Hierarchical distributed receding horizon control for a group of agents

© 2015 Technical Committee on Control Theory, Chinese Association of Automation.This paper is concerned with a hierarchical distributed receding horizon control (HDRHC) approach, by which a global objective of locating the peaks of an unknown environment of interest can be achieved among locally communicating agents. The proposed HDRHC approach is executed by each agent independently and consists of two levels. In the first level, a radial basis function network is used to model the unknown environment of interest. On the basis of the established environment model, a dynamical optimization problem is formulated and solved by using a receding horizon control approach such that an ideal movement trajectory for each agent is generated. The agents can trace the peaks of the environment of interest by moving along the ideal movement trajectory; however, the collision among agents may occur. In the second level, a cooperative control optimization problem, whose aim is to avoid collision among agents, is designed. Hence, the real movement trajectory of each agent, which is produced by using the receding horizon control approach, not only should minimize the cooperative control optimization problem, but also should be close to the ideal movement trajectory. Finally, the effectiveness of the proposed HDRHC approach is illustrated for the gradient climbing problem

A finite-time particle swarm optimization algorithm for odor source localization

This paper is concerned with a finite-time particle swarm optimization algorithm for odor source localization. First, a continuous-time finite-time particle swarm optimization (FPSO) algorithm is developed based on the continuous-time model of the particle swarm optimization (PSO) algorithm. Since the introduction of a nonlinear damping item, the proposed continuous-time FPSO algorithm can converge over a finite-time interval. Furthermore, in order to enhance its exploration capability, a tuning parameter is introduced into the proposed continuous-time FPSO algorithm. The algorithm’s finite-time convergence is analyzed by using the Lyapunov approach. Second, the discrete-time FPSO algorithm is obtained by using a given dicretization scheme. The corresponding convergence conditionis derived by using a linear matrix inequality (LMI) approach. Finally, the features and performance of the proposed FPSO algorithm are illustrated by using two ill-posed functions and twenty-five benchmark functions, respectively. In numerical simulation results, the problem of odor source localization is presented to validate the effectiveness of the proposed FPSO algorithm

Determination of government guarantee and revenue cap in public–private partnership contracts

PurposeConsidering there is a lack of research in determining the optimal levels of government guarantee and revenue cap, the objective of this research is to determine their optimal levels to achieve a reasonable financial risk allocation between governments and private investors while avoiding overly lucrative conditions for private investors.Design/methodology/approachExpanded net present value (NPV) analysis and bargaining game theory are employed to construct the core of the determination process. The risk gap between governments and private investors is assessed via an expanded NPV analysis to see if the financial risk has been shared reasonably, based on which the range of the government guarantee is decided. A bargaining model is then created to help locate the optimal level of the government guarantee. Finally, a revenue cap, often combined with the government guarantee in public–private partnership (PPP) agreements, will be determined if overly lucrative conditions for private investors are observed or governments suffer a risk spillover.FindingsReferring to a real PPP project in Australia, Project BA is created to validate the applicability of the proposed determination process. The outcome shows that the proposed determination process in this paper is capable of determining the optimal levels of government guarantee and revenue cap. The government preferences towards risk allocation will influence the values of the optimal levels. Governments may also consider to alleviate the control over investors' net profits to mobilise private investors into PPP projects.Research limitations/implicationsThere is a potential possibility that the revenue cap fails to control the financial risk for governments or the overly lucrative condition for private investors. In other words, even though the revenue cap is set at the minimal level, the financial risk for governments still beyond their tolerance range or the overly lucrative condition for private investors still occurs. Future research may focus on other financial protective schemes which help to better control the financial risks for governments and profits for private investors.Originality/valueGovernment guarantees are frequently used as an investment incentive to reduce the probabilities of suffering loss for private investors. Nevertheless, the financial risks for governments may increase after providing guarantees and, as a result, revenue cap is required by governments to avoid placing themselves in an unprotected situation. By recognising the importance of the two contractual parameters, many scholars dig into their option values. However, there are very rare research works focussing on the method of determining the specific levels of government guarantee and revenue cap. To overcome the limitations of existing models and enrich the methodology for government guarantee and revenue cap determination, this paper contributes to the body of knowledge by developing a government guarantee and revenue cap determination process which contributes to a reasonable allocation of financial risks between governments and private investors

A finite-time motion control strategy for odor source localization

This paper deals with the problem of odor source localization by designing and analyzing a finite-time motion control strategy (FTMCS), which consists of a finite-time parallel motion control algorithm and a finite-time circular motion control algorithm. Specifically, a motion control architecture is first given and includes two important modules: 1) a coordinating control module; and 2) a tracking control module. In the coordinating control module, robots communicate with each other to coordinate their virtual position and virtual velocity such that the virtual velocity consensus and the accurate virtual shape decided by the potential function can be reached within a finite-time interval. In the tracking control module, a finite-time tracking control algorithm is implemented such that the real velocity and the real position of the robot can track the virtual velocity and the virtual position within a finite-time interval. Based on the proposed motion control architecture, a finite-time parallel motion control algorithm that can control a group of robots to trace a plume, is derived. Moreover, a finite-time circular motion control algorithm that can enable the robot group to search for odor clues is also designed. Finally, simulations are worked out to illustrate the effectiveness of the FTMCS for odor source localization

Computation of delay bound for linear neutral systems with interval time-varying discrete delay

This paper is concerned with the stability for a class of uncertain linear neutral systems. The uncertainty under consideration is of norm-bounded type. The discrete delay is assumed to be a time-varying continuous function belonging to a given interval, which means that the lower and upper bounds for the time-varying discrete delay are available, and no restriction on the derivative of the time-varying discrete delay is needed, which allows the discrete delay to be a fast time-varying function. Based on an integral inequality, which is introduced in this paper, and Lyapunov-Krasovskii functional approach, some stability criteria, which are formulated in the form of linear matrix inequalities (LMIs), are derived without using model transformation and bounding techniques on the related cross product terms. By the obtained criteria one can compute the allowed delay bound to guarantee the asymptotic stability of the considered systems. A numerical example is given to demonstrate effectiveness of the proposed criteria

POS-BERT: Point cloud one-stage BERT pre-training[Formula presented]

Recently, the pre-training paradigm combining Transformer and masked language modeling in BERT has achieved tremendous success not only in NLP, but also in images and point clouds. However, directly extending BERT from NLP to point clouds requires first training a discrete Variational AutoEncoder (dVAE) as the tokenizer, which results in a complex two-stage process, as in Point-BERT. Inspired by BERT and MoCo, we propose POS-BERT, a one-stage BERT pre-training method for point clouds. Specifically, we use the masked patch modeling (MPM) task to perform point cloud pre-training, which aims to recover masked patch information under the supervision of a tokenizer's output. Unlike Point-BERT, whose tokenizer is extra-trained and frozen, we propose a momentum tokenizer which is dynamically updated during training the Transformer. Furthermore, in order to better learn high-level semantic representation, we integrate contrastive learning into the proposed framework to maximize the class token consistency between augmented point cloud pairs. Experiments show that POS-BERT achieves the state-of-the-art performance on linear SVM classification of ModelNet40 with fixed feature extractors, and it exceeds Point-BERT by 3.5%. In addition, POS-BERT has significantly improved many downstream tasks, including fine-tuned classification, few-shot classification and part segmentation. The code and trained models will be released on https://github.com/fukexue/POS-BERT.git

Experimental and numerical study on temperature control performance of phase change material heat sink

With the increasing power consumption of electronic products, the rising temperature leads to the deterioration of working environment and the reduction of service life. In order to control the temperature rise of electronic products, in this paper, the heat sinks with phase change material (PCM) and honeycomb metal were designed. And their performance was compared with that of the original heat sink (without PCM and honeycomb metal) then studied under various working conditions. Paraffin (n-eicosane) and prepared low melting point alloy were filled into the heat sink with honeycomb metal. The temperature control performance of heat sink was tested experimentally under different heating power. A two-dimensional heat transfer mathematical model of honeycomb metal PCM heat sink was established and solved numerically. The numerical results were compared with experimental data to verify the mathematical model. The results demonstrate that PCM heat sinks effectively controls the temperature of electronic devices and the low melting point alloy makes heat sink better performance than paraffin. Honeycomb metal further decreases the temperature of the paraffin heat sink by up to 3.6 °C, and extends the effective working time of the alloy heat sink by 14 %. The performance of the low melting point honeycomb metal heat sink is superior to that of the paraffin honeycomb metal heat sink at high heating power, and is close to the latter at low heating power. The effective working time of PCM honeycomb metal heat sink is significantly extended when the PCM melting point is close to the limit temperature, especially at high heating power and for the PCM with low thermal conductivity. This study provides valuable reference for temperature control design of electronic products.</p

A novel premixing strategy for highly sensitive detection of nitrite on paper-based analytical devices

A novel premixing strategy for highly sensitive detection of nitrite on paper-based analytical device

Spatio-temporal variation of evapotranspiration and its linkage with environmental factors in the largest freshwater lake wetland in China

Study region Poyang lake wetland Study focus Evapotranspiration (ET) is a critical parameter in the hydrologic and energy budget, and it is important for understanding spatio-temporal variations in ET in large lake wetlands. Monthly ET in the Poyang Lake wetland during 2008–2017 was estimated by the remote sensing ET retrieval model based on a Nonparametric approach (RS-NP). And the spatio-temporal variation of ET and the influencing factors were analyzed. New hydrological insights for the region The validation revealed the usefulness of the RS-NP model in a moist region and yielded a relative error of 6 % (12 %) in the wet (dry) season. Temporally, the average yearly ET was 884 mm with ET/P (precipitation) of 55 %− 65 %. The monthly ET/P ratio increased significantly from 40 %− 50 % from January to May to 90 %− 130 % from July to October. ET in June was lower by over 20 % than that in May and July with the lowest ET/P value (33 %). Spatially, relatively larger and lower ET occurred in the northern and southern parts, respectively. The main factors affecting ET variability were downwelling shortwave radiation (∼2/3 contribution) and water area (∼1/3 contribution) at the monthly scale. Precipitation and surface temperature dominantly controlled the hysteresis effects with a lag of one month and regulated LE monthly variations. This study improves our understanding of complicated water-atmosphere interactions and their linkages with environmental factors in large lake systems.</p

Manganese(I)-catalyzed asymmetric (transfer) hydrogenation of ketones: An insight into the effect of chiral PNN and NN ligands

A new type of (RC,SP)-1-(2-diphenylphosphino)ferrocenylethylamine N-substituted with a (RC)-5,6,7,8-tetrahydroquinolinyl group (LPNN-1) was successfully employed as a chiral chelating ligand in both Mn-catalyzed asymmetric transfer hydrogenation (ATH) and asymmetric hydrogenation (AH) of a broad range of ketonic substrates (39 examples), leading to high conversions and excellent enantioselectivities for their product alcohols. In particular, PNN-pincer complex Mn-1 and its NN-bidentate analogue Mn-2 have been isolated and their comparative performance as catalysts studied with Mn-1 proving more effective in both ATH and AH. Moreover, Mn-1 generally imparted higher degrees of enantiomeric excess (ee) in both hydrogenation processes which can reach up to 99% in ATH and 93% in AH for propiophenone-type substrates. DFT calculations highlight the importance of π-π interactions and steric hindrance between catalyst and substrate which manifests itself in enhancements in ee for propiophenone over acetophenone substrates. Furthermore, a possible mechanism for the Mn-catalyzed ATH has been proposed on the basis of a joint DFT and experimental investigation.</p