Geometric model-based joint angle selection criterion for force parameter identification & Decoupling control method of position and posture in shaft-hole assembly

A novel decoupling control method based on neural networks inverse system is presented in this paper for a bearingless synchronous reluctance motor (BSRM) possessing the characteristics of multi-input-multi-output, nonlinearity, and strong coupling. The dynamic mathematical models are built, which are verified to be invertible. A controller based on neural network inverse is designed, which decouples the original nonlinear system to two linear position subsystems and an angular velocity subsystem. Furthermore, the linear control theory is applied to closed-loop synthesis to meet the desired performance. Simulation and experiment results show that the presented neural networks inverse control strategy can realize the dynamic decoupling of BSRM, and that the control system has fine dynamic and static performance.

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mparison of model-based identification methods for reserve-capacity assessment of existing bridges

IABSE Conference, Copenhagen 2018: Engineering the Past, to Meet the Needs of the Future ◽

10.2749/copenhagen.2018.393 ◽

2018 ◽

Author(s):

Marco Proverbio ◽

François-Xavier Favre ◽

Ian F. C. Smith

Keyword(s):

Parameter Identification ◽

Structural Identification ◽

Domain Model ◽

Reserve Capacity ◽

Steel Bridge ◽

Model Based ◽

Wrong Identification ◽

Alternative Methodology ◽

Existing Bridges ◽

Residual Minimization

The goal of model-based structural identification is to find suitable values of parameters that affect structure behaviour. To this end, measurements are often compared with predictions of finiteelement models. Although residual minimization (RM) is a prominent methodology for structural identification, it provides wrong parameter identification when flawed model classes are adopted. Error-domain model falsification (EDMF) is an alternative methodology that helps identify candidate models – models that are compatible with behaviour measurements – among an initial model population. This study focuses on the comparison between RM and EDMF for the structural identification of a steel bridge in Exeter (UK). Advantages and limitations of both methodologies are discussed with reference to parameter identification and prognosis tasks such as quantification of reserve capacity. Results show that the employment of RM may lead to wrong identification and unsafe estimations of reserve capacity.

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Nonlinear observer based sliding mode control and oxygen fraction estimation for diesel engine

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331217700242 ◽

2017 ◽

Vol 40 (7) ◽

pp. 2227-2239 ◽

Cited By ~ 1

Author(s):

Haoping Wang ◽

Qiankun Qu ◽

Yang Tian

Keyword(s):

Diesel Engine ◽

Sliding Mode Control ◽

Oxygen Concentration ◽

Control Method ◽

Sliding Mode ◽

Observer Design ◽

Nonlinear Observer ◽

Linear Matrix ◽

Model Based ◽

Mode Control

In this paper, a nonlinear observer based sliding mode control (NOSMC) approach for air-path and a model-based observer for oxygen concentration in the diesel engine equipped with a variable geometry turbocharger and exhaust gas recirculation is introduced. We propose a less conservative observer design technique for Lipschitz nonlinear systems using Ricatti equations. The observer gains are obtained by solving the linear matrix inequality (LMI). Then a robust nonlinear control method, sliding mode control is applied for the states of intake and exhaust manifold pressure and compressor mass flow rate for the sake of the minimization of emissions. The proposed NOSMC controller is applied on a mean value model of turbocharged diesel engine. Besides this, a model-based observer is developed to estimate the oxygen concentration in the intake and exhaust manifolds owing to its significance in reducing emissions of diesel engines. The validation and efficiency of the proposed method are demonstrated by AMESim and Matlab/Simulink co-simulation results.

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Model-based ankle joint angle tracing by cuff electrode recordings of peroneal and tibial nerves

Medical & Biological Engineering & Computing ◽

10.1007/s11517-007-0162-5 ◽

2007 ◽

Vol 45 (4) ◽

pp. 375-385 ◽

Cited By ~ 8

Author(s):

Chou-Ching K. Lin ◽

Ming-Shaung Ju ◽

Hang-Shing Cheng

Keyword(s):

Ankle Joint ◽

Joint Angle ◽

Model Based ◽

Cuff Electrode

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Parameter identification for fractional fractal diffusion model based on experimental data

Chaos An Interdisciplinary Journal of Nonlinear Science ◽

10.1063/1.5111832 ◽

2019 ◽

Vol 29 (8) ◽

pp. 083134 ◽

Cited By ~ 2

Author(s):

Xiu Yang ◽

Xiaoyun Jiang ◽

Jianhong Kang

Keyword(s):

Experimental Data ◽

Parameter Identification ◽

Diffusion Model ◽

Model Based

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ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM

INMATEH Agricultural Engineering ◽

10.35633/inmateh-61-03 ◽

2020 ◽

Vol 61 (2) ◽

pp. 25-34 ◽

Cited By ~ 2

Author(s):

Yibo Li ◽

Hang Li ◽

Xiaonan Guo

Keyword(s):

Parameter Identification ◽

Measured Data ◽

Model Parameters ◽

Identification Algorithm ◽

Output Data ◽

Swarm Optimization ◽

Model Based ◽

Simulation Results ◽

Filtering Effect ◽

Ipso Algorithm

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

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