Maximizing Convergence Speed for Second Order Consensus in Leaderless Multi-Agent Systems

The paper deals with the consensus problem in a leaderless network of agents that have to reach a common velocity while forming a uniformly spaced string. Moreover, the final common velocity (reference velocity) is determined by the agents in a distributed and leaderless way. Then, the consensus protocol parameters are optimized for networks characterized by a communication topology described by a class of directed graphs having a directed spanning tree, in order to maximize the convergence rate and avoid oscillations. The advantages of the optimized consensus protocol are enlightened by some simulation results and comparison with a protocol proposed in the related literature. The presented protocol can be applied to coordinate agents such as mobile robots, automated guided vehicles (AGVs) and autonomous vehicles that have to move with the same velocity and a common inter-space gap.      

Blockchain verification and validation: Techniques,challenges, and research directions

As blockchain technology is gaining popularity in industry and society, solutions for Verification and Validation (V&V) of blockchain-based software applications (BC-Apps) have started gaining equal attention. To ensure that BC-Apps are properly developed before deployment, it is paramount to apply systematic V&V to verify their functional and non-functional requirements. While existing research aims at addressing the challenges of engineering BC-Apps by providing testing techniques and tools, blockchain-based software development is still an emerging research discipline, and therefore, best practices and tools for the V&V of BC-Apps are not yet sufficiently developed. In this paper, we provide a comprehensive survey on V&V solutions for BC-Apps. Specifically, using a layered approach, we synthesize V&V tools and techniques addressing different components at various layers of the BC-App stack, as well as across the whole stack. Next, we provide a discussion on the challenges associated with BC-App V&V, and summarize a set of future research directions based on the challenges and gaps identified in existing research work. Our study aims to highlight the importance of BC-App V&V and pave the way for a disciplined, testable, and verifiable BC development.     

Minimum cost consensus model for CRP-driven preference optimization analysis in large-scale group decision making using Louvain algorithm

Large-scale group decision-making problems based on social network analysis and minimum cost consensus models (MCCMs) have recently attracted considerable attention. However, few studies have combined them to form a complete decision-making system. Accordingly, we define the satisfaction index to optimize the classical MCCM by considering the effect of the group on individuals. Similarly, we define the consistency index to optimize the consensus reaching process (CRP). Regarding the evolution of the consensus network, the Louvain algorithm is used to divide the entire group into several subgroups to ensure that each subgroup is independent but has strong cohesion. By constructing the MCCM based on the satisfaction index and the optimized consensus-reaching process, the group opinions in each subgroup are ranked to obtain the final ranking of alternatives. Finally, to verify the validity of CRP and the practical value of the proposed model, we conduct consensus network evolution and decision-making analysis in the case of a negotiation between the government and polluting companies to achieve uniform pollution emissions. Sensitivity analysis is performed to demonstrate the stability of the subgroup weights. Furthermore, a comparative analysis using existing models verifies the effectiveness of the proposed model.     

Influence of expertise on semantic categorization of wine odors

Aromatic characterization is a key element of enhancing one’s knowledge of wine. While several studies have investigated the importance of wine expertise in the ability to perform odor-related sensory tasks, little attention has been paid to the influence of expertise on the semantic categorization of wine odors. To bridge this gap, this study aimed to explore the influence of a subject’s expertise on the semantic representation of wine odors by means of a free sorting task. For this purpose, 156 subjects were recruited. Their level of expertise was measured using a questionnaire and the data analysis revealed four clusters of subjects with a gradual level of expertise. Subjects also performed a sorting task on 96 odor terms. From the number and the size of odor groups formed, as well as the additive tree representation and the consensus partition between the terms for each expertise level, we observed that all subjects, regardless of their experience, had largely the same semantic categorization of wine-odor attributes, which was mainly shaped by the sources of the odorants. It appeared that level of wine expertise played a minor role in creating the semantic representation of wine odors, affecting mainly the knowledge of specialized terms.     

A dual mode distributed economic control for a fuel cell– photovoltaic-battery hybrid power generation system based on marginal cost

Proton exchange membrane fuel cell system (PEMFCS) combining with photovoltaic and battery is now considered to be a promising alternative power generation. This paper presents a dual mode distributed economic control for a fuel cell-photovoltaic-battery hybrid power generation system (HPGS), which selects free control mode in normal situation, and the cooperative control mode is adopted when control precision declines or large state of charge (SoC) differences of battery energy storage devices (BESDs) appears. The droop control based on power generation cost by combining droop coefficient and marginal cost function considering line loss together is employed to minimize system power generation cost under free control mode. The system average voltage obtained by consensus algorithm is added to compensate bus voltage and reference power based on average marginal cost function considering line loss is calculated to correct output power under cooperative control mode. The challenge of further balancing SoCs of BESDs is implemented by internal control among BESDs. Results conducted on RT-LAB simulation platform verify effectiveness of the proposed method, indicating that cooperative control mode as a complementary way of free control mode can achieve economical operation of the studied HPGS, while maintaining bus voltage in reasonable range and balancing SoC.     

Planning transport infrastructure: examining the politics of transport planning in Melbourne,Sydney and Perth

Abstract

Australian cities have observed a “consensus turn” expressed as broad public support of greater accessibility and public transport provision as revealed in metropolitan strategic plans. In contrast large-scale road projects proposed to traverse the inner-city of three major Australian cities reveals an ongoing and deep-seated attachment by some to car-based travel in Australian urban transport planning. Comparative case studies of these three road projects in Melbourne, Sydney and Perth explores the impact that an antagonistic relationship between the state and community has on the culture of transport planning. Through observational insights, policy and media analysis and interviews with community groups, we show that this antagonistic planning culture arises when there is a fracture between metropolitan strategic plan-making and project planning, and when clear channels of communication and deliberation are undermined.     

Linear uncertain extensions of the minimum cost consensus model based on uncertain distance and consensus utility

Uncertainty theory adopts the belief degree and uncertainty distribution to ensure good alignment with a decision-maker’s uncertain preferences, making the final decisions obtained from the consensus-reaching process closer to the actual decision-making scenarios. Under the constraints of the uncertain distance measure and consensus utility, this article explores the minimum-cost consensus model under various linear uncertainty distribution-based preferences. First, the uncertain distance is used to measure the deviation between individual opinions and the consensus through uncertainty distributions. A nonlinear analytical formula is derived to avoid the computational complexity of integral and piecewise function operations, thus reducing the calculation cost of the uncertain distance measure. The consensus utility function defined in this article characterizes the adjustment value and degree of aggregation of individual opinions. Three new consensus models are constructed based on the consensus utility and linear uncertainty distribution. The results show that, in complex group decision-making contexts, the uncertain consensus models are more flexible than traditional minimum-cost consensus models: compared with the high volatility of the adjusted opinions in traditional deterministic consensus models with crisp number-based preferences, the variation trends of both individual adjusted opinions and the collective opinion with a linear uncertainty distribution are much smoother and the fitting range is closer to reality. The introduction of the consensus utility not only reflects the relative changes of individual opinions, but also accounts for individual psychological changes during the opinion-adjustment process. Most importantly, it reduces the cost per unit of consensus utility, facilitates the determination of the optimal threshold for the consensus utility, and improves the efficiency of resource allocation.     

Managing consensus and weights in iterative multiple-attribute group decision making

This study put forwards a novel consensus framework to manage the consensus and weights (i.e., weights of the experts and attributes) in iterative multiple-attribute group decision making (MAGDM) problem. In this consensus framework, an optimization-based consensus model is devised to support the process of preferences-modifying, which seeks to minimize the adjustment amounts (in the sense of Manhattan distance) between the original and adjusted preferences. Then, the other two optimization-based consensus models are constructed to support the weights-updating, in which the consensus level among experts can be further improved. A numerical example is provided to show the application of the proposed consensus framework, and a detailed comparison analysis is presented to verify the effectiveness of the proposed consensus framework.     

Robust consensus control with guaranteed rate of convergence using second-order Hurwitz polynomials

This paper considers homogeneous networks of general, linear time-invariant, second-order systems. We consider linear feedback controllers and require that the directed graph associated with the network contains a spanning tree and systems are stabilisable. We show that consensus with a guaranteed rate of convergence can always be achieved using linear state feedback. To achieve this, we provide a new and simple derivation of the conditions for a second-order polynomial with complex coefficients to be Hurwitz. We apply this result to obtain necessary and sufficient conditions to achieve consensus with networks whose graph Laplacian matrix may have complex eigenvalues. Based on the conditions found, methods to compute feedback gains are proposed. We show that gains can be chosen such that consensus is achieved robustly over a variety of communication structures and system dynamics. We also consider the use of static output feedback.     

Multi-leader coordination algorithm for networks with switching topology and quantized information

This paper analyzes the stability and convergence properties of a proportional–integral protocol for coordination of a network of agents with dynamic information flow and quantized information exchange. In the setup adopted, each agent is only required to exchange its coordination state with its neighboring agents, and the desired reference rate is only available to a group of leaders. We show that the integral term of the protocol allows the agents to learn the reference rate, rather than have it available a priori, and also provides disturbance rejection capabilities. The paper addresses the case where the graph that captures the underlying network topology is not connected during some interval of time or even fails to be connected at all times.