Title :
Adaptive predictive control policy for nonlinear stochastic systems
Author :
Filatov, N.M. ; Unbehausen, H.
Author_Institution :
Autom. Control Lab., Ruhr-Univ., Bochum, Germany
fDate :
11/1/1995 12:00:00 AM
Abstract :
The problem of optimal adaptive predictive control for nonlinear stochastic systems is considered. A classification of various suboptimal approaches is given. A new approximation for the probability measure for the extended state of the system is suggested to derive a new suboptimal control law. It is assumed for this approximation that the system operates in closed-loop feedback mode for one part of the extended state vector and in open feedback loop mode for the other part of this vector. The certainty equivalence (CE) assumption is used only for the first part of the extended state vector. An analytical comparison for the suggested control policy shows its superiority in control quality compared with that of the open-loop optimal control policy in the case of an exactly observed first part of the extended state vector. The upper bound of the performance index is determined for this case. The suggested control policy has a simple form for linear systems with unknown parameters. A simulated example is used to demonstrate the potential of the suggested method and its superiority over the usually applied CE policy
Keywords :
adaptive control; closed loop systems; feedback; nonlinear control systems; optimal control; performance index; predictive control; probability; stochastic systems; suboptimal control; adaptive predictive control policy; certainty equivalence; closed-loop feedback mode; control quality; extended state; linear systems; nonlinear stochastic systems; open feedback loop mode; performance index; probability measure; suboptimal approaches; Adaptive control; Control systems; Feedback loop; Open loop systems; Optimal control; Predictive control; Programmable control; State feedback; Stochastic systems; Vectors;
Journal_Title :
Automatic Control, IEEE Transactions on