By Dan Zhang, Bin Wei
The robot mechanism and its controller make a whole procedure. because the robot mechanism is reconfigured, the keep an eye on approach needs to be tailored therefore. the necessity for the reconfiguration frequently arises from the altering sensible specifications. This publication will concentrate on the adaptive keep an eye on of robot manipulators to deal with the replaced stipulations. the purpose of the e-book is to summarise and introduce the state of the art applied sciences within the box of adaptive keep watch over of robot manipulators on the way to increase the methodologies at the adaptive keep watch over of robot manipulators. Advances made long ago a long time are defined within the booklet, together with adaptive regulate theories and layout, and alertness of adaptive regulate to robot manipulators.
Read or Download Adaptive control for robotic manipulators PDF
Similar robotics & automation books
"Control idea, constructed within the 20th century, is the topic of this compilation of 25 annotated reprints of seminal papers representing the evolution of the keep an eye on box. rigorously assembled via a uncommon editorial board to make sure that each one paper contributes to the complete, instead of exist as a separate entity, this can be the 1st booklet to record the study and accomplishments that experience pushed the perform of keep watch over.
Bipedal locomotion is likely one of the so much tricky demanding situations up to the mark engineering. so much books deal with the topic from a quasi-static viewpoint, overlooking the hybrid nature of bipedal mechanics. suggestions regulate of Dynamic Bipedal robotic Locomotion is the 1st e-book to offer a entire and mathematically sound remedy of suggestions layout for reaching strong, agile, and effective locomotion in bipedal robots.
This ebook explores rising equipment and algorithms that permit specific keep watch over of micro-/nano-positioning platforms. The textual content describes 3 regulate innovations: hysteresis-model-based feedforward keep watch over and hysteresis-model-free suggestions regulate in line with and loose from nation remark. each one paradigm gets devoted cognizance inside a selected a part of the textual content.
- Applied control theory
- Control Valve Primer, 4th Edition: A User's Guide
- Linear Control Theory: Structure, Robustness, and Optimization (Automation and Control Engineering)
- Hybrid Predictive Control for Dynamic Transport Problems
- Fuzzy Controllers, Theory and Applications
Additional info for Adaptive control for robotic manipulators
Convex Optimization, Cambridge University Press. Bristow, D. , M. Tharayil and A. G. Alleyne. 2006. A survey of iterative learning control. Control Systems, IEEE, 26(3): 96–114. Chen, Y. and C. Wen. 1999. Iterative learning control: Convergence, robustness and applications. , T. Yucelen, M. Muhlegg and E. N. Johnson. 2013. Concurrent learning adaptive control of linear systems with exponentially convergent bounds. International Journal of Adaptive Control and Signal Processing, 27(4): 280–301. Ciliz, K.
191–196. In: Proceedings of Conference on Applied Motion Control, University of Minnesota. , E. Carpanzano and M. Brusaferri. 2011. Design and implementation of distributed and adaptive control solutions for reconfigurable manufacturing systems. In: CIRP Sponsored ICMS. International Conference on Manufacturing Systems. , M. Mazzolinib and E. Carpanzanoa. 2015. An approach to design and develop reconfigurable control software for highly automated production systems. International Journal of Computer Integrated Manufacturing, 28(3): 321–336.
As shown in the subsequent stability analysis, the parameter estimate learning rate is related to the N minimum eigenvalue of ∑ Y 1iT Y1i, motivating the use of the singular value maximization i=1 algorithm in (Chowdhary 2010) for adding data to the history stack. , (Mühlegg et al. 2012). Substituting the controller from (6) into the error dynamics in (5) results in the following closed-loop tracking error dynamics M (q) r˙ = Y2 (q, q˙, qd, q˙d, q¨ d) θ˜ − e − Vm (q, q˙) r − k1r. (9) Similarly, taking the time derivative of (4) and substituting the parameter estimate update law from (8) results in the following closed-loop parameter estimation error dynamics .