Aircraft parameter estimation using a new filtering technique based upon a neural network and Gauss-Newton method

2009 ◽  
Vol 113 (1142) ◽  
pp. 243-252 ◽  
Author(s):  
N. K. Peyada ◽  
A. K. Ghosh
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Dynamic Model ◽  
Newton Method ◽  
Initial Conditions ◽  
Estimation Method ◽  
Wind Tunnel Test ◽  
Neural Model ◽  
Estimation Methods ◽  
Flight Data

Abstract A new parameter estimation method based upon neural network is proposed. The method proposed here uses feed forward neural networks to establish a neural model that could be used to predict subsequent time histories given the suitable measured initial conditions. The proposed neural model would not represent a generic flight dynamic model. The neural model in this case develops point to point fitting of the input and the output data. Thus, it could at best be referred to as flight dynamic model in restricted sense. Gauss-Newton method is then used to obtain optimal values of the aerodynamic parameters by minimising a suitable defined error cost function. The method has been validated using longitudinal and lateral-directional flight data of various test aircraft. The results thus obtained were compared with those obtained through wind tunnel test, or those obtained using Maximum likelihood and/or Filter error methods. Unlike, most of the parameter estimation methods, the proposed method does not require a prior description of the model. It also bypasses the requirement of solving equations of motion. This feature of the proposed method may have special significance in handling flight data of an unstable aircraft.

Parameter estimation of UAV from flight data using neural network

2018 ◽  
Vol 90 (2) ◽  
pp. 302-311 ◽  
Author(s):  
Dhayalan R. ◽  
Subrahmanyam Saderla ◽  
Ajoy Kanti Ghosh
Keyword(s):  
Parameter Estimation ◽  
Design Methodology ◽  
Maximum Likelihood Method ◽  
Estimation Method ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Process Noise ◽  
Content Type ◽  
Flight Data ◽  
Aerial Vehicle

Purpose The purpose of this paper is to present the application of the neural-based estimation method, Neural-Gauss-Newton (NGN), using the real flight data of a small unmanned aerial vehicle (UAV). Design/methodology/approach The UAVs in general are lighter in weight and their flight is usually influenced by the atmospheric winds because of their relatively lower cruise speeds. During the presence of the atmospheric winds, the aerodynamic forces and moments get modified significantly and the accurate mathematical modelling of the same is highly challenging. This modelling inaccuracy during parameter estimation is routinely treated as the process noise. Furthermore, because of the limited dimensions of the small UAVs, the measurements are usually influenced by the disturbances caused by other subsystems. To handle these measurement and process noises, the estimation methods based on neural networks have been found reliable in the manned aircrafts. Findings Six sets of compatible longitudinal flight data of the designed UAV have been chosen to estimate the parameters using the NGN method. The consistency in the estimates is verified from the obtained mean and the standard deviation and the same has been validated by the proof-of-match exercise. It is evident from the results that the NGN method was able to perform on a par with the conventional maximum likelihood method. Originality/value This is a partial outcome of the research carried out in estimating parameters from the UAVs.

Aircraft parameter estimation using ELM network

2020 ◽  
Vol 92 (6) ◽  
pp. 895-907
Author(s):  
Hari Om Verma ◽  
Naba Kumar Peyada
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Mathematical Formulation ◽  
Estimation Methods ◽  
Content Type ◽  
Flight Data ◽  
The Real ◽  
Aerodynamic Parameters ◽  
Hidden Layer ◽  
Error Method

Purpose The purpose of this paper is to investigate the estimation methodology with a highly generalized cost-effective single hidden layer neural network. Design/methodology/approach The aerodynamic parameter estimation is a challenging research area of aircraft system identification, which finds various applications such as flight control law design and flight simulators. With the availability of the large database, the data-driven methods have gained attention, which is primarily based on the nonlinear function approximation using artificial neural networks. A novel single hidden layer feed-forward neural network (FFNN) known as extreme learning machine (ELM), which overcomes the issues such as learning rate, number of epochs, local minima, generalization performance and computational cost, as encountered in the conventional gradient learning-based FFNN has been used for the nonlinear modeling of the aerodynamic forces and moments. A mathematical formulation based on the partial differentiation is proposed to estimate the aerodynamic parameters. Findings The real flight data of longitudinal and lateral-directional motion have been considered to estimate their respective aerodynamic parameters using the proposed methodology. The efficacy of the estimates is verified with the results obtained through the conventional parameter estimation methods such as the equation-error method and filter-error method. Originality/value The present study is an outcome of the research conducted on ELM for the estimation of aerodynamic parameters from the real flight data. The proposed method is capable to estimate the parameters in the presence of noise.

PPG-BASED AUTOMATED ESTIMATION OF BLOOD PRESSURE USING PATIENT-SPECIFIC NEURAL NETWORK MODELING

2020 ◽  
Vol 20 (06) ◽  
pp. 2050037
Author(s):  
ABHISHEK CHAKRABORTY ◽  
DEBOLEENA SADHUKHAN ◽  
SAURABH PAL ◽  
MADHUCHHANDA MITRA
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Estimation Method ◽  
Estimation Methods ◽  
Patient Specific ◽  
Neural Network Modeling ◽  
Correction Factors ◽  
Healthcare Applications ◽  
Average Accuracy ◽  
Blood Pressure Estimation

Recently, photoplethysmography (PPG)-based techniques have been extensively used for cuff-less, automated estimation of blood pressure because of their inexpensive and effortless acquisition technology compared to other conventional approaches. However, most of the reported PPG-based, generalized BP estimation methods often lack the desired accuracy due to pathophysiological diversity. Moreover, some methods rely on several correction factors, which are not globalized yet and require further investigation. In this paper, a simple and automated systolic (SBP) and diastolic (DBP) blood pressure estimation method is proposed based on patient-specific neural network (NN) modeling. Initially, 15 time-plane PPG features are extracted and after feature selection, only four selected features are used in the NN model for beat-to-beat estimation of SBP and DBP, respectively. The proposed technique also presents reasonable accuracy while used for generalized estimation of BP. Performance of the algorithm is evaluated on 670 records of 50 intensive care unit (ICU) patients taken from MIMIC, MIMIC II and MIMIC Challenge databases. The proposed algorithm exhibits high average accuracy with (mean[Formula: see text][Formula: see text][Formula: see text]SD) of the estimated SBP as ([Formula: see text]) mmHg and DBP as ([Formula: see text]) mmHg. Compared to the other generalized models, the use of patient-specific approach eliminates the necessity of individual correction factors, thus increasing the robustness, accuracy and potential of the method to be implemented in personal healthcare applications.

Online Parameter Estimation of the Lankarani–Nikravesh Contact Force Model Using Two Different Methods

2016 ◽  
Vol 13 (04) ◽  
pp. 1641017 ◽  
Author(s):  
Jia Ma ◽  
Linfang Qian ◽  
Guangsong Chen
Keyword(s):  
Parameter Estimation ◽  
Contact Force ◽  
Unscented Kalman Filter ◽  
Initial Conditions ◽  
Linear Method ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Force Model ◽  
Contact Force Model ◽  
Current Contact

Current contact force models are expected to be used under different environments, where the dynamical parameter estimation becomes an important issue in accurately analyzing the overall behavior of mechanical system especially for complex contact situations. In recent years, a significant amount of research has been carried out in relation to the nonlinear inverse problems, which can be generally divided into two categories: one is the linear method and the other can be called the nonlinear one. In this paper, both methods are described and compared. The linear method is based on the Taylor series and Exponentially Weighted Recursive Least Squares (EWRLS) estimation method. Whereas, the core of the nonlinear one is the Unscented Kalman Filter (UKF). The Lankarani–Nikravesh (L–N) contact force model is employed to quantify the contact effect in this paper, since it is proven to be more consistent with the physics of contact. Some simulation examples are employed to evaluate the convergence sensitivity of these two methods to parameter initial conditions. And the comparisons under the same simulation condition between both methods indicate that the nonlinear one is more robust and can converge faster than the linear one.

Lower Bound on the Accuracy of Parameter Estimation Methods for Linear Sensorimotor Synchronization Models

2015 ◽  
Vol 3 (1-2) ◽  
pp. 32-51 ◽  
Author(s):  
Nori Jacoby ◽  
Peter E. Keller ◽  
Bruno H. Repp ◽  
Merav Ahissar ◽  
Naftali Tishby
Keyword(s):  
Parameter Estimation ◽  
Lower Bound ◽  
Computational Models ◽  
Linear Models ◽  
Estimation Error ◽  
Explanatory Power ◽  
Estimation Method ◽  
Sensorimotor Synchronization ◽  
Estimation Methods ◽  
Unbiased Estimation

The mechanisms that support sensorimotor synchronization — that is, the temporal coordination of movement with an external rhythm — are often investigated using linear computational models. The main method used for estimating the parameters of this type of model was established in the seminal work of Vorberg and Schulze (2002), and is based on fitting the model to the observed auto-covariance function of asynchronies between movements and pacing events. Vorberg and Schulze also identified the problem of parameter interdependence, namely, that different sets of parameters might yield almost identical fits, and therefore the estimation method cannot determine the parameters uniquely. This problem results in a large estimation error and bias, thereby limiting the explanatory power of existing linear models of sensorimotor synchronization. We present a mathematical analysis of the parameter interdependence problem. By applying the Cramér–Rao lower bound, a general lower bound limiting the accuracy of any parameter estimation procedure, we prove that the mathematical structure of the linear models used in the literature determines that this problem cannot be resolved by any unbiased estimation method without adopting further assumptions. We then show that adding a simple and empirically justified constraint on the parameter space — assuming a relationship between the variances of the noise terms in the model — resolves the problem. In a follow-up paper in this volume, we present a novel estimation technique that uses this constraint in conjunction with matrix algebra to reliably estimate the parameters of almost all linear models used in the literature.

scholarly journals Neural network based dynamic model and gust identification system for the Jetstream G-NFLA

2016 ◽  
Vol 231 (6) ◽  
pp. 1138-1153 ◽  
Author(s):  
Aristeidis Antonakis ◽  
Mudassir Lone ◽  
Alastair Cooke
Keyword(s):  
Neural Network ◽  
Dynamic Model ◽  
Linear Models ◽  
Identification System ◽  
Aerospace Vehicles ◽  
Flight Data ◽  
Longitudinal Dynamic ◽  
Data Analysis Procedure ◽  
Real World Applications ◽  
Established Technique

Artificial neural networks are an established technique for constructing non-linear models of multi-input-multi-output systems based on sets of observations. In terms of aerospace vehicle modelling, however, these are currently restricted to either unmanned applications or simulations, despite the fact that large amounts of flight data are typically recorded and kept for reasons of safety and maintenance. In this paper, a methodology for constructing practical models of aerospace vehicles based on available flight data recordings from the vehicles’ operational use is proposed and applied on the Jetstream G-NFLA aircraft. This includes a data analysis procedure to assess the suitability of the available flight databases and a neural network based approach for modelling. In this context, a database of recorded landings of the Jetstream G-NFLA, normally kept as part of a routine maintenance procedure, is used to form training datasets for two separate applications. A neural network based longitudinal dynamic model and gust identification system are constructed and tested against real flight data. Results indicate that in both cases, the resulting models’ predictions achieve a level of accuracy that allows them to be used as a basis for practical real-world applications.

scholarly journals Online Capacity Estimation for Lithium-Ion Batteries Based on Semi-Supervised Convolutional Neural Network

2021 ◽  
Vol 12 (4) ◽  
pp. 256
Author(s):  
Yi Wu ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Capacity Estimation ◽  
Practical Applications

Accurate capacity estimation can ensure the safe and reliable operation of lithium-ion batteries in practical applications. Recently, deep learning-based capacity estimation methods have demonstrated impressive advances. However, such methods suffer from limited labeled data for training, i.e., the capacity ground-truth of lithium-ion batteries. A capacity estimation method is proposed based on a semi-supervised convolutional neural network (SS-CNN). This method can automatically extract features from battery partial-charge information for capacity estimation. Furthermore, a semi-supervised training strategy is developed to take advantage of the extra unlabeled sample, which can improve the generalization of the model and the accuracy of capacity estimation even in the presence of limited labeled data. Compared with artificial neural networks and convolutional neural networks, the proposed method is demonstrated to improve capacity estimation accuracy.

scholarly journals Multitarget Parameter Estimation of Monopulse Radar Based on RJ-MCMC Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jian Gong ◽  
Yiduo Guo ◽  
Hui Yuan ◽  
Qun Wan
Keyword(s):  
Parameter Estimation ◽  
Target Location ◽  
Estimation Method ◽  
Observation Time ◽  
Joint Estimation ◽  
Estimation Methods ◽  
Mcmc Algorithm ◽  
Target Number ◽  
Real Time Processing ◽  
Location Parameters

A multiple parameter estimation method based on RJ-MCMC for multiple nondiscernible targets is proposed in this paper. Different from the traditional estimation methods, the proposed method can simultaneously complete the joint estimation of the target number and the target location parameters. More importantly, the method proposed in this chapter is applicable to many situations with different power and nondistinguishable target. The simulation results show that the method proposed in this chapter requires less observation time to obtain similar and even better estimation performance than the ML-MDL method, which is of great significance for real-time processing.

scholarly journals Unification of Internal Resistance Estimation Methods for Li-Ion Batteries Using Hysteresis-Free Equivalent Circuit Models

Batteries ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 32
Author(s):  
S M Rakiul Islam ◽  
Sung-Yeul Park ◽  
Balakumar Balasingam
Keyword(s):  
Parameter Estimation ◽  
Equivalent Circuit ◽  
Electrochemical Impedance ◽  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Parameter Extraction ◽  
Internal Resistance ◽  
Least Square ◽  
Estimation Methods ◽  
Li Ion

Internal resistance is one of the important parameters in the Li-Ion battery. This paper identifies it using two different methods: electrochemical impedance spectroscopy (EIS) and parameter estimation based on equivalent circuit model (ECM). Comparing internal resistance, the conventional parameter estimation method yields a different value than EIS. Therefore, a hysteresis-free parameter identification method based on ECM is proposed. The proposed technique separates hysteresis resistance from the effective resistance. It precisely estimated actual internal resistance, which matches the internal resistance obtained from EIS. In addition, state of charge, open circuit voltage, and different internal equivalent circuit components were identified. The least square method was used to identify the parameters based on ECM. A parameter extraction algorithm to interpret impedance spectrum obtained from the EIS. The algorithm is based on the properties of Nyquist plot, phasor algebra, and resonances. Experiments were conducted using a cellphone pouch battery and a cylindrical 18650 battery.

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