69,520 research outputs found
Measurement and control of emergent phenomena emulated by resistive-capacitive networks, using fractionalorder internal model control and external adaptive control
A fractional-order internal model control technique is applied to a three-dimensional resistive-capacitive network to enforce desired closed loop
dynamics of first order. In order to handle model mismatch issues resulting from the random allocation of the components within the network, the control law is augmented with a model-reference adaptive strategy in an external loop. By imposing a control law on the system to obey first order dynamics, a calibrated transient response is ensured. The methodology enables feedback control of complex
systems with emergent responses and is robust in the presence of measurement noise or under conditions of poor model identification. Furthermore, it is also applicable to systems that exhibit higher order fractional dynamics. Examples of feedback-controlled transduction include cantilever positioning in atomic force microscopy or the control of complex de-excitation lifetimes encountered in
many types of spectroscopies, e.g., nuclear magnetic, electron-spin, microwave, multiphoton fluorescence, Förster resonance, etc. The proposed solution should also find important applications in more complex electronic, microwave, and photonic lock-in problems. Finally, there are further applications across the broader measurement science and instrumentation community when designing complex feedback systems at the system level, e.g., ensuring the adaptive control of distributed physiological processes through the use of biomedical implants
Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach
10.1109/TNN.2008.2003290IEEE Transactions on Neural Networks19111873-1886ITNN
Adaptive Predictive Control Using Neural Network for a Class of Pure-feedback Systems in Discrete-time
10.1109/TNN.2008.2000446IEEE Transactions on Neural Networks1991599-1614ITNN
PID control system analysis, design, and technology
Designing and tuning a proportional-integral-derivative
(PID) controller appears to be conceptually intuitive, but can
be hard in practice, if multiple (and often conflicting) objectives
such as short transient and high stability are to be achieved.
Usually, initial designs obtained by all means need to be adjusted
repeatedly through computer simulations until the closed-loop
system performs or compromises as desired. This stimulates
the development of "intelligent" tools that can assist engineers
to achieve the best overall PID control for the entire operating
envelope. This development has further led to the incorporation
of some advanced tuning algorithms into PID hardware modules.
Corresponding to these developments, this paper presents a
modern overview of functionalities and tuning methods in patents,
software packages and commercial hardware modules. It is seen
that many PID variants have been developed in order to improve
transient performance, but standardising and modularising PID
control are desired, although challenging. The inclusion of system
identification and "intelligent" techniques in software based PID
systems helps automate the entire design and tuning process to
a useful degree. This should also assist future development of
"plug-and-play" PID controllers that are widely applicable and
can be set up easily and operate optimally for enhanced productivity,
improved quality and reduced maintenance requirements
Control of Networked Multiagent Systems with Uncertain Graph Topologies
Multiagent systems consist of agents that locally exchange information
through a physical network subject to a graph topology. Current control methods
for networked multiagent systems assume the knowledge of graph topologies in
order to design distributed control laws for achieving desired global system
behaviors. However, this assumption may not be valid for situations where graph
topologies are subject to uncertainties either due to changes in the physical
network or the presence of modeling errors especially for multiagent systems
involving a large number of interacting agents. Motivating from this
standpoint, this paper studies distributed control of networked multiagent
systems with uncertain graph topologies. The proposed framework involves a
controller architecture that has an ability to adapt its feed- back gains in
response to system variations. Specifically, we analytically show that the
proposed controller drives the trajectories of a networked multiagent system
subject to a graph topology with time-varying uncertainties to a close
neighborhood of the trajectories of a given reference model having a desired
graph topology. As a special case, we also show that a networked multi-agent
system subject to a graph topology with constant uncertainties asymptotically
converges to the trajectories of a given reference model. Although the main
result of this paper is presented in the context of average consensus problem,
the proposed framework can be used for many other problems related to networked
multiagent systems with uncertain graph topologies.Comment: 14 pages, 2 figure
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