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PID control with intelligent compensation for exoskeleton robots /

Explains how to use neural PD and PID controls to reduce integration gain, and provides explicit conditions on how to select linear PID gains using proof of semi-global asymptotic stability and local asymptotic stability with a velocity observer. These conditions are applied in both task and joint s...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Yu, Wen (Robotics engineer) (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London ; San Diego : Academic Press, an imprint of Elsvier, [2018]
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Yu, Wen  |c (Robotics engineer),  |e author. 
245 1 0 |a PID control with intelligent compensation for exoskeleton robots /  |c Wen Yu, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico. 
264 1 |a London ;  |a San Diego :  |b Academic Press, an imprint of Elsvier,  |c [2018] 
264 4 |c �2018 
300 |a 1 online resource :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
505 0 |a Stable PID control and systematic tuning of PID gains -- PID control in task space -- PD control with neural compensation -- PID control with neural compensation -- PD control with fuzzy compensation -- PD control with sliding mode compensation -- PID admittance control in task space -- PID admittance control in joint space -- Robot trajectory generation in joint space -- Design of upper limb exoskeletions. 
520 |a Explains how to use neural PD and PID controls to reduce integration gain, and provides explicit conditions on how to select linear PID gains using proof of semi-global asymptotic stability and local asymptotic stability with a velocity observer. These conditions are applied in both task and joint spaces, with PID controllers compensated by neural networks. This is a great resource on how to combine traditional PD/PID control techniques with intelligent control. Dr. Wen Yu presents several leading-edge methods for designing neural and fuzzy compensators with high-gain velocity observers for PD control using Lyapunov stability. Proportional-integral-derivative (PID) control is widely used in biomedical and industrial robot manipulators. An integrator in a PID controller reduces the bandwidth of the closed-loop system, leads to less-effective transient performance and may even destroy stability. Many robotic manipulators use proportional-derivative (PD) control with gravity and friction compensations, but improved gravity and friction models are needed. The introduction of intelligent control in these systems has dramatically changed the face of biomedical and industrial control engineering. 
588 0 |a Online resource; title from PDF title page (EBSCO, viewed January 23, 2018). 
650 0 |a PID controllers. 
650 0 |a Intelligent control systems. 
650 0 |a Robotics. 
650 2 |a Robotics  |0 (DNLM)D012371 
650 6 |a R�egulateurs PID.  |0 (CaQQLa)201-0192532 
650 6 |a Commande intelligente.  |0 (CaQQLa)201-0203115 
650 6 |a Robotique.  |0 (CaQQLa)201-0110752 
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650 7 |a Intelligent control systems  |2 fast  |0 (OCoLC)fst00975911 
650 7 |a PID controllers  |2 fast  |0 (OCoLC)fst01049851 
650 7 |a Robotics  |2 fast  |0 (OCoLC)fst01098997 
776 0 8 |i Print version :  |z 9780128133804 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780128133804  |z Texto completo