Data Driven Distributed Bipartite Consensus Tracking for Nonlinear Multiagent Systems via Iterative Learning Control

This article explores a data-driven distributed bipartite consensus tracking (DBCT) problem for discrete-time multi-agent systems (MASs) with coopetition networks under repeatable operations. To solve this problem, a time-varying linearization model along the iteration axis is first established by using the measurement input and output (I/O) data of agents. Then a data-driven distributed bipartite consensus iterative learning control (DBCILC) algorithm is proposed considering both fixed and switching topologies. Compared with existing bipartite consensus, the main characteristic is to construct the proposed control protocol without requiring any explicit or implicit information of MASs’ mathematical model. The difference from existing iterative learning control (ILC) approaches is that both the cooperative interactions and antagonistic interactions, and time-varying switching topologies are considered. Furthermore, through rigorous theoretical analysis, the proposed DBCILC approach can guarantee the bipartite consensus reducing tracking errors in the limited iteration steps. Moreover, although not all agents can receive information from the virtual leader directly, the proposed distributed scheme can maintain the performance and reduce the costs of communication. The results of three examples further illustrate the correctness, effectiveness, and applicability of the proposed algorithm

High-Order Leader-Follower Tracking Control under Limited Information Availability

Limited information availability represents a fundamental challenge for control of multi-agent systems, since an agent often lacks sensing capabilities to measure certain states of its own and can exchange data only with its neighbors. The challenge becomes even greater when agents are governed by high-order dynamics. The present work is motivated to conduct control design for linear and nonlinear high-order leader-follower multi-agent systems in a context where only the first state of an agent is measured. To address this open challenge, we develop novel distributed observers to enable followers to reconstruct unmeasured or unknown quantities about themselves and the leader and on such a basis, build observer-based tracking control approaches. We analyze the convergence properties of the proposed approaches and validate their performance through simulation

Distributed Model-Free Bipartite Consensus Tracking for Unknown Heterogeneous Multi-Agent Systems with Switching Topology

This paper proposes a distributed model-free adaptive bipartite consensus tracking (DMFABCT) scheme. The proposed scheme is independent of a precise mathematical model, but can achieve both bipartite time-invariant and time-varying trajectory tracking for unknown dynamic discrete-time heterogeneous multi-agent systems (MASs) with switching topology and coopetition networks. The main innovation of this algorithm is to estimate an equivalent dynamic linearization data model by the pseudo partial derivative (PPD) approach, where only the input–output (I/O) data of each agent is required, and the cooperative interactions among agents are investigated. The rigorous proof of the convergent property is given for DMFABCT, which reveals that the trajectories error can be reduced. Finally, three simulations results show that the novel DMFABCT scheme is effective and robust for unknown heterogeneous discrete-time MASs with switching topologies to complete bipartite consensus tracking tasks

Bipartite consensus of nonlinear agents in the presence of communication noise

In this paper, a Distributed Nonlinear Dynamic Inversion (DNDI)-based consensus protocol is designed to achieve the bipartite consensus of nonlinear agents over a signed graph. DNDI inherits the advantage of nonlinear dynamic inversion theory, and the application to the bipartite problem is a new idea. Moreover, communication noise is considered to make the scenario more realistic. The convergence study provides a solid theoretical base, and a realistic simulation study shows the effectiveness of the proposed protocol.Engineering and Physical Sciences Research Council (EPSRC): EP/R009953/

Distributed Tracking Control Design for Leader-Follower Multi-Agent Systems

Multi-agent systems (MASs) have been widely recognized as a key way to model, analyze, and engineer numerous kinds of complex systems composed of distributed agents. The aim of this dissertation is to study control design for leader-follower MASs such that a group of followers can track a specified leader via distributed decision making based on distributed information. We identify and consider several critical problems that have stood in the way of distributed tracking control synthesis and analysis. Specifically, they include: 1) limited information access by the followers to the leader, 2) effects of external disturbances, 3) complicated dynamics of agents, and 4) energy efficiency. To overcome the first three problems, we take a lead with the design of distributed-observer-based control, with the insight that distributed observers can enable agents to recover unknown quantities in a collective manner for the purpose of control. To deal with the fourth problem, we propose the first study of MAS tracking control conscious of nonlinear battery dynamics to increase operation time and range. The dissertation will present the following research contributions. First, we propose the notion of designing distributed observers to make all the followers aware of the leader's state and driving input, regardless of the network communication topology, and perform tracking controller design based on the observers. Second, we further develop distributed disturbance observers and observer-based robust tracking control to handle the scenario when all the leader and followers are affected by unknown disturbances only bounded in rates of change. The third contribution lies in treating a leader-follower MAS with high-order, nonlinear dynamics. Assuming the availability of very limited measurement data, we substantively expand the idea of observer-based control to develop a catalog of distributed observers such that the followers can reconstruct large amounts of information necessary for effective tracking control. Finally, we propose a distributed predictive optimization method to integrate onboard battery management with tracking control for long-endurance operation of an electric-powered MAS. The proposed dissertation research offers new insights and a set of novel tools to enhance the control performance of leader-follower MASs. The results also have a promise to find potential applications in other types of MASs

Ostinato Process Model for Visual Network Analytics: Experiments in Innovation Ecosystems

More often than ever before, innovation activities are crossing organizational boundaries and taking place in the spaces between formal, organizational structures. This new context for innovation activities is increasingly referred to as an innovation ecosystem. Open innovation, co-creation, user-driven innovation, API and platform economies, and business ecosystems are key drivers of the transformation. Innovation ecosystems are open, dynamic systems that cross geographical as well as organizational boundaries and include financial, technological, and political dimensions. Talented humans have a crucial driving role in ecosystemic innovation activities. Innovation ecosystems set a new framework for analyzing, investigating, and therefore measuring innovation.Measuring and visualizing innovation is difficult, particularly within innovation ecosystems where activities take very complex forms and even identifying all relevant actors and stakeholders is challenging. At the same time, ecosystem-level analyses of innovation ecosystem structures are imperative for three groups: innovation ecosystem scholars, policy and decision makers, and innovation ecosystem actors. Moreover, new sources of digital data on innovation activities have become available, introducing new opportunities to investigate innovation ecosystems at the ecosystem level.In this dissertation, we seek to develop new means to utilize digital data in analyzing innovation ecosystems at the ecosystem level. We take an action design research approach to develop the means to investigate the structural properties of innovation ecosystems at the ecosystem level by using visual network analytics. We start from the realization that interconnectedness is a key property of innovation ecosystems. Addressing innovation ecosystems as networks, that is, as collections of pairs of interconnected innovation ecosystem actors, allows scholars and practitioners to gain insight into innovation ecosystem structures and the structural roles of individual ecosystem actors. To determine how innovation ecosystems should be modeled and analyzed as networks, we investigate several innovation ecosystems representing regional, metropolitan, national, and international contexts as well as investigating the context of programmatic activities that support innovation and growth. Our main objective in the dissertation is to develop a process model for data-driven visual network analytics of innovation ecosystems.Visual network analytics is a valuable method for investigating and mapping the innovation ecosystem structure. In the proposed approach, transactional microdata on innovation ecosystem actors and their interconnections is collected from various digital sources. Innovation ecosystem actors are represented as network nodes that are connected through transactions, including investments and acquisitions and advisory, founder, and contributor affiliations. Network metrics are used to quantify actors’ structural positions. Interactive visual analytics tools are used to support the visual exploration of the innovation ecosystem under investigation by using both top-down and bottom-up strategies.This work makes several contributions to the art and science of data-driven visual network analytics of innovation ecosystems. Most importantly, the dissertation proposes the ostinato model, an iterative, user-centric, process-automated model for data-driven visual network analytics. The ostinato model simultaneously supports the automation of the process and enables interactive and transparent exploration. The model has two phases: data collection and refinement, and network creation and analysis. The data collection and refinement phase is further divided into entity index creation, Web/API crawling, scraping, and data aggregation. The network construction and analysis phase is composed of filtering in entities, node and edge creation, metrics calculation, node and edge filtering, entity index refinement, layout processing, and visual properties configuration. The cycle of exploration and automation characterizes the model and is embedded in each phase.In addition to the ostinato model, we contribute a set of design guidelines for modeling and visualizing innovation ecosystems as networks. Finally, we contribute to the empirical body of knowledge on innovation ecosystems through a series of investigations of innovation ecosystems of different levels of abstraction and complexity. Innovation ecosystem scholars, policy makers, orchestrators, and other stakeholders in the innovation ecosystem under investigation in this dissertation have subscribed to the approach presented herein. The design guidelines, together with the ostinato model, allow innovation ecosystem investigators and actors an opportunity to significantly advance in utilizing visual network analytics in managing and orchestrating innovation ecosystems. Further research and development of supporting processes and tools are needed to take full advantage of the presented approach in analyzing, investigating, facilitating, and orchestrating interorganizational innovation activities