701,758 research outputs found
Model-Reference Adaptive Control of Distributed Lagrangian Infinite-Dimensional Systems Using Hamiltons Principle
This paper presents a Hamilton's principle for distributed control of infinite-dimensional systems modeled by a distributed form of the Euler-Lagrange method. The distributed systems are governed by a system of linear partial differential equations in space and time. A generalized potential energy expression is developed that can capture most physical systems including those systems that have no spatial distribution. The Hamilton's principle is applied to derive distributed feedback control methods without resorting to the standard weak-form discretization approach to convert an infinite-dimensional systems to a finite-dimensional systems. It can be shown by the principle of least action that the distributed control synthesized by the Hamilton's principle is a minimum-norm control. A model-reference adaptive control framework is developed for distributed Lagrangian systems in the presence of uncertainty. The theory is demonstrated by an application of adaptive flutter suppression control of a flexible aircraft wing
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Model-Matching type-methods and Stability of Networks consisting of non-Identical Dynamic Agents
Many recent approaches of distributed control over networks of dynamical agents rely on the assumption of identical agent dynamics. In this paper we propose a systematic method for removing this assumption, leading to a general approach for distributed-control stabilization of networks of non-identical dynamics. Local agents are assumed to share a minimal set of structural properties, such as input dimension, state dimension and controllability indices, which are generically satisfied for parametric families of systems. Our approach relies on the solution of certain model-matching type problems using local state-feedback and input matrix transformations which map the agent dynamics to a target system, selected to minimize the joint control effort of the local feedback-control schemes. By adapting a well-established distributed LQR control design methodology to our framework, the stabilization problem for a network of non-identical dynamical agents is solved. The applicability of our approach is illustrated via a simple UAV formation control problem
Control limitations from distributed sensing: theory and Extremely Large Telescope application
We investigate performance bounds for feedback control of distributed plants
where the controller can be centralized (i.e. it has access to measurements
from the whole plant), but sensors only measure differences between neighboring
subsystem outputs. Such "distributed sensing" can be a technological necessity
in applications where system size exceeds accuracy requirements by many orders
of magnitude. We formulate how distributed sensing generally limits feedback
performance robust to measurement noise and to model uncertainty, without
assuming any controller restrictions (among others, no "distributed control"
restriction). A major practical consequence is the necessity to cut down
integral action on some modes. We particularize the results to spatially
invariant systems and finally illustrate implications of our developments for
stabilizing the segmented primary mirror of the European Extremely Large
Telescope.Comment: submitted to Automatic
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