scholarly journals Physical Constraints on Biological Integral Control Design for Homeostasis and Sensory Adaptation

2013 ◽  
Vol 104 (2) ◽  
pp. 505-515 ◽  
Author(s):  
Jordan Ang ◽  
David R. McMillen
Keyword(s):  
Control Design ◽  
Sensory Adaptation ◽  
Integral Control ◽  
Physical Constraints

scholarly journals String stable integral control design for vehicle platoons with disturbances

Automatica ◽  
2021 ◽  
Vol 127 ◽  
pp. 109542
Author(s):  
Guilherme Fróes Silva ◽  
Alejandro Donaire ◽  
Aaron McFadyen ◽  
Jason J. Ford
Keyword(s):  
Control Design ◽  
Integral Control ◽  
Vehicle Platoons

scholarly journals General Integral Control Design via Feedback Linearization

2014 ◽  
Vol 05 (01) ◽  
pp. 19-23 ◽  
Author(s):  
Baishun Liu ◽  
Jianhui Li ◽  
Xiangqian Luo
Keyword(s):  
Feedback Linearization ◽  
Control Design ◽  
Integral Control ◽  
General Integral

Lyapunov-Based Predictive Control Methodologies for Networked Control Systems

2018 ◽  
pp. 81-111
Author(s):  
Constantin-Florin Caruntu
Keyword(s):  
Communication Network ◽  
Computational Complexity ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Networked Control Systems ◽  
Control Design ◽  
Approximation Technique ◽  
Time Varying ◽  
Physical Constraints ◽  
Design Methodologies

The problem considered in this chapter is to control a vehicle drivetrain in order to minimize its oscillations while coping with the time-varying delays introduced by the CAN communication network and the strict timing limitations. As such, two Lyapunov-based model predictive control design methodologies are presented: one based on modeling the network-induced time-varying delays using a polytopic approximation technique and the second one based on modeling the delays as disturbances. Several tests performed using an industry validated drivetrain model indicate that the proposed design methodologies can handle both the performance/physical constraints and the strict limitations on the computational complexity, while effectively coping with the time-varying delays. Moreover, a comparative analysis between the two Lyapunov-based model predictive control design methodologies in terms of computational complexity, number of optimization variables, and obtained performances is carried out.

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