By P. J. Fleming, D. I. Jones
Machine scientists have lengthy favored that the connection among algorithms and structure is essential. on the whole the extra really expert the structure is to a selected set of rules then the extra effective may be the computation. The penalty is that the structure turns into dead for computing something except that set of rules. This message holds for the algorithms utilized in real-time automated regulate up to the other box. those complaints will offer researchers during this box with an invaluable up to date reference resource of contemporary advancements.
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Extra info for Algorithms and Architectures for Real-Time Control 1991
The result s obtaine d are presente d for simulate d plant s with varyin g time-dela y and varyin g number , and locations , of the poles . Keyword s . PID autotuning , paralle l processing , neura l networks , adaptiv e control , multilaye r perceptrons , OCCAM . ). INTRODUCTIO N The Proportional , Integraland Derivativ e (PID ) controlle r is the most commo n controlle r in Industry . It offers a robus t performanc e over a wide rang e of operationa l condition s and, since it has just three tenns , is suitabl e for manua l tuning .
Severa l method s have been propose d for PID autotuning . Some are based on some knowledg e of the Nyquis t curve of the plant . Example s of this approac h are, for instance , the well know n Ziegle r and Nichols' s tunin g rules (Ziegler , 1942) and method s base d on relay feedbac k (Astrom , 1984a),(Astrom , 1984b) . Method s base d on on-lin e paramete r estimatio n hav e also been propose d for automati c tunin g of PID regulators . Self-tunin g regulator s based on minimu m variance , pole placemen t and LQG design method s may be configure d to give PID control .
A very big advanc e with neura l computin g is the tact that the knowledg e of the plant and its environmen t can be considerabl y less precise , leadin g to controllin g a plant under increase d uncertainty . Unfortunatel y it will take a lot of time to teach a neura l networ k system , but once it is taugh t it is fast and robus t leavin g the range of uncertaint y substantiall y greate r than that with adap tive control . Histor y has made clear it that neura l network s will hrst have to prove themselve s by solvin g problem s that have been previousl y impossibl e or very difficul t to solve, befor e being accepte d by industry .