A Universal Gains Selection Method for Speed Observers of Induction Machine

Properties of state observers depend on proper gains selection. Each method of state estimation may require the implementation of specific techniques of finding those gains. The aim of this study is to propose a universal method of automatic gains selection and perform its verification on an induction machine speed observer. The method utilizes a genetic algorithm with fitness function which is directly based on the impulse response of the observer. System identification using least-squares estimation is implemented to determine the dynamic properties of the observer based on the estimation error signal. The influence of sampling time as well as signal length on the system identification has been studied. The results of gains selection using the proposed method have been compared with results obtained using the approach based on the placement of the poles of linearized estimation error equations. The introduced method delivers results comparable with analytical methods and does not require prior preparation specific to the implemented speed observer, such as linearization.

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Genetic Algorithm Approach for Gains Selection of Induction Machine Extended Speed Observer

Energies ◽

10.3390/en13184632 ◽

2020 ◽

Vol 13 (18) ◽

pp. 4632

Author(s):

Daniel Wachowiak

Keyword(s):

Nonlinear Equations ◽

Dynamic Properties ◽

Fitness Function ◽

Experimental Studies ◽

Induction Machine ◽

Loop Control ◽

Rotation Direction ◽

Machine Speed ◽

Poles Placement ◽

Selection Of

The subject of this paper is gains selection of an extended induction machine speed observer. A high number of gains makes manual gains selection difficult and due to nonlinear equations of the observer, well-known methods of gains selection for linear systems cannot be applied. A method based on genetic algorithms has been proposed instead. Such an approach requires multiple fitness function calls; therefore, using a quality index based on simulations makes gains selection a time-consuming process. To find a fitness function that evaluates, in a short time, quality indices based on poles placement have been proposed. As the observer is nonlinear, equations describing the observer dynamics have been linearized. The relationship between poles placement and real dynamic properties has been shown. A series of studies has been performed to investigate the influence of the operating point of the machine on the dynamics of the observer. It has been proven that rotor speed has a significant impact on the placement of the poles and the observer may lose stability after a rotation direction change. A method of gains modification to maintain symmetrical properties of the observer for both directions has been presented. Experimental studies of the observer during machine reverse in the open and closed-loop control system have been performed. The results show that the observer can be implemented in a sensorless drive, using the proposed gains selection method.

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Optimal Design and Control of a Slider-Crank Mechanism System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.487.608 ◽

2012 ◽

Vol 487 ◽

pp. 608-612 ◽

Cited By ~ 1

Author(s):

Chih Cheng Kao

Keyword(s):

Particle Swarm Optimization ◽

System Identification ◽

Optimal Design ◽

Fitness Function ◽

Local Optimum ◽

Swarm Optimization ◽

Modified Particle Swarm Optimization ◽

Pm Synchronous Motor ◽

And Control ◽

Crank Mechanism

This paper mainly proposes an efficient modified particle swarm optimization (MPSO) method, to identify a slider-crank mechanism driven by a field-oriented PM synchronous motor. The parameters of many industrial machines are difficult to obtain if these machines cannot be taken apart. In system identification, we adopt the MPSO method to find parameters of the slider-crank mechanism. This new algorithm is added with “distance” term in the traditional PSO’s fitness function to avoid converging to a local optimum. Finally, the comparisons of numerical simulations and experimental results prove that the MPSO identification method for the slider-crank mechanism is feasible.

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Multi-phase Induction Machine Speed Estimation Method Using Rotor Slot Harmonics

IEEE EUROCON 2019 -18th International Conference on Smart Technologies ◽

10.1109/eurocon.2019.8861970 ◽

2019 ◽

Author(s):

Ivana Isakov ◽

Veran Vasic ◽

Dejan Jerkan ◽

Dejan Reljic ◽

Ivan Todorovic

Keyword(s):

Induction Machine ◽

Estimation Method ◽

Speed Estimation ◽

Machine Speed ◽

Multi Phase

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A Mechatronic Motor-Table System Identification Based on an Energetics Fitness Function

IEEE/ASME Transactions on Mechatronics ◽

10.1109/tmech.2017.2739122 ◽

2017 ◽

Vol 22 (5) ◽

pp. 2288-2295 ◽

Cited By ~ 3

Author(s):

Kun-Yung Chen ◽

Rong-Fong Fung

Keyword(s):

System Identification ◽

Fitness Function

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State Observation for Lipschitz Nonlinear Dynamical Systems Based on Lyapunov Functions and Functionals

Mathematics ◽

10.3390/math8091424 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1424 ◽

Cited By ~ 2

Author(s):

Angelo Alessandri ◽

Patrizia Bagnerini ◽

Roberto Cianci

Keyword(s):

Dynamical Systems ◽

Asymptotic Stability ◽

Lyapunov Functions ◽

Nonlinear Dynamical Systems ◽

Estimation Error ◽

Stability Conditions ◽

Nonlinear Dynamical ◽

State Observation ◽

State Observers ◽

The Stability

State observers for systems having Lipschitz nonlinearities are considered for what concerns the stability of the estimation error by means of a decomposition of the dynamics of the error into the cascade of two systems. First, conditions are established in order to guarantee the asymptotic stability of the estimation error in a noise-free setting. Second, under the effect of system and measurement disturbances regarded as unknown inputs affecting the dynamics of the error, the proposed observers provide an estimation error that is input-to-state stable with respect to these disturbances. Lyapunov functions and functionals are adopted to prove such results. Third, simulations are shown to confirm the theoretical achievements and the effectiveness of the stability conditions we have established.

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Robust Sensor Placement to Measurement Noise for Structural Dynamic Systems

Solid State Phenomena ◽

10.4028/www.scientific.net/ssp.120.247 ◽

2007 ◽

Vol 120 ◽

pp. 247-254

Author(s):

Dong Ho Oh ◽

Shiao Hua Chen

Keyword(s):

System Identification ◽

Dynamic Systems ◽

Estimation Error ◽

Sensor Placement ◽

Measurement Noise ◽

Element Analysis ◽

Structural Dynamic ◽

Measurement Selection ◽

Robust Measurements ◽

Damped Systems

Performance of experimental dynamic system identification depends on sensor placement especially when the number of sensor locations is relatively small and measurement noise is significant. We propose a method to select robust measurements based on the estimation error in modal analysis, which is one of the most popular system identification methods for dynamic systems, by defining a measure of the estimation. The measure is developed to deal with various types of sensors and with general damped systems. It is calculated from the eigenvector information obtained by finite element analysis or preliminary experiments and represents the weighted covariance of estimation error induced by measurement noise as well as the orthogonality of the projection matrix which is the corresponding submatrix of eigenvector-matrix by partially measured outputs. Effectiveness of the proposed measurement selection schemes is demonstrated by computer simulations and experiments.

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Test of dynamic closed-loop baroreflex and autoregulatory control of total peripheral resistance in intact and conscious sheep

AJP Heart and Circulatory Physiology ◽

10.1152/ajpheart.00490.2003 ◽

2004 ◽

Vol 287 (5) ◽

pp. H2274-H2286 ◽

Cited By ~ 7

Author(s):

Nikolai Aljuri ◽

Robert Marini ◽

Richard J. Cohen

Keyword(s):

System Identification ◽

Cardiovascular System ◽

Total Peripheral Resistance ◽

Closed Loop ◽

Dynamic Properties ◽

Peripheral Resistance ◽

Right Atrial ◽

Individual Subject ◽

System Identification Method ◽

Identification Method

This is the first study able to examine and delineate the actual actions of the physiological mechanisms responsible for the dynamic couplings between cardiac output (CO), arterial pressure (Pa), right atrial pressure (PRA), and total peripheral resistance (TPR) in an individual subject without altering the underlying regulatory mechanisms. Eight conscious male sheep were used, where both types of baroreceptors were independently exposed to simultaneous beat-to-beat pressure perturbations under intact closed-loop conditions while CO, Pa, PRA, and TPR were measured. We applied the cardiovascular system identification method proposed in a companion paper ( 4 ) to quantitatively characterize the dynamic closed-loop transfer relations CO→Pa, PRA→Pa, Pa→TPR, and PRA→TPR from the measured signals. To validate the dynamic properties of the estimated transfer relations, the essential parts of the linear dynamics of the model were independently and comprehensively evaluated via error model cross-validation, and the overall model's steady-state behavior was compared with a separate random effects regression approach. In addition to numerous physiological findings, we found that the cardiovascular system identification results were exceptionally consistent with the analytically derived solutions previously discussed in Ref. 4 . In conclusion, this study presents the first time validation of a cardiovascular system identification method by means of experimentally acquired animal data in the intact and conscious animal and offers a set of powerful quantitative tools essential to advancing our knowledge of cardiovascular regulatory physiology.

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