scholarly journals On-line identification methods as part of the tunnel ventilation control algorithm

2021 ◽  
Vol 55 ◽  
pp. 961-968
Author(s):  
Roman Michalík ◽  
Jozef Hrbček ◽  
Vojtech Šimák
Keyword(s):  
Control Algorithm ◽  
Ventilation Control ◽  
Tunnel Ventilation ◽  
Identification Methods ◽  
Line Identification ◽  
On Line

Autonomous Vibration Suppression Using On-Line Pole-Zero Identification

2000 ◽  
Author(s):  
Mark McEver ◽  
Donald J. Leo
Keyword(s):  
Control Algorithm ◽  
Vibration Suppression ◽  
Resonant Mode ◽  
Pole Placement ◽  
Flexible Beam ◽  
Optimal Parameters ◽  
Equivalent Damping ◽  
Line Identification ◽  
Dc Gain ◽  
On Line

Abstract An on-line identification and control algorithm is developed based on the properties of collocated sensing and actuation. The feedback control law consists of second-order compensators that achieve equivalent damping in both the filter dynamics and resonant structural dynamics, thus maximizing the damping in the structure and controller. Optimal design of the feedback compensator is obtained using a pole placement algorithm applied to a single, undamped resonant mode. Numerical analysis indicates that multiple modes and structural damping do not appreciably change the damping achieved using the optimal parameters. The pole placement analysis demonstrates that only the pole-zero spacing and DC gain of the collocated transfer function are required to choose the optimal parameters. An on-line identification procedure is developed that sequentially determines the DC gain and pole-zero spacing and automatically designs the feedback compensator. This forms the basis for the autonomous control algorithm. Experimental results on a flexible beam demonstrate that the procedure can accurately identify the pole-zero spacing and automatically design the feedback compensator. A fivefold increase in damping is achieved in the first mode and a twofold increase in damping is achieved in the second mode. Discrepancies between predicted and measured damping are attributed to phase lags due to signal conditioning and low-pass filtering of the sensor signal.

On-line identification methods

1980 ◽  
Vol 7 (1) ◽  
pp. 35-43
Author(s):  
Naim A. Kheir ◽  
Donald Sutherlin
Keyword(s):  
Identification Methods ◽  
Line Identification ◽  
On Line

scholarly journals Induction Machine On-Line Parameter Identification for Resource-Constrained Microcontrollers Based on Steady-State Voltage Model

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1981
Author(s):  
Tomas Kostal ◽  
Pavel Kobrle
Keyword(s):  
Control Strategy ◽  
Manufacturing Industry ◽  
Dynamic Properties ◽  
Low Cost ◽  
Induction Machine ◽  
Resource Constrained ◽  
Identification Methods ◽  
Rotor Resistance ◽  
Line Identification ◽  
On Line

This paper presents a new, computationally modest on-line identification method for the simultaneous estimation of the rotor resistance and magnetizing inductance of an induction machine suitable for electric drives that use an indirect field-oriented control strategy (IFOC), and their control hardware is equipped with a resource-constrained microcontroller. Such drives can be found both in the manufacturing industry and railway traction vehicles in the thousands, having either older control hardware that cannot cope with computationally excessive identification methods or being in cost-sensitive applications, thus being equipped with a low-cost microcontroller. IFOC is a very common control strategy for such drives due to its good dynamic properties and comparatively simple implementation. However, it is sensitive to inaccuracies of rotor resistance and magnetizing inductance. These two parameters change during the operation of the drive, being influenced by the temperature, frequency, and saturation of the magnetic circuit. Improper values of parameters in the controller can degrade the performance of IFOC, resulting in slower acceleration or unnecessary oversaturation of the machine. Respecting these changes can therefore bring significant benefits such as the good dynamic properties of the drive, which can shorten operations in the manufacturing industry or travel times of vehicles. A number of on-line identification methods for monitoring the parameter changes have been published so far, but the majority of them are demanding on microcontroller time or its memory. The proposed method, on the contrary, is comparatively simple and thus easy for implementation with low requirements to the microcontroller. Therefore, it is suitable for both upgrades of existing drives or new low-cost applications. Derivation of the method from the mathematical model and the final algorithm for the microcontroller are presented. The performance of the proposed method is validated with experimental results obtained with a 3.5 kW induction machine drive with an industrial microcontroller during a warming test and under various loads and frequencies.

On-Line Identification of Process Model Based on Swarm Intelligence Technology

2011 ◽  
Vol 403-408 ◽  
pp. 3216-3219 ◽  
Author(s):  
Li Ting Cao ◽  
Qi Bing Jin ◽  
Tong Shun Fan ◽  
Wei Su
Keyword(s):  
Process Model ◽  
Closed Loop ◽  
Estimation Algorithm ◽  
System Optimization ◽  
Step Response ◽  
Identification Methods ◽  
Identification Method ◽  
Line Identification ◽  
On Line ◽  
Intelligent Technology

On-line identification problem of process model was discussed in this paper, which use warm intelligent technology. An on-line identification method based on HPSO-Rosenbrock parameter estimation algorithm is proposed to solve the problem that traditional identification methods cannot be used in continuous-time systems on closed-loop step response conditions. This identification method is a combined method of a modified PSO and Rosenbrock which can make full use of global search ability of PSO and local search ability of Rosenbrock. Identification results of HPSO-Rosenbrock algorithm were made and compared with the other identification methods. The simulation and compare results show that the on-line identification method proposed in this paper is an approximate unbiased and effective identification method. This method can be successfully applied to closed-loop identification under secious noise and big dead-time object which provides a new idea for system optimization and advanced control.

Autonomous Vibration Suppression Using On-Line Pole-Zero Identification

2001 ◽  
Vol 123 (4) ◽  
pp. 487-495 ◽  
Author(s):  
Mark McEver ◽  
Donald J. Leo
Keyword(s):  
Control Algorithm ◽  
Vibration Suppression ◽  
Resonant Mode ◽  
Pole Placement ◽  
Flexible Beam ◽  
Optimal Parameters ◽  
Equivalent Damping ◽  
Line Identification ◽  
Dc Gain ◽  
On Line

An on-line identification and control algorithm is developed based on the properties of collocated sensing and actuation. The feedback control law consists of second-order compensators that achieve equivalent damping in both the filter dynamics and resonant structural dynamics, thus maximizing the damping in the structure and controller. Optimal design of the feedback compensator is obtained using a pole placement algorithm applied to a single, undamped resonant mode. Numerical analysis indicates that multiple modes and structural damping do not appreciably change the damping achieved using the optimal parameters. The pole placement analysis demonstrates that only the pole-zero spacing and DC gain of the collocated transfer function are required to choose the optimal parameters. An on-line identification procedure is developed that sequentially determines the DC gain and pole-zero spacing and automatically designs the feedback compensator. This forms the basis for the autonomous control algorithm. Experimental results on a flexible beam demonstrate that the procedure can accurately identify the pole-zero spacing and automatically design the feedback compensator. A fivefold increase in damping is achieved in the first mode and a twofold increase in damping is achieved in the second mode. Discrepancies between predicted and measured damping are attributed to phase lags due to signal conditioning and low-pass filtering of the sensor signal.

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