Degree of disturbance rejection capability for linear anti-stable systems

Lee, Haemin; Park, Youngjin

doi:10.1109/iccas.2014.6987977

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…(14), the measure is yielded: (17) Secondly, for the case of OJ = 1 and 02 = 0, i.e. unmatched disturbance, the measure can be calculated with same procedure and the result is yielded: (18) The two results yield that the control input energy of unmatched disturbance system is larger than that of matched disturbance system, which means that this approach maintains the physical meanings of DODR. Furthermore, this measure can be easily calculated even for marginally systems as shown in this example.…”

Section: Numerical Examplementioning

confidence: 97%

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Degree of disturbance rejection capability for linear marginally stable systems

Lee

Park

2015

2015 15th International Conference on Control, Automation and Systems (ICCAS)

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Controllability is a structural property that carries information useful for control system design such as actuator placement. There are several measures to determine the locations and types of actuators, and one of them is degree of disturbance rejection capability (DODR) which is a quantitative measure representing how controllable the system is under the presence of persistent disturbance. However, it is hard to obtain the steady-state solution of DOOR if the system is marginally stable because of several problems such as singularity and intensiveness of computation to obtain the Gramians. Thus, this problem to apply the measure to marginally system still remains. In this paper, therefore, the way to apply DOOR to marginally systems with simple calculation is proposed. The definition of the measure is same with existing measure of DODR. We used a novel concept of time averaged gradient of Gramians to calculate the steady-state solution of DO DR .

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Section: Numerical Examplementioning

confidence: 97%

“…Although the previous researches in [18] proposed a method to calculate the DODR measure for anti-stable systems, the problem to apply the measure to marginally stable system still remains. Hence, in this paper, the way to apply DODR measure with simple calculation to marginally-stable systems is proposed.…”

Section: Introductionmentioning

confidence: 98%

Degree of disturbance rejection capability for linear marginally stable systems

Lee

Park

2015

2015 15th International Conference on Control, Automation and Systems (ICCAS)

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“…The Gramians constructed from the A, B , and G system matrices encode the dynamic properties of a system. These matrices have been widely used in testing for controllability and observability [65–67], in model reduction [66, 68–70], in sensor and actuator placement [15, 71–73], in disturbance rejection [74, 75], and in joint sensor and actuator design [76, 77]. When the system matrix A is stable, the controllability and observability gramians are defined in the time domain as: The disturbance sensitivity Gramian X d is constructed similar to X c , by swapping the B with the G matrix in Eqn.…”

Section: Supplementary Informationmentioning

confidence: 99%

Motion vision is tuned to maximize sensorimotor energy transfer in blowfly flight

Humbert,

Krapp,

Baeder

et al. 2024

Preprint

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Biological sensors have evolved to act as matched filters that respond preferentially to the stimuli they expect to receive during ecologically relevant tasks. For instance, insect visual systems are tuned to detect stimuli ranging from the small-target motion of mates or prey to the polarization pattern of the sky. In flies, individually identified neurons called lobula plate tangential cells respond to optic flow fields matched to specific self-motions, forming the output layer of what is presently nature's best-understood deep convolutional neural network. But what functional principle does their tuning embed, and how does this aid motor control? Here we test the hypothesis that evolution co-tunes physics and physiology by aligning the preferred directions of an animal's sensors to the most dynamically-significant directions of its motor system. We build a state-space model of blowfly flight by combining visual electrophysiology, synchrotron-based X-ray microtomography, high-speed videogrammetry, and computational fluid dynamics. We then apply control-theoretic tools to show that the tuning of the fly's widefield motion vision system maximizes the flow of energy from control inputs and disturbances to sensor outputs, rather than optimizing state estimation as is the conventional approach to sensor placement in engineering. We expect the same functional principle to apply across sensorimotor systems in other organisms, with implications for the design of novel control architectures for robotic systems combining high performance with low computational load and low power consumption.

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Minimum energy control for complex networks

Lindmark

Altafini

2018

Sci Rep

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The aim of this paper is to shed light on the problem of controlling a complex network with minimal control energy. We show first that the control energy depends on the time constant of the modes of the network, and that the closer the eigenvalues are to the imaginary axis of the complex plane, the less energy is required for complete controllability. In the limit case of networks having all purely imaginary eigenvalues (e.g. networks of coupled harmonic oscillators), several constructive algorithms for minimum control energy driver node selection are developed. A general heuristic principle valid for any directed network is also proposed: the overall cost of controlling a network is reduced when the controls are concentrated on the nodes with highest ratio of weighted outdegree vs indegree.

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Degree of disturbance rejection capability for linear anti-stable systems

Cited by 3 publications

References 17 publications

Degree of disturbance rejection capability for linear marginally stable systems

Degree of disturbance rejection capability for linear marginally stable systems

Motion vision is tuned to maximize sensorimotor energy transfer in blowfly flight

Minimum energy control for complex networks

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