Michael Z. Q. Chen

Bio: Michael Z. Q. Chen is an academic researcher from University of Hong Kong. The author has contributed to research in topics: Inerter & Nonlinear system. The author has an hindex of 24, co-authored 60 publications receiving 1931 citations. Previous affiliations of Michael Z. Q. Chen include Nanjing University of Science and Technology & Nanyang Technological University.

The missing mechanical circuit element

Abstract: In 2008, two articles in Autosport revealed details of a new mechanical suspension component with the name "J-damper" which had entered Formula One Racing and which was delivering significant performance gains in handling and grip From its first mention in the 2007 Formula One "spy scandal" there was much speculation about what the J-damper actually was The Autosport articles revealed that the J-damper was in fact an "inerter" and that its origin lay in academic work on mechanical and electrical circuits at Cambridge University This article aims to provide an overview of the background and origin of the inerter, its application, and its intimate connection with the classical theory of network synthesis

Decentralized Adaptive Pinning Control for Cluster Synchronization of Complex Dynamical Networks

Abstract: In this brief, we investigate pinning control for cluster synchronization of undirected complex dynamical networks using a decentralized adaptive strategy. Unlike most existing pinning-control algorithms with or without an adaptive strategy, which require global information of the underlying network such as the eigenvalues of the coupling matrix of the whole network or a centralized adaptive control scheme, we propose a novel decentralized adaptive pinning-control scheme for cluster synchronization of undirected networks using a local adaptive strategy on both coupling strengths and feedback gains. By introducing this local adaptive strategy on each node, we show that the network can synchronize using weak coupling strengths and small feedback gains. Finally, we present some simulations to verify and illustrate the theoretical results.

Performance evaluation for inerter-based dynamic vibration absorbers

Abstract: This paper is concerned with the H∞ and H2 optimization problem for inerter-based dynamic vibration absorbers (IDVAs). The proposed IDVAs are obtained by replacing the damper in the traditional dynamic vibration absorber (TDVA) with some inerter-based mechanical networks. It is demonstrated in this paper that adding one inerter alone to the TDVA provides no benefits for the H∞ performance and negligible improvement (less than 0.32% improvement over the TDVA when the mass ratio less than 1) for the H2 performance. This implies the necessity of introducing another degree of freedom (element) together with inerter to the TDVA. Therefore, four different IDVAs are proposed by adding an inerter together with a spring to the TDVA, and significant improvement for both the H∞ and H2 performances is obtained. Numerical simulations in dimensionless form show that more than 20% and 10% improvement can be obtained for the H∞ and H2 performances, respectively. Besides, for the H∞ performance, the effective frequency band can be further widened by using inerter.

Fast Consensus Via Predictive Pinning Control

Abstract: By incorporating some predictive mechanism into a few pinning nodes, we show that convergence procedure to consensus can be substantially accelerated in networks of interconnected dynamic agents while physically maintaining the network topology. Such an acceleration stems from the compression mechanism of the eigenspectrum of the state matrix conferred by the predictive mechanism. This study provides a technical basis for the roles of some predictive mechanisms in widely-spread biological swarms, flocks, and consensus networks. From the engineering application point of view, inclusion of an efficient predictive mechanism allows for a significant increase in the convergence speed towards consensus.

A Note on Tests for Positive-Real Functions

Abstract: This technical note presents a modified test for positive-realness of scalar real-rational functions which avoids the need to test residue conditions. Necessary and sufficient conditions for the positive-realness of some classes of low-order real-rational functions are given by making use of the modified test.

Consensus Tracking of Multi-Agent Systems With Lipschitz-Type Node Dynamics and Switching Topologies

Abstract: Distributed consensus tracking is addressed in this paper for multi-agent systems with Lipschitz-type node dynamics. The main contribution of this work is solving the consensus tracking problem without the assumption that the topology among followers is strongly connected and fixed. By using tools from M-matrix theory, a class of consensus tracking protocols based only on the relative states among neighboring agents is designed. By appropriately constructing Lyapunov function, it is proved that consensus tracking in the closed-loop multi-agent systems with a fixed topology having a directed spanning tree can be achieved if the feedback gain matrix and the coupling strength are suitably selected. Furthermore, with the assumption that each possible topology contains a directed spanning tree, it is theoretically shown that consensus tracking under switching directed topologies can be achieved if the control parameters are suitably selected and the dwell time is larger than a positive threshold. The results are then extended to the case where the communication topology contains a directed spanning tree only frequently as the system evolves with time. Finally, some numerical simulations are given to verify the theoretical analysis.

Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

Abstract: Collaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users. Fortunately, with the continuous growth of online user reviews, the information available for training a recommender system is no longer limited to just numerical star ratings or user/item features. By extracting explicit user opinions about various aspects of a product from the reviews, it is possible to learn more details about what aspects a user cares, which further sheds light on the possibility to make explainable recommendations. In this work, we propose the Explicit Factor Model (EFM) to generate explainable recommendations, meanwhile keep a high prediction accuracy. We first extract explicit product features (i.e. aspects) and user opinions by phrase-level sentiment analysis on user reviews, then generate both recommendations and disrecommendations according to the specific product features to the user's interests and the hidden features learned. Besides, intuitional feature-level explanations about why an item is or is not recommended are generated from the model. Offline experimental results on several real-world datasets demonstrate the advantages of our framework over competitive baseline algorithms on both rating prediction and top-K recommendation tasks. Online experiments show that the detailed explanations make the recommendations and disrecommendations more influential on user's purchasing behavior.

Using an inerter-based device for structural vibration suppression

Abstract: SUMMARY: This paper proposes the use of a novel type of passive vibration control system to reduce vibrations in civil engineering structures subject to base excitation. The new system is based on the inerter, a device that was initially developed for high-performance suspensions in Formula 1 racing cars. The principal advantage of the inerter is that a high level of vibration isolation can be achieved with low amounts of added mass. This feature makes it an attractive potential alternative to traditional tuned mass dampers (TMDs). In this paper, the inerter system is modelled inside a multi-storey building and is located on braces between adjacent storeys. Numerical results show that an excellent level of vibration reduction is achieved, potentially offering improvement over TMDs. The inerter-based system is compared to a TMD system by using a range of base excitation inputs, including an earthquake signal, to demonstrate how the performance could potentially be improved by using an inerter instead of a TMD. © 2013 John Wiley & Sons, Ltd.

Cooperation prevails when individuals adjust their social ties

Abstract: Conventional evolutionary game theory predicts that natural selection favours the selfish and strong even though cooperative interactions thrive at all levels of organization in living systems. Recent investigations demonstrated that a limiting factor for the evolution of cooperative interactions is the way in which they are organized, cooperators becoming evolutionarily competitive whenever individuals are constrained to interact with few others along the edges of networks with low average connectivity. Despite this insight, the conundrum of cooperation remains since recent empirical data shows that real networks exhibit typically high average connectivity and associated single-to-broad–scale heterogeneity. Here, a computational model is constructed in which individuals are able to self-organize both their strategy and their social ties throughout evolution, based exclusively on their self-interest. We show that the entangled evolution of individual strategy and network structure constitutes a key mechanism for the sustainability of cooperation in social networks. For a given average connectivity of the population, there is a critical value for the ratio W between the time scales associated with the evolution of strategy and of structure above which cooperators wipe out defectors. Moreover, the emerging social networks exhibit an overall heterogeneity that accounts very well for the diversity of patterns recently found in acquired data on social networks. Finally, heterogeneity is found to become maximal when W reaches its critical value. These results show that simple topological dynamics reflecting the individual capacity for self-organization of social ties can produce realistic networks of high average connectivity with associated single-to-broad–scale heterogeneity. On the other hand, they show that cooperation cannot evolve as a result of “social viscosity” alone in heterogeneous networks with high average connectivity, requiring the additional mechanism of topological co-evolution to ensure the survival of cooperative behaviour.

Semi-Global Leader-Following Consensus of Linear Multi-Agent Systems With Input Saturation via Low Gain Feedback

Abstract: This paper investigates the problem of leader-following consensus of a linear multi-agent system on a switching network. The input of each agent is subject to saturation. Low gain feedback based distributed consensus protocols are developed. It is established that, under the assumptions that each agent is asymptotically null controllable with bounded controls and that the network is connected or jointly connected, semi-global leader-following consensus of the multi-agent system can be achieved. Numerical examples are presented to illustrate this result.