Optimal nonlinear damping control of second-order systems

Ruderman, Michael

doi:10.1016/j.jfranklin.2021.03.022

Cited by 10 publications

(14 citation statements)

References 32 publications

(37 reference statements)

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“…The property of a system to be convergent follows from the sufficient condition, given by Demidovich [8], which is formulated in the following theorem [9]: Theorem 1. Consider the system (1). Suppose, for some positive definite matrix P = P T > 0 the matrix…”

Section: Convergent Dynamicsmentioning

confidence: 99%

“…In the following, we will summarize the optimal nonlinear damping control, insofar as necessary for the main results presented in Section 3, while for that recently introduced control and its properties we refer to [1].…”

Section: Optimal Nonlinear Damping Controlmentioning

confidence: 99%

“…Remark 4. The closed-loop control system (4), (5) allows, in addition, for a bounded control action | ẋ2 | < S, with S = const > 0 to be the controller saturation (see [1]), which is affecting yet neither the stability nor convergence performance of the state trajectories.…”

Section: Optimal Nonlinear Damping Controlmentioning

confidence: 99%

“…Being motivated by the Demidovich's [8] concept and definition of the convergent systems, reviewed in [9], this note analyzes and develops the output regulation properties of the optimal nonlinear damping control [1].…”

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Convergent dynamics of optimal nonlinear damping control

Ruderman

2021

Preprint

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Following Demidovich's concept and definition of convergent systems, we analyze the optimal nonlinear damping control, recently proposed [1] for the second-order systems. Targeting the problem of output regulation, correspondingly tracking of C 1 -trajectories, it is shown that all solutions of the control system are globally uniformly asymptotically stable. The existence of the unique limit solution in the origin of the control error and its time derivative coordinates are shown in the sense of Demidovich's convergent dynamics. Explanative numerical examples are also provided along with analysis.

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Section: Convergent Dynamicsmentioning

confidence: 99%

Section: Optimal Nonlinear Damping Controlmentioning

confidence: 99%

Section: Optimal Nonlinear Damping Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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…”

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confidence: 99%

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Convergent dynamics of optimal nonlinear damping control

Ruderman

2021

Preprint

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Biomembrane‐Based Memcapacitive Reservoir Computing System for Energy‐Efficient Temporal Data Processing

Hossain,

Mohamed,

Armendarez

et al. 2023

Advanced Intelligent Systems

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Reservoir computing is a highly efficient machine learning framework for processing temporal data by extracting input features and mapping them into higher dimensional spaces. Physical reservoirs have been realized using spintronic oscillators, atomic switch networks, volatile memristors, etc. However, these devices are intrinsically energy‐dissipative due to their resistive nature, increasing their power consumption. Therefore, memcapacitive devices can provide a more energy‐efficient approach. Herein, volatile biomembrane‐based memcapacitors are leveraged as reservoirs to solve classification tasks and process time series in simulation and experimentally. This system achieves a 99.6% accuracy for spoken‐digit classification and a normalized mean square error of in a second‐order nonlinear regression task. Furthermore, to showcase the device's real‐time temporal data processing capability, a 100% accuracy for an epilepsy detection problem is achieved. Most importantly, it is demonstrated that each memcapacitor consumes an average of 41.5 fJ of energy per spike, regardless of the selected input voltage pulse width, while maintaining an average power of 415 fW for a pulse width of 100 ms, orders of magnitude lower than those achieved by state‐of‐the‐art devices. Lastly, it is believed that the biocompatible, soft nature of our memcapacitor renders it highly suitable for computing applications in biological environments.

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Parametric design of functional observer for second‐order linear time‐varying systems

Sun

Liu

2022

Asian Journal of Control

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This note constructs a functional observer for second‐order linear time‐varying (SOLTV) systems under framework of second‐order systems. Based on the assumption that the SOLTV system is observable, sufficient conditions for the existence of a second‐order functional observer are proposed. Combined with the solution of generalized Sylvester equation (GSE), the completely parameterized expressions of coefficient matrices of functional observer are proposed, which includes the free parameters representing degrees of freedom, and can be utilized to achieve some additional performance. Further, the design procedure is put forward, whose validity is verified through an example.

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Optimal nonlinear damping control of second-order systems

Cited by 10 publications

References 32 publications

Convergent dynamics of optimal nonlinear damping control

Convergent dynamics of optimal nonlinear damping control

Biomembrane‐Based Memcapacitive Reservoir Computing System for Energy‐Efficient Temporal Data Processing

Parametric design of functional observer for second‐order linear time‐varying systems

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