Statistic tracking control: a multi-objective optimization algorithm

Authors:
Lei Guo
Affiliations:
Research Institute of Automation, Southeast University, Nanjing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 4
Cited 0

Bounded Dynamic Stochastic Systems: Modelling and Control

Bounded Dynamic Stochastic Systems: Modelling and Control
Estimation with Applications to Tracking and Navigation

Estimation with Applications to Tracking and Navigation
PID controller design for output PDFs of stochastic systems using linear matrix inequalities

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Technical Communique: Generalized discrete-time PI control of output PDFs using square root B-spline expansion

Automatica (Journal of IFAC)

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Abstract

This paper addresses a new type of control framework for dynamical stochastic systems, which is called statistic tracking control here. General non-Gaussian systems are considered and the tracked objective is the statistic information (including the moments and the entropy) of a given target probability density function (PDF), rather than a deterministic signal. The control is aiming at making the statistic information of the output PDFs to follow those of a target PDF. The B-spline neural network with modelling error is applied to approximate the corresponding dynamic functional. For the nonlinear weighting system with time delays in the presence of exogenous disturbances, the generalized H2 and H∞ optimization technique is then used to guarantee the tracking, robustness and transient performance simultaneously in terms of LMI formulations.