scholarly journals Comparing Methods of DC Motor Control for UUVs

2021 ◽  
Vol 11 (11) ◽  
pp. 4972
Author(s):  
Rohan Shah ◽  
Timothy Sands
Keyword(s):  
Artificial Intelligence ◽  
Least Squares ◽  
Performance Metrics ◽  
Recursive Least Squares ◽  
Estimation Methods ◽  
Unmanned Underwater Vehicles ◽  
Model Following ◽  
Dc Motor Control ◽  
The Difference ◽  
Parameter Estimation Methods

Adaptive and learning methods are proposed and compared to control DC motors actuating control surfaces of unmanned underwater vehicles. One type of adaption method referred to as model-following is based on algebraic design, and it is analyzed in conjunction with parameter estimation methods such as recursive least squares, extended least squares, and batch least squares. Another approach referred to as deterministic artificial intelligence uses the process dynamics defined by physics to control output to track a necessarily specified autonomous trajectory (sinusoidal versions implemented here). In addition, one instantiation of deterministic artificial intelligence uses 2-norm optimal feedback learning of parameters to modify the control signal, while another instantiation is presented with proportional plus derivative adaption. Model-following and deterministic artificial intelligence are simulated, and respective performance metrics for transient response and input tracking are evaluated and compared. Deterministic artificial intelligence outperformed the model-following approach in minimal peak transient value by a percent range of approximately 2–70%, but model-following achieved at least 29% less error in input tracking than deterministic artificial intelligence. This result is surprising and not in accordance with the recently published literature, and the explanation of the difference is theorized to be efficacy with discretized implementations.

scholarly journals Handling heteroskedasticity in labour demand functions of athletes

2018 ◽  
Vol 4 (2) ◽  
pp. 47-56
Author(s):  
Gábor Rappai ◽  
Diána Ivett Fűrész
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Estimation Methods ◽  
Demand Functions ◽  
Demand Curves ◽  
The Difference ◽  
Sport Events ◽  
Parameter Estimation Methods ◽  
Sport Economics

AbstractBased on previous research it can be stated that modelling sport economics related demand curves (e.g. demand for sport events and athletes) is different from other types of modelling. The difference lies in the fact that some parts of the demand curves are nearly horizontal in case of sport goods and nearly vertical in case of athletes, because the price of sport events is inflexible and at the same time, salaries of top athletes are extremely flexible. This study investigates parameter estimation methods appropriate for the relevant demand functions of sport economics. In this cases the generally used ordinary least squares estimator is less robust, so the weighted least squares estimators are able to handle heteroskedasticity. If the distribution of the variables is known, the Newey-West heteroscedasticity corrected estimates give even stronger results. The empirical study analyses footballer transfer fees in top European leagues and identifies a threshold at which the traditional supply-demand functions are not appropriate. According to the results, word class athletes, in a way, can be considered prestige goods for which demand may be irrational.

scholarly journals Identification of Nonlinear Vibrating Structures: Part I—Formulation

1987 ◽  
Vol 54 (4) ◽  
pp. 918-922 ◽  
Author(s):  
S. F. Masri ◽  
R. K. Miller ◽  
A. F. Saud ◽  
T. K. Caughey
Keyword(s):  
Time Domain ◽  
Parametric Identification ◽  
Companion Paper ◽  
Sufficient Accuracy ◽  
Recursive Least Squares ◽  
Estimation Methods ◽  
Free Oscillations ◽  
Vibrating Structures ◽  
Parameter Estimation Methods ◽  
Identification Techniques

A self-starting multistage, time-domain procedure is presented for the identification of nonlinear, multi-degree-of-freedom systems undergoing free oscillations or subjected to arbitrary direct force excitations and/or nonuniform support motions. Recursive least-squares parameter estimation methods combined with non-parametric identification techniques are used to represent, with sufficient accuracy, the identified system in a form that allows the convenient prediction of its transient response under excitations that differ from the test signals. The utility of this procedure is demonstrated in a companion paper.

Estimation of time-varying respiratory mechanical parameters by recursive least squares

1991 ◽  
Vol 71 (3) ◽  
pp. 1159-1165 ◽  
Author(s):  
A. M. Lauzon ◽  
J. H. Bates
Keyword(s):  
Least Squares ◽  
Time Course ◽  
Weighted Average ◽  
Recursive Least Squares ◽  
Dynamic Hyperinflation ◽  
Time Varying ◽  
Mechanical Parameters ◽  
Tracheal Pressure ◽  
The Difference ◽  
Estimation Of Time

Continuous estimation of time-varying respiratory mechanical parameters is required to fully characterize the time course of bronchoconstriction. To achieve such estimation, we developed an estimator that uses the recursive linear least-squares algorithm to fit the equation Ptr = RV + EV + K to measurements of tracheal pressure (Ptr) and flow (V). The volume (V) is obtained by numerical integration of V. The estimator has a finite memory with length into the past at each point in time that varies inversely with the difference between the current measurement of Ptr and that predicted by the model, to allow the algorithm to track rapidly varying parameters (R, E, and K). V usually exhibits significant drift and must be corrected. Of the several correction methods investigated, subtraction of the recursively weighted average of V before integration to V was found to perform best. The estimator was tested on simulated noisy data where it successfully followed a fivefold increase in R and a twofold increase in E occurring over 10 s. Three dogs and two cats were anesthetized, paralyzed, tracheostomized, and challenged with a bolus of methacholine (approximately 13 mg/kg iv). Increases of 3- to 10-fold were observed in R and 2- to 3-fold in E, beginning within 10–40 s after the bolus injection. In some animals we found that the increase in E occurred more slowly than that in R, which the V signal suggested was due to dynamic hyperinflation of the lungs. These results demonstrate that our recursive estimator is able to track rapid changes in respiratory mechanical parameters during bronchoconstrictor challenge.

Online Parameter Estimation of Hydraulic System Based on Stochastic Gradient Descent

2020 ◽  
Author(s):  
Takashi Yamada ◽  
Matthew Howard
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Hydraulic System ◽  
Gradient Descent ◽  
Estimation Error ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Estimation Methods ◽  
Hydraulic Systems ◽  
Parameter Estimation Methods

Abstract In this paper, offline and online parameter estimation methods for hydraulic systems based on stochastic gradient descent are presented. In contrast to conventional approaches, the proposed methods can estimate any parameter in mathematical models based on multi-step prediction error. These advantages are achieved by calculating the gradient of the multi-step error against the estimated parameters using Lagrange multipliers and the calculus of variations, and by forming differentiable models of hydraulic systems. In experiments on a physical hydraulic system, the proposed methods with three different gradient decent methods (normal gradient descent, Nesterov’s Accelerated Gradient (NAG), and Adam) are compared with conventional least squares. In the offline experiment, the proposed method with NAG achieves estimation error about 95% lower than that of least squares. In online estimation, the proposed method with NAG produces predictive models with about 20% lower error than that of the offline method. These results suggest the proposed method is a practical alternative to more conventional parameter estimation methods.

Laser Beam Jitter Control Using Recursive-Least-Squares Adaptive Filters

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Hyungjoo Yoon ◽  
Brett E. Bateman ◽  
Brij N. Agrawal
Keyword(s):  
Laser Beam ◽  
Least Squares ◽  
Adaptive Filter ◽  
Adaptive Filters ◽  
Reference Signal ◽  
Notch Filter ◽  
Recursive Least Squares ◽  
Control Techniques ◽  
Filter Methods ◽  
The Difference

The primary focus of this research is to develop and implement control schemes for combined broadband and narrowband disturbances to optical beams. The laser beam jitter control testbed developed at the Naval Postgraduate School is used for development of advanced jitter control techniques. First, we propose a least quadratic Gaussian feedback controller with integrator for cases when only the error signal (the difference between the desired and the actual beam positions) is available. An anti-notch filter is also utilized to attenuate a vibrational disturbance with a known frequency. Next, we develop feedforward adaptive filter methods for cases when a reference signal, which is highly correlated with the jitter disturbance, is available. A filtered-X recursive least-squares algorithm with an integrated bias estimator is proposed to deal with a constant bias disturbance. Finally, experimental results are provided to validate and compare the performance of the developed control techniques. The designed adaptive filter has a simple structure but shows good jitter rejection performance, thanks to the use of a reference signal.

scholarly journals Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density

2021 ◽  
Author(s):  
Ahmet Emre Onay ◽  
Emrah Dokur ◽  
Mehmet Kurban
Keyword(s):  
Parameter Estimation ◽  
Wind Speed ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Performance Metrics ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Coefficient Of Determination ◽  
Wind Speed Data ◽  
Parameter Estimation Methods

To install a wind energy conversion system to a region, the wind speed characteristics of that region must be identified. The two-parameter Weibull distribution is highly efficient in modeling wind speed characteristics. In this study, the wind speed data of 32 cities in three different regions of Turkey have been comparatively analysed to estimate Weibull distribution function parameters by the use of three well-known methods (Graphical Method (GM), Maximum Likelihood Method (MLM), Justus Moment Method (JMM)) and three new parameter estimation methods (Energy Pattern Factor Method (EPFM), Wind Energy Intensification Method (WEIM), Power Density Method (PD)) which have been proposed in recent years. Three years of hourly wind speed data of the specified regions have been used. The performance metrics of these analyses have been compared using Wind Energy Error (WEE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). The results have shown that while the PD method has high model performance, the JMM is closely competitive with the MLM. Besides, the wind energy densities that were estimated by using actual data have been compared with the resulting Weibull distribution. It has been clear that the method that has the closest estimation to the actual values is the PD method.

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