Least Squares Matrix Algorithm for State-Space Modelling of Dynamic Systems

Olkkonen, Juuso; Olkkonen, H.

doi:10.4236/jsip.2011.24041

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…We prefer the companion matrix structure of the state transition matrix A , which allows the direct insertion of the polynomial coefficients in (1). Fast computational algorithms are presented in [5,6].…”

Section: Implementation Of the Iir Filters And Arma Modelsmentioning

confidence: 99%

Adaptive Matrix/Vector Gradient Algorithm for Design of IIR Filters and ARMA Models

Olkkonen¹,

Ahtiainen²,

Järvinen³

et al. 2013

JSIP

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This work describes a novel adaptive matrix/vector gradient (AMVG) algorithm for design of IIR filters and ARMA signal models. The AMVG algorithm can track to IIR filters and ARMA systems having poles also outside the unit circle. The time reversed filtering procedure was used to treat the unstable conditions. The SVD-based null space solution was used for the initialization of the AMVG algorithm. We demonstrate the feasibility of the method by designing a digital phase shifter, which adapts to complex frequency carriers in the presence of noise. We implement the half-sample delay filter and describe the envelope detector based on the Hilbert transform filter.

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Section: Implementation Of the Iir Filters And Arma Modelsmentioning

confidence: 99%

Adaptive Matrix/Vector Gradient Algorithm for Design of IIR Filters and ARMA Models

Olkkonen¹,

Ahtiainen²,

Järvinen³

et al. 2013

JSIP

Self Cite

View full text Add to dashboard Cite

show abstract

“…ese include the Equation Error Method (EEM) [5][6][7][8], the Output Error Method (OEM) [9][10][11], and the Filter Error Method (FEM) [12][13][14][15]. However, those methods still require a predefined/ initial aircraft model.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Control Parameter Estimation Using Self‐Adaptive Teaching‐Learning‐Based Optimization with an Acceptance Probability

Kanokmedhakul

Panagant

Bureerat

et al. 2021

Computational Intelligence and Neuroscience

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This work presents a metaheuristic (MH) termed, self-adaptive teaching-learning-based optimization, with an acceptance probability for aircraft parameter estimation. An inverse optimization problem is presented for aircraft longitudinal parameter estimation. The problem is posed to find longitudinal aerodynamic parameters by minimising errors between real flight data and those calculated from the dynamic equations. The HANSA-3 aircraft is used for numerical validation. Several established MHs along with the proposed algorithm are used to solve the proposed optimization problem, while their search performance is investigated compared to a conventional output error method (OEM). The results show that the proposed algorithm is the best performer in terms of search convergence and consistency. This work is said to be the baseline for purely applying MHs for aircraft parameter estimation.

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Least Squares Matrix Algorithm for State-Space Modelling of Dynamic Systems

Cited by 2 publications

References 14 publications

Adaptive Matrix/Vector Gradient Algorithm for Design of IIR Filters and ARMA Models

Adaptive Matrix/Vector Gradient Algorithm for Design of IIR Filters and ARMA Models

Aircraft Control Parameter Estimation Using Self‐Adaptive Teaching‐Learning‐Based Optimization with an Acceptance Probability

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