1,328,928 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
Mathematical control of complex systems 2013
Mathematical control of complex systems have already become an ideal research area for control engineers, mathematicians, computer scientists, and biologists to understand, manage, analyze, and interpret functional information/dynamical behaviours from real-world complex dynamical systems, such as communication systems, process control, environmental systems, intelligent manufacturing systems, transportation systems, and structural systems. This special issue aims to bring together the latest/innovative knowledge and advances in mathematics for handling complex systems. Topics include, but are not limited to the following: control systems theory (behavioural systems, networked control systems, delay systems, distributed systems, infinite-dimensional systems, and positive systems); networked control (channel capacity constraints, control over communication networks, distributed filtering and control, information theory and control, and sensor networks); and stochastic systems (nonlinear filtering, nonparametric methods, particle filtering, partial identification, stochastic control, stochastic realization, system identification)
Robust Decentralized Secondary Frequency Control in Power Systems: Merits and Trade-Offs
Frequency restoration in power systems is conventionally performed by
broadcasting a centralized signal to local controllers. As a result of the
energy transition, technological advances, and the scientific interest in
distributed control and optimization methods, a plethora of distributed
frequency control strategies have been proposed recently that rely on
communication amongst local controllers.
In this paper we propose a fully decentralized leaky integral controller for
frequency restoration that is derived from a classic lag element. We study
steady-state, asymptotic optimality, nominal stability, input-to-state
stability, noise rejection, transient performance, and robustness properties of
this controller in closed loop with a nonlinear and multivariable power system
model. We demonstrate that the leaky integral controller can strike an
acceptable trade-off between performance and robustness as well as between
asymptotic disturbance rejection and transient convergence rate by tuning its
DC gain and time constant. We compare our findings to conventional
decentralized integral control and distributed-averaging-based integral control
in theory and simulations
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