodating renewable energy. Then, we establish the information control model of a single REH. Next, based on the system architecture and the model, a collaborative control strategy, including state awareness, real-time analysis, scientific decision-making, and precise execution, is presented to realize the optimal control of the aggregated REHs for accommodating renewable energy. Finally, the simulation results show that the proposed strategy not only can meet the demand for accommodating renewable energy generation effectively but also ensure residential comfort and the heat-storage percent of REHs.