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SUMMARY:Data-driven Nonlinear Control Design Using Robust Adaptive Dynamic
  Programming - Professor Zhong-Ping Jiang\, New York University
DTSTART:20150212T140000Z
DTEND:20150212T150000Z
UID:TALK57863@talks.cam.ac.uk
CONTACT:Tim Hughes
DESCRIPTION:Bellman's Dynamic Programming is a powerful theory for address
 ing multi-stage decision making problems\, and has been used to solve the 
 optimal control problem. However\, its well-known shortcoming is the so-ca
 lled 'curse of dimensionality'\, and optimal controllers designed rely on 
 the solution of certain HJB equation\, a PDE which is very hard\, if not i
 mpossible\, to solve for general nonlinear systems. Approximate/adaptive d
 ynamic programming (ADP) has been introduced to overcome the curse of dime
 nsionality and recently utilized in optimal controller design.\n\nIn this 
 talk\, we present a data-driven\, non-model-based framework for optimal no
 nlinear control design using data collected from the control plan in quest
 ion. Specifically\, we present a novel methodology called 'robust adaptive
  dynamic programming' (robust-ADP)\, that aims to generalize ADP theory to
  nonlinear systems with both parametric and dynamic uncertainties. Linear 
 and nonlinear controllers are designed with guaranteed robust stability an
 d optimality. Examples from computational neuroscience and electric power 
 systems are presented to illustrate the potentially wide applicability of 
 the robust-ADP theory.
LOCATION:Cambridge University Engineering Department\, LR3
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