Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

2020 ◽  
Vol 34 (11) ◽  
pp. 1658-1676
Author(s):  
Ting Cui ◽  
Ling Xu ◽  
Feng Ding ◽  
Ahmed Alsaedi ◽  
Tasawar Hayat
Keyword(s):  
Maximum Likelihood ◽  
Differential Evolution ◽  
State Space ◽  
Space Form ◽  
The State ◽  
Multivariable Systems ◽  
Identification Algorithm ◽  
Adaptive Differential Evolution

scholarly journals Experience Rating of ARIMA Processes by the Kalman Filter

1986 ◽  
Vol 16 (1) ◽  
pp. 19-31 ◽  
Author(s):  
Jukka Rantala
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
State Space ◽  
Theoretical Approach ◽  
Space Form ◽  
The State ◽  
Experience Rating ◽  
Filter Technique ◽  
Main Emphasis ◽  
Arima Processes

AbstractThis paper deals with experience rating of claims processes of ARIMA structures. By experience rating we mean that future premiums should be only a function of past values of the claims process. The main emphasis is on demonstrating the usefulness of the control-theoretical approach in the search for optimal rating rules. Optimality is here defined to mean as smooth a flow of premiums as possible when the variation in the accumulated profit is restricted to a certain amount. First it is shown how the underlying model in its simplest form can be transformed into the state-space form. Then the Kalman filter technique is used to find the optimal rules. Also a time delay in information is taken into account. The optimal rules are illustrated by examples.

scholarly journals Parameter and State Estimator for State Space Models

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ruifeng Ding ◽  
Linfan Zhuang
Keyword(s):  
State Space ◽  
State Equation ◽  
The State ◽  
Parameter Estimates ◽  
Identification Algorithm ◽  
State Variables ◽  
Input Output ◽  
State Estimator ◽  
Output Data ◽  
Canonical State

This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.

Plant-Input-Mapping Discretization Method for a Feedback System in the State-Space Form

2021 ◽  
Author(s):  
Keisuke Yagi ◽  
Hiroaki Muto ◽  
Yoshikazu Mori
Keyword(s):  
Transfer Function ◽  
State Space ◽  
State Feedback ◽  
Closed Loop ◽  
Space Form ◽  
Feedback System ◽  
The State ◽  
Loop System ◽  
Closed Loop System ◽  
Digital Redesign

Abstract The paper proposes the digital redesign technique called plant-input-mapping (PIM) method for a feedback system described in the state-space form. The PIM method, which was originally presented in the transfer function form, focuses on the plant input signal via the plant input transfer function and discretizes it so as to satisfy the control zero principle in the resulting discrete-time closed-loop system, which leads to guaranteeing the closed-loop stability for any non-pathological sampling interval. In accordance with this approach, the proposed PIM method focuses on the control zeros included in the plant input signal. The paper proves that the matched-pole-zero discrete-time model of the plant input state-equation satisfies the control zero principle with the step-invariant model of the plant. Then, when the matched-pole-zero model is set as the target of model matching, the parameters of the state-space PIM controller employing the observer-based dynamic state-feedback can systematically be determined from the underlying continuous-time closed-loop system with guaranteed stability. This discretization process can immediately be applied to a state-feedback system and a class of multi-input multi-output systems without any modification, which cannot be discretized by the conventional PIM methods. The discretization performance of the proposed PIM method is evaluated through illustrative examples with comparable digital redesign methods, which reveal that the proposed method performs a good reproduction of the characteristics of the underlying closed-loop system.

Maximum likelihood-based robust state estimation over a horizon length during measurement outliers

2020 ◽  
pp. 014233122092889
Author(s):  
Khurram Ali ◽  
Muhammad Tahir
Keyword(s):  
Maximum Likelihood ◽  
State Estimation ◽  
State Space ◽  
The State ◽  
State Space Models ◽  
Dynamic State ◽  
State Estimator ◽  
Detection Mechanism ◽  
Estimation Process ◽  
Novel Approach

In this paper, a novel approach towards horizon-based maximum likelihood (ML) state estimator is proposed that makes the state estimation process more robust against unmodeled and unstructured noise and disturbances in the state-space models. State space models provide a powerful way to perform state and parameter estimation for dynamical systems. However, if the measurements are contaminated by outliers and disturbances with no known models, the estimation process is highly biased. A ML-based approach is used to find a batch solution for the filtering problem. Based on ML solution, a robust algorithm is proposed that seamlessly estimates dynamic state in the presence of zero or non-zero mean measurement outliers. The proposed algorithm that dictates this switching uses an explicit outlier detection mechanism that enables its seamless working. Simulations have been carried out for a moving target tracking application as an example to demonstrate the resilience of the proposed method against zero-mean and non-zero mean measurement outliers in comparison to state-of-the-art methods.

OPTIMIZATION OF PLATE-FIN HEAT EXCHANGER USING ADAPTIVE DIFFERENTIAL EVOLUTION JADE

2016 ◽  
Author(s):  
Marcos Batistella Lopes ◽  
Viviana Mariani ◽  
Emerson Hochsteiner de Vasconcelos Segundo
Keyword(s):  
Heat Exchanger ◽  
Differential Evolution ◽  
Adaptive Differential Evolution
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