scholarly journals Feedback control architecture of the R. sphaeroides chemotaxis network

2011 ◽  
Author(s):  
Abdullah Hamadeh ◽  
Elias August ◽  
Mark Roberts ◽  
Philip K. Maini ◽  
Judith P. Armitage ◽  
...  
Keyword(s):  
Feedback Control ◽  
Control Architecture

Statistical Feedback Control Architecture for Layered Manufacturing

1999 ◽  
Vol 7 (4) ◽  
pp. 391-404 ◽  
Author(s):  
TONG FANG ◽  
MOHSEN A. JAFARI ◽  
AHMAD SAFARI ◽  
STEPHEN C. DANFORTH
Keyword(s):  
Feedback Control ◽  
Layered Manufacturing ◽  
Control Architecture

scholarly journals A feedback control architecture for bioelectronic devices with applications to wound healing

2021 ◽  
Vol 18 (185) ◽  
Author(s):  
Bashir Hosseini Jafari ◽  
Ksenia Zlobina ◽  
Giovanny Marquez ◽  
Mohammad Jafari ◽  
John Selberg ◽  
...  
Keyword(s):  
Wound Healing ◽  
Feedback Control ◽  
Sliding Mode ◽  
Biological Response ◽  
Control Architecture ◽  
Target Area ◽  
Modular Control ◽  
Accelerate Wound Healing ◽  
Information Tracking ◽  
Bioelectronic Device

Bioelectronic devices can provide an interface for feedback control of biological processes in real-time based on sensor information tracking biological response. The main control challenges are guaranteeing system convergence in the presence of saturating inputs into the bioelectronic device and complexities from indirect control of biological systems. In this paper, we first derive a saturated-based robust sliding mode control design for a partially unknown nonlinear system with disturbance. Next, we develop a data informed model of a bioelectronic device for in silico simulations. Our controller is then applied to the model to demonstrate controlled pH of a target area. A modular control architecture is chosen to interface the bioelectronic device and controller with a bistable phenomenological model of wound healing to demonstrate closed-loop biological treatment. External pH is regulated by the bioelectronic device to accelerate wound healing, while avoiding chronic inflammation. Our novel control algorithm for bioelectronic devices is robust and requires minimum information about the device for broad applicability. The control architecture makes it adaptable to any biological system and can be used to enhance automation in bioengineering to improve treatments and patient outcomes.

scholarly journals Feedback Control Architecture and the Bacterial Chemotaxis Network

2011 ◽  
Vol 7 (5) ◽  
pp. e1001130 ◽  
Author(s):  
Abdullah Hamadeh ◽  
Mark A. J. Roberts ◽  
Elias August ◽  
Patrick E. McSharry ◽  
Philip K. Maini ◽  
...  
Keyword(s):  
Feedback Control ◽  
Bacterial Chemotaxis ◽  
Control Architecture

scholarly journals Effects on Cardiac and Peripheral Autonomic Nervous System by Posture Respiration Feedback Control Architecture

2019 ◽  
Vol 18 (4) ◽  
pp. 197-203
Author(s):  
Samith S. HERATH ◽  
Kazuki HAYAKAWA ◽  
Osamu SAKAI ◽  
Ryoma SEKIYA ◽  
Shusaku NOMURA
Keyword(s):  
Nervous System ◽  
Autonomic Nervous System ◽  
Feedback Control ◽  
Control Architecture ◽  
Autonomic Nervous

A Continuous First-Order Sliding Mode Control Law

2017 ◽  
Author(s):  
Keyvan Mohammadi ◽  
Andrea L’Afflitto
Keyword(s):  
Feedback Control ◽  
Sliding Mode Control ◽  
Closed Loop ◽  
Sliding Mode ◽  
Nonlinear Differential Equations ◽  
Nonlinear Observer ◽  
Control Architecture ◽  
Control Law ◽  
Control Laws ◽  
Mode Control

Sliding mode control is a technique to design robust feedback control laws. In its classical formulation, this approach involves discontinuous controls that arise several theoretical and practical challenges, such as the existence of non-unique solutions of nonlinear differential equations and chattering. Numerous variations of the sliding mode control architecture, such as the higher-order sliding mode method, have been presented to overcome these issues. In this paper, we present an alternative sliding mode control architecture that involves Hölder continuous feedback control laws, is simpler to implement than other non-classical nonlinear robust control techniques, guarantees robustness and uniform asymptotic stability of the closed-loop system, and ensures both existence and uniqueness of the closed-loop system’s trajectory. Our results are applied to design a robust nonlinear observer in the same form as the Walcott and Żak observer. Moreover, a numerical example illustrates our theoretical results and compares the proposed control law to the classical sliding mode control, the second order sliding mode control, and the super-twisting control.

scholarly journals Feedforward of Measurable Disturbances to Improve Multi-Input Feedback Control

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2114
Author(s):  
Javier Rico-Azagra ◽  
Montserrat Gil-Martínez
Keyword(s):  
Feedback Control ◽  
Model Uncertainty ◽  
Control Architecture ◽  
Control Input ◽  
Useful Plants ◽  
Reference Tracking ◽  
Output Performance ◽  
Input Control ◽  
Single Input ◽  
Noise Amplification

The availability of multiple inputs (plants) can improve output performance by conveniently allocating the control bandwidth among them. Beyond that, the intervention of only the useful plants at each frequency implies the minimum control action at each input. Secondly, in single input control, the addition of feedforward loops from measurable external inputs has been demonstrated to reduce the amount of feedback and, subsequently, palliate its sideband effects of noise amplification. Thus, one part of the action calculated by feedback is now provided by feedforward. This paper takes advantage of both facts for the problem of robust rejection of measurable disturbances by employing a set of control inputs; a previous work did the same for the case of robust reference tracking. Then, a control architecture is provided that includes feedforward elements from the measurable disturbance to each control input and feedback control elements that link the output error to each control input. A methodology is developed for the robust design of the named control elements that distribute the control bandwidth among the cheapest inputs and simultaneously assures the prescribed output performance to correct the disturbed output for a set of possible plant cases (model uncertainty). The minimum necessary feedback gains are used to fight plant uncertainties at the control bandwidth, while feedforward gains achieve the nominal output response. Quantitative feedback theory (QFT) principles are employed. An example illustrates the method and its benefits versus a control architecture with only feedback control elements, which have much more gain beyond the control bandwidth than when feedforward is employed.

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