Michael Z. Q. Chen

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

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.

Decentralized Adaptive Pinning Control for Cluster Synchronization of Complex Dynamical Networks

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.

Semiglobal Observer-Based Leader-Following Consensus With Input Saturation

This paper studies the observer-based leader-following consensus of a linear multiagent system on switching networks, in which the input of each agent is subject to saturation. Based on a low-gain output feedback method, distributed consensus protocols are developed. Under the assumptions that the networks are connected or jointly connected and that each agent is asymptotically null controllable with bounded controls and detectable, semiglobal observer-based leader-following consensus of the multiagent system can be reached on switching networks. A numerical example is presented to illustrate the theoretical results.

Centralized Control for Parallel Operation of Distributed Generation Inverters in Microgrids

This paper presents a centralized control system that coordinates parallel operations of different distributed generation (DG) inverters within a microgrid. The control design for the DG inverters employs a new Model Predictive Control algorithm that allows faster computational time for large power systems by optimizing the steady-state and the transient control problems separately. An overall energy management system is also implemented for the microgrid to coordinate load sharing among different DG units during both grid-connected and islanded operations. The design concept of the proposed control system is evaluated through simulation studies under different test scenarios. The impact of the increased penetration of DG units on the distribution grid is also investigated using the proposed microgrid. The simulation results show that the operations of the DG units within the microgrid can be coordinated effectively under the proposed control system to ensure stable operation of the overall microgrid.

Fast Consensus Via Predictive Pinning Control

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.

Coordinated Control and Energy Management of Distributed Generation Inverters in a Microgrid

This paper presents a microgrid consisting of different distributed generation (DG) units that are connected to the distribution grid. An energy-management algorithm is implemented to coordinate the operations of the different DG units in the microgrid for grid-connected and islanded operations. The proposed microgrid consists of a photovoltaic (PV) array which functions as the primary generation unit of the microgrid and a proton-exchange membrane fuel cell to supplement the variability in the power generated by the PV array. A lithium-ion storage battery is incorporated into the microgrid to mitigate peak demands during grid-connected operation and to compensate for any shortage in the generated power during islanded operation. The control design for the DG inverters employs a new model predictive control algorithm which enables faster computational time for large power systems by optimizing the steady-state and the transient control problems separately. The design concept is verified through various test scenarios to demonstrate the operational capability of the proposed microgrid, and the obtained results are discussed.

H∞ pinning synchronization of directed networks with aperiodic sampled-data communications

This paper addresses the global H∞ pinning synchronization problem for a class of directed networks with aperiodic sampled-data communications. Important yet challenging issues of how many and which nodes should be pinned for realizing global synchronization in a fixed directed network without external disturbances are first discussed. By using a combined tool from the input-delay approach and free-weighting matrices technique, some sufficient synchronizability conditions are then derived for such networks. Furthermore, a multi-step algorithm is designed to estimate the upper bound of the maximum allowable sampling intervals for achieving synchronization. Theoretical results are then extended to global H∞ pinning synchronization in fixed and switched directed networks with external disturbances, showing that a finite H∞ performance index can be guaranteed under some suitable conditions. Finally, numerical simulations are performed to demonstrate the effectiveness of the analytical results.

A Note on Tests for Positive-Real Functions

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.

Restricted Complexity Network Realizations for Passive Mechanical Control

This paper considers a realization problem of restricted complexity arising in an approach to passive control of mechanical systems. This approach is based on synthesizing a positive-real admittance or impedance function using springs, dampers and inerters. This paper solves the following problem: what is the most general class of mechanical admittances which can be realized if the number of dampers and inerters is restricted to one in each case, while allowing an arbitrary number of springs and no transformers (levers)? The solution uses element extraction of the damper and inerter followed by the derivation of a necessary and sufficient condition for the one-element-kind (transformerless) realization of an associated three-port network. This involves the derivation of a necessary and sufficient condition for a third-order non-negative definite matrix to be reducible to a paramount matrix using a diagonal transformation. It is shown that the relevant class of mechanical admittances can be parametrized in terms of five circuit arrangements each containing four springs.

Unknown Input Observer-Based Robust Fault Estimation for Systems Corrupted by Partially Decoupled Disturbances

Robust fault estimation plays an important role in real-time monitoring, diagnosis, and fault-tolerance control. Accordingly, this paper aims to develop an effective fault estimation technique to simultaneously estimate the system states and the concerned faults, while minimizing the influences from process/sensor disturbances. Specifically, an augmented system is constructed by forming an augmented state vector composed of the system states and the concerned faults. Next, an unknown input observer (UIO) is designed for the augmented system by decoupling the partial disturbances and attenuating the disturbances that cannot be decoupled, leading to a simultaneous estimate of the system states and the concerned faults. In order to be close to the practical engineering situations, the process disturbances in this study are assumed not to be completely decoupled. In the first part of this paper, the existence condition of such an UIO is proposed to facilitate the fault estimation for linear systems subjected to process disturbances. In the second part, robust fault estimation techniques are addressed for Lipschitz nonlinear systems subjected to both process and sensor disturbances. The proposed technique is finally illustrated by the simulation studies of a three-shaft gas turbine engine and a single-link flexible joint robot.