This paper presents a new stochastical real-time LPC (Last Principal Component) algorithm to estimate single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) varying time models from input output data clusters of non stationary black boxes. Each of data clusters is on a time window. An application to estimate the control system model of a planar manipulator is developed. In fact many mathematical models of physical systems are non stationary such as industrial manipulator model. A real time estimation algorithm via stochastical LPC algorithm and an appraiser called "finite state machine" is then described For every data cluster the finite state machine updates the parameters of a Gaussian varying time model via an optimality design criterion that maximises the Likelihood function. The estimated steady-state parameters are constant values. By applying to two links planar manipulator, numerical tests of simulation in Matlab 6.5 demonstrate the effectiveness of this algorithm.

RAIMONDI FM, MELLUSO M (2005). Stochastical Real Time Finite State Machine LPC for Planar Manipulator Control System Model estimation. In Proceedings of the 7th WSEAS International Conference on Automatic Control, Modeling and Simulation (pp.127-132). PRAGA.

Stochastical Real Time Finite State Machine LPC for Planar Manipulator Control System Model estimation

RAIMONDI, Francesco Maria;MELLUSO, Maurizio
2005-01-01

Abstract

This paper presents a new stochastical real-time LPC (Last Principal Component) algorithm to estimate single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) varying time models from input output data clusters of non stationary black boxes. Each of data clusters is on a time window. An application to estimate the control system model of a planar manipulator is developed. In fact many mathematical models of physical systems are non stationary such as industrial manipulator model. A real time estimation algorithm via stochastical LPC algorithm and an appraiser called "finite state machine" is then described For every data cluster the finite state machine updates the parameters of a Gaussian varying time model via an optimality design criterion that maximises the Likelihood function. The estimated steady-state parameters are constant values. By applying to two links planar manipulator, numerical tests of simulation in Matlab 6.5 demonstrate the effectiveness of this algorithm.
7th International Conference on Automatic Control, Modeling and Simulation
Prague (Czech Republic)
13-15 March 2015
7
2005
6
PRAGUE, CZECH REPUBLIC
RAIMONDI FM, MELLUSO M (2005). Stochastical Real Time Finite State Machine LPC for Planar Manipulator Control System Model estimation. In Proceedings of the 7th WSEAS International Conference on Automatic Control, Modeling and Simulation (pp.127-132). PRAGA.
Proceedings (atti dei congressi)
RAIMONDI FM; MELLUSO M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/27794
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