Practical Iterative Learning Control with Frequency Domain by Danwei Wang, Visit Amazon's Yongqiang Ye Page, search
By Danwei Wang, Visit Amazon's Yongqiang Ye Page, search results, Learn about Author Central, Yongqiang Ye, , Bin Zhang
This e-book is at the iterative studying keep an eye on (ILC) with specialize in the layout and implementation. We procedure the ILC layout in line with the frequency area research and handle the ILC implementation in keeping with the sampled information tools. this is often the 1st ebook of ILC from frequency area and sampled info methodologies. The frequency area layout equipment provide ILC clients insights to the convergence functionality that is of sensible merits. This e-book offers a entire framework with a variety of methodologies to make sure the learnable bandwidth within the ILC process to be set with a stability among studying functionality and studying balance. The sampled facts implementation guarantees powerful execution of ILC in functional dynamic platforms. The provided sampled info ILC equipment additionally make sure the stability of functionality and balance of studying procedure. additionally, the provided theories and methodologies are verified with an ILC managed robot process. The experimental effects convey that the machines can paintings in a lot greater accuracy than a suggestions regulate on my own can provide. With the proposed ILC algorithms, it really is attainable that machines can paintings to their layout limits set through sensors and actuators. the objective viewers for this publication comprises scientists, engineers and practitioners interested in any structures with repetitive operations.
Read Online or Download Practical Iterative Learning Control with Frequency Domain Design and Sampled Data Implementation PDF
Best applied books
The main tricky a part of making judgements within the wellbeing and fitness care box on all degrees (national, local, institutional, sufferer) is associated with the very complexity of the process itself, to the intrinsic uncertainty concerned and its dynamic nature. This calls for not just the power to research and interpret a large number of details but in addition organize it in order that it turns into a cognitive base for applicable decision-making.
This publication offers a vast layout purview in the framework of “pre-design, layout, and post-design” through targeting the “motive of design,” which suggests an underlying explanation for the layout of a product. The chapters are made out of papers in accordance with discussions on the “Design study best Workshop” held in Nara, Japan, in 2013.
- Applied and Industrial Mathematics: Venice - 1, 1989
- Physics for Scientists and Engineers Student Solutions Manual, Volume 1 (v. 1)
- A Course of Mathematics for Engineers and Scientists. Volume 6: Advanced Theoretical Mechanics
- Co-production in the Public Sector: Experiences and Challenges
- Elementary Vectors
Additional info for Practical Iterative Learning Control with Frequency Domain Design and Sampled Data Implementation
8 Robot Application of Multi-channel A-Type ILCs 10 47 2 RMS Error (degree) Single Channel without Cutoff Single Channel with Cutoff Two Channels, DFT/IDFT Approach Two Channels, Zero−Phase Filters Approach 10 1 10 0 10 −1 10 −2 0 20 40 60 80 100 120 140 160 180 200 Repetition Fig. 20 RMS error histories of 1 and 2-channels, A-type in Fig. 20 with the dash-dot line. The RMS error stops decreasing after about 80 repetitions. This is because the single-channel A-type learns only the frequency components in the range [0, 31) Hz, compared with the wider learnable frequency band, [0, 50] Hz, of the proposed multi-channel learning scheme.
In the time domain, Tayebi and Zaremba proposed gain-scheduling-based iterative learning controllers for continuous-time non-linear systems described by a blended multiple model representation . In , the learning gain changes according to the values of the validity functions depending on the operating point in the time domain, while in our approach, the learning compensator or parameter depend on frequency. The idea of using summational multiple functions to represent a blended model is the same in our multi-channel method and .
Automatica 36:717–725 24. Xu J-X, Viswanathan B (2000) Adaptive robust iterative learning control with dead zone scheme. Automatica 36:91–99 25. Fu J, Sinha NK (1990) An iterative learning scheme for motion control of robot using neural networks: a case study. J Intell Rob Syst 8:375–398 26. Kawato M, Furukawa K, Suzuki R (1987) A hierarchical neural network model for control and learning of voluntary movement. Biol Cybern 57:169–185 27. Moore KL, Dahleh M, Bhattacharyya SP (1989) Artificial neural networks for learning control.