Imperial College London
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DrGiordanoScarciotti

Faculty of Engineering, Department of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6268g.scarciotti Website

 
 
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Location

 

1118Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Scarciotti:2019:10.23919/ECC.2019.8795975,
author = {Scarciotti, G and Mellone, A},
doi = {10.23919/ECC.2019.8795975},
pages = {287--292},
publisher = {IEEE},
title = {ε-approximate output regulation of linear stochastic systems: a hybrid approach},
url = {http://dx.doi.org/10.23919/ECC.2019.8795975},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - The   problem   of   output   regulation   for   linear stochastic systems is addressed. The controlled system belongs to a general class of linear systems, namely the state, the control input  and  the  exogenous  input  appear  in  both  the  drift  and diffusion terms of the differential equations. Building upon the solution  of  the  ideal,  non-causal,  stochastic  regulator  problem,we define an approximate full-information problem. By means of  measurements  of  the  state  vector,  we  provide  a  way  to compute  a  sequence  of  scalars  approximating a  posteriori the variations of the Brownian motion. Then, we propose a hybrid control  architecture  which  solves  the  approximate  problem.The  continuous-time  part  of  the  controller  is  deterministic,whereas the discrete-time part has the function of “correcting”the   control   action   by   means   of   the   approximate   discrete-time  Brownian  motion.  The  solution  of  the  ideal  stochastic regulator  problem  is  recovered  as  the  sampling  time  tends to  zero.  We  illustrate  the  results  by  means  of  a  numerical example  and  conclude  the  paper  with  some  final  remarks:the  proposed  control  architecture  is  the  first  causal  solution of  the  full-information  output  regulation  problem  and  is  an essential intermediate step for the solution of the error-feedback problem.
AU  - Scarciotti,G
AU  - Mellone,A
DO  - 10.23919/ECC.2019.8795975
EP  - 292
PB  - IEEE
PY  - 2019///
SP  - 287
TI  - ε-approximate output regulation of linear stochastic systems: a hybrid approach
UR  - http://dx.doi.org/10.23919/ECC.2019.8795975
UR  - http://hdl.handle.net/10044/1/68376
ER  -
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