scholarly journals State observation of LTV systems with delayed measurements: A parameter estimation-based approach with fixed convergence time

Automatica ◽  
2021 ◽  
pp. 109674
Author(s):  
Alexey Bobtsov ◽  
Nikolay Nikolaev ◽  
Romeo Ortega ◽  
Denis Efimov
Keyword(s):  
Parameter Estimation ◽  
Convergence Time ◽  
State Observation ◽  
Delayed Measurements

scholarly journals Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real-Coded Genetic Algorithm (RCGA)

2018 ◽  
Vol 7 (4.30) ◽  
pp. 443 ◽  
Author(s):  
Ainul, H.M.. Y ◽  
Salleh, S. M ◽  
Halib, N ◽  
Taib, H. ◽  
Fathi, M. S
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Technique ◽  
Convergence Time ◽  
Generation Gap ◽  
Model Order ◽  
Real Coded Genetic Algorithm ◽  
The Mean ◽  
Conventional Genetic Algorithm

System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different model orders have been considered where each of model order has been analyzed for 10 times. The influence of conventional genetic algorithm parameter - generation gap has been investigated too. The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). The validation test-through correlation analysis was used to validate the model. The model of model order 2 is chosen as the best model as it has fulfilled the criteria involved in selecting the accurate model. Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.

Intravenous Drug Delivery System for Blood Pressure Patient Based on Adaptive Parameter Estimation

2018 ◽  
Vol 7 (3) ◽  
pp. 42-53 ◽  
Author(s):  
Bharat Singh ◽  
Shabana Urooj
Keyword(s):  
Blood Pressure ◽  
Drug Delivery ◽  
Parameter Estimation ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Pressure Control ◽  
Convergence Time ◽  
Therapeutic Drug ◽  
Adaptive Parameter ◽  
Arterial Blood

Controlled drug delivery systems (DDS's) is an electromechanical system that supports the injection of a therapeutic drug intravenously into a patient's body and easily controls the infusion rate of patient's drug, blood pressure, and time of drug release. The controlled operation of mean arterial blood pressure (MABP) and cardiac output (CO) is highly desired in clinical operations. Different methods have been proposed for controlling MABP, all methods have certain disadvantages according to patient model. In this article, the authors propose blood pressure control using integral reinforcement learning based fuzzy inference systems (IRLFI) based on parameter estimation techniques and have compared this method in terms of integral squared error (ISE), integral absolute error (IAE), integral time-weighed absolute error (ITAE), root mean square error (RMSE), convergence time (CT).

scholarly journals Multi-Parameter Estimation of Uncertain Systems Based on the Extended PID Control Method

2021 ◽  
Author(s):  
Jinping Feng ◽  
Wei Wang
Keyword(s):  
Complex System ◽  
Parameter Estimation ◽  
Convergence Analysis ◽  
Control Method ◽  
Estimation Method ◽  
Pid Controllers ◽  
Unknown Parameters ◽  
State Observation ◽  
New States ◽  
Nonlinear Pid Controller

Parameter estimation is an important step in the identification of systems. With the extension of systems, there needs the multi-parameter estimation of systems. The estimation of multi parameters of complex systems based on the extended PID controllers is considered in this chapter. As the related references proved that the integral item of the nonlinear PID controller could deal with the uncertain part of the complex system (which can also be called new stripping principle, simple notes as NSP). Based on this theory, new multi-parameter estimation method is given. Firstly, the unknown parameters are expanded to new states of the system. Two cases, parameters are constant or changing with time, are separately analyzed. In the time-variant case, the unknown parameters are extended to functions which actual forms are uncertain. Secondly the method NSP could be applied to cope with the uncertain part, and then reconstruction state observation to estimate the states. If the states are observed, the unknown parameters are obtained at the same time. Finally the convergence analysis of the error systems and some simulations will be given in this chapter to indicate the effectiveness of the proposed method.

scholarly journals BDS Multipath Parameter Estimation in the Presence of Impulsive Noise

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Jicheng Ding ◽  
Lin Zhao ◽  
Chun Jia ◽  
Zhibin Luo
Keyword(s):  
Parameter Estimation ◽  
Impulsive Noise ◽  
Research Work ◽  
Satellite System ◽  
Performance Assessments ◽  
Convergence Time ◽  
Multipath Mitigation ◽  
Short Delay ◽  
Processing Gain ◽  
Similar Principle

This study focuses on mitigating the multipath, especially the short-delay multipath of the BeiDou navigation satellite system under impulsive noise conditions. A modified least meanp-norm (LMP) algorithm is developed to reduce the convergence time with the same steady-state error by predicting the updating trend of weights. The modified normalized power and the normalized polynomial least meanpth power are also directly provided according to a similar principle. According to the research work, an average filter has been utilized to improve the processing gain of designed mitigation scheme. Some significant simulation results verified the performance of the proposed adaption algorithm. Multipath parameter estimation tests have been conducted under different noise levels. Some comparative statistics performance assessments are quantified and verified under impulsive and additional white Gaussian noise environments. Results with various window widths of the average filtering and carrier-to-noise ratios indicate that the proposed scheme is able to improve the performance of the short-delay multipath mitigation under normal and degraded environments.

scholarly journals GLSDC Based Parameter Estimation Algorithm for a PMSM Model

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 611
Author(s):  
Artun Sel ◽  
Bilgehan Sel ◽  
Cosku Kasnakoglu
Keyword(s):  
Parameter Estimation ◽  
Permanent Magnet Synchronous Motor ◽  
Estimation Algorithm ◽  
Industrial Applications ◽  
Convergence Time ◽  
Initial State ◽  
Input And Output ◽  
Noisy Input ◽  
Before And After ◽  
Parameter Estimation Algorithm

In this study, a GLSDC (Gaussian Least Squares Differential Correction) based parameter estimation algorithm is used to identify a PMSM (Permanent Magnet Synchronous Motor) model. In this method, a nonlinear model is assumed to be the correct representation of the underlying state dynamics and the output signals are assumed to be measured in a noisy environment. Using noisy input and output signals, parameters that constitute the coefficients of the nonlinear state and input signal terms are to be estimated using the state transition matrix which is computed by the numerical means that are detailed. Since a GLSDC algorithm requires correct initial state value, this term is also estimated in addition to the unknown coefficients whose bounds are assumed to be known, which is mostly the case in the industrial applications. The batch input and output signals are used to iteratively estimate the parameter set before and after the convergence, and to recover the filtered state trajectories. A couple of different scenarios are tested by means of numerical simulations and the results are addressed. Different methods are discussed to compute better initial estimate values, to shorten the convergence time.

Sign in / Sign up

Export Citation Format

Share Document