Distributed Stabilization of Nonlinear Multi-Agent Systems

The study of multi-agent systems (MASs) is focused on systems in which many autonomous agents interact and operate within a limited communication environment. The general goal of the MAS research is to design interconnection control laws such that all the dynamic agents in the group are synchronized to a desired common trajectory by exchanging information with adjacent agents over certain constrained communication networks. Based on the review and modification of existing results concerning the consensus control of linear heterogeneous MASs in Moreau (2004) [21], Scardovi and Sepulchre (2009) [25], Wieland et al (2011) [30], and Alvergue et al. (2013) [1], this thesis investigates the distributed stabilization of the heterogeneous MAS, consisting of N different continuous-time nonlinear dynamic systems, under connected communication graphs. The conditions for a nonlinear dynamic agent to be feedback equivalent to a strictly passive system are derived along with the feedback law. A distributed stabilization control protocol using state feedback is then proposed under the idea of feedback connection of two passive systems. It proves to be sufficient for only one or a few agents to have access to the reference signal for the MAS to achieve stability, which lowers the communication overhead from the reference to different agents. The result can be interpreted as an extension of the stabilizing law for linear MASs introduced in [1], and considered as a fundamental preliminary for the consensus research for nonlinear MASs in the future

FILTERED-DYNAMIC-INVERSION CONTROL FOR UNKNOWN MINIMUM-PHASE SYSTEMS WITH UNKNOWN RELATIVE DEGREE

We present filtered-dynamic-inversion (FDI) control for unknown linear time-invariant systems that are multi-input multi-output and minimum phase with unknown-but-bounded relative degree. This FDI controller requires limited model information, specifically, knowledge of an upper bound on the relative degree and knowledge of the first nonzero Markov parameter. The FDI controller is a single-parameter high-parameter-stabilizing controller that is robust to uncertainty in the relative degree. We characterize the stability of the closed-loop system. We present numerical examples, where the FDI controller is implemented in feedback with mathematical and physical systems. The numerical examples demonstrate that the FDI controller for unknown relative degree is effective for stabilization, command following, and disturbance rejection. We demonstrate that for a sufficiently large parameter, the average power of the closed-loop performance is arbitrarily small

Mathematical control of complex systems

Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

<i>H</i>₂ and mixed <i>H</i>₂/<i>H</i>_∞ Stabilization and Disturbance Attenuation for Differential Linear Repetitive Processes

Repetitive processes are a distinct class of two-dimensional systems (i.e., information propagation in two independent directions) of both systems theoretic and applications interest. A systems theory for them cannot be obtained by direct extension of existing techniques from standard (termed 1-D here) or, in many cases, two-dimensional (2-D) systems theory. Here, we give new results towards the development of such a theory in H2 and mixed H2/H∞ settings. These results are for the sub-class of so-called differential linear repetitive processes and focus on the fundamental problems of stabilization and disturbance attenuation

Stabilization of Linear Systems Over Gaussian Networks

The problem of remotely stabilizing a noisy linear time invariant plant over a Gaussian relay network is addressed. The network is comprised of a sensor node, a group of relay nodes and a remote controller. The sensor and the relay nodes operate subject to an average transmit power constraint and they can cooperate to communicate the observations of the plant's state to the remote controller. The communication links between all nodes are modeled as Gaussian channels. Necessary as well as sufficient conditions for mean-square stabilization over various network topologies are derived. The sufficient conditions are in general obtained using delay-free linear policies and the necessary conditions are obtained using information theoretic tools. Different settings where linear policies are optimal, asymptotically optimal (in certain parameters of the system) and suboptimal have been identified. For the case with noisy multi-dimensional sources controlled over scalar channels, it is shown that linear time varying policies lead to minimum capacity requirements, meeting the fundamental lower bound. For the case with noiseless sources and parallel channels, non-linear policies which meet the lower bound have been identified