ISA Transactions, Journal Year: 2024, Volume and Issue: 157, P. 213 - 223
Published: Dec. 10, 2024
Language: Английский
Optimal Control Applications and Methods, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 29, 2025
ABSTRACT Through dividing a multivariable system into several subsystems, this paper derives the sub‐identification model. Utilizing obtained model, an auxiliary model‐based maximum likelihood forgetting gradient algorithm is derived. Considering enhancing parameter estimation accuracy, multi‐innovation (AM‐ML‐MIFG) proposed taking advantage of identification theory. Simulation results test effectiveness algorithms, and confirm that AM‐ML‐MIFG has satisfactory performance in capturing dynamic properties system.
Language: Английский
Citations
2
Optimal Control Applications and Methods, Journal Year: 2025, Volume and Issue: unknown
Published: March 13, 2025
ABSTRACT This article addresses the parameter estimation problems of bilinear output‐error systems, and auxiliary model identification idea particle filtering technique are adopted to overcome obstacle resulting from unknown true outputs. Then a filtering‐based forgetting factor stochastic gradient algorithm is proposed for systems. To enhance convergence rate accuracy estimation, we expand scalar innovation an vector develop multi‐innovation algorithm. Finally, numerical example practical continuous stirred tank reactor process provided show that discussed methods work well. The results indicate algorithms effective identifying systems can generate more accurate estimates than model‐based
Language: Английский
Citations
0
International Journal of Robust and Nonlinear Control, Journal Year: 2025, Volume and Issue: unknown
Published: April 22, 2025
ABSTRACT This article focuses on the parameter estimation issues for dual‐rate Volterra fractional‐order autoregressive moving average models. In case of sampling, we derive a identification model system and implement intersample output with help an auxiliary method. Then, combined self‐organizing map technique, propose Aitken multi‐innovation gradient‐based iterative algorithm. The parameters are estimated using algorithm, whereas differential orders determined Moreover, computational cost proposed algorithm is analyzed floating point operation. Finally, convergence analysis simulation examples show effectiveness
Language: Английский
Citations
0
Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering, Journal Year: 2025, Volume and Issue: unknown
Published: March 28, 2025
Considering the existence of nonlinearity and fractional-order phenomena in practical environments, this paper investigates parameter estimation methods for a class Hammerstein nonlinear systems disturbed by colored noise. The are based on decomposition strategy, separating identification from system parameters. Meanwhile, is divided into two subsystems, which linear block using hierarchical principle. To overcome problem redundant estimation, over-parameterization method key item separation used, respectively. Then, two-stage gradient-based iterative algorithm term derived, auxiliary model used to compute unmeasurable variables. In addition, we analyze computational efficiencies proposed algorithms. simulation results indicate that algorithms effective. Finally, evaluated through battery model. show well agreement with real outputs.
Language: Английский
Citations
0
International Journal of Adaptive Control and Signal Processing, Journal Year: 2025, Volume and Issue: unknown
Published: March 28, 2025
ABSTRACT This article presents a decomposition‐based least squares estimation algorithm for the multivariate input nonlinear system. By using hierarchical identification principle, breaks down system into two subsystems, one containing parameters of linear dynamic block and other static block. Treating unknown variables contained in information vector model is to replace them with outputs an auxiliary model. The comparative results between recursive developed this are provided test proposed algorithms have lower computational cost higher accuracy. Furthermore, convergence analyzed, which can guarantee stability algorithm. simulation confirm efficacy derived effectively estimating systems.
Language: Английский
Citations
0
International Journal of Robust and Nonlinear Control, Journal Year: 2025, Volume and Issue: unknown
Published: April 4, 2025
ABSTRACT When the physical properties of mechanical systems align with structure model, continuous‐time (CT) can be effectively represented by an interpretable and parsimonious additive formal models. This article addresses parameter estimation challenges CT autoregressive moving average (ACTARMA) systems. Based on maximum likelihood principle, optimality conditions for proposed identification algorithms are formulated ACTARMA Additionally, auxiliary model‐based hierarchical refined instrumental variable (AM‐HRIV) iterative algorithm AM‐HRIV recursive developed means principle model idea. These establish a pseudo‐linear regression relationship involving optimal prefilters derived from unified model. The effectiveness methods is demonstrated numerical simulation, performance method in identifying modal representations verified experimental data.
Language: Английский
Citations
0
International Journal of Robust and Nonlinear Control, Journal Year: 2025, Volume and Issue: unknown
Published: May 5, 2025
ABSTRACT This article investigates the problem of parameter estimation for bilinear state‐space systems with nonlinear input. An innovative approach that combines Nesterov‐accelerated adaptive moment algorithm a line search strategy is proposed to address such complex systems. The uses backtracking method dynamically select an appropriate step‐size, thereby enhancing efficiency. effectiveness demonstrated through simulation experiments.
Language: Английский
Citations
0
International Journal of Adaptive Control and Signal Processing, Journal Year: 2025, Volume and Issue: unknown
Published: May 7, 2025
ABSTRACT This paper mainly investigates the joint estimation of parameters and states for fractional‐order Wiener state space model. Based on Kalman filter principle, a generalized recursive least squares algorithm with forgetting factor is proposed. In addition, filtering‐based presented, which reduces influence colored noise parameter estimation. A gradient identification introduced to estimate order fractional‐order. Under persistent excitation conditions, analysis indicates that proposed can system. simulation example given confirm algorithms are effective.
Language: Английский
Citations
0
International Journal of Robust and Nonlinear Control, Journal Year: 2025, Volume and Issue: unknown
Published: May 9, 2025
ABSTRACT Nonlinear system identification plays a key role in real‐world modeling. The spline networks can model the nonlinearity with high precision without prior knowledge of nonlinear structure. This paper examines problem Hammerstein systems outliers by using to describe nonlinearity. To avoid redundant computation, two sub‐models are derived, one local parameters and other global linear parameters. By exploiting insensitivity correntropy outliers, correntropy‐based robust interval‐varying recursive estimation method is presented. proposed not only models unknown computational efficiency but also under premise that total distribution observed data unknown. superiority algorithm validated simulation experiments.
Language: Английский
Citations
0
ISA Transactions, Journal Year: 2024, Volume and Issue: 157, P. 213 - 223
Published: Dec. 10, 2024
Language: Английский
Citations
0