scholarly journals Unbalance Compensation of a Full Scale Test Rig Designed for HTR-10GT: A Frequency-Domain Approach Based on Iterative Learning Control

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Ying He ◽  
Lei Shi ◽  
Zhengang Shi ◽  
Zhe Sun
Keyword(s):  
Frequency Domain ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Magnetic Bearing ◽  
Iterative Learning ◽  
Full Scale ◽  
Full Scale Test ◽  
Test Rig ◽  
Operation Speed ◽  
Scale Test

Unbalance vibrations are crucial problems in heavy rotational machinery, especially for the systems with high operation speed, like turbine machinery. For the program of 10 MW High Temperature gas-cooled Reactor with direct Gas-Turbine cycle (HTR-10GT), the rated operation speed of the turbine system is 15000 RPM which is beyond the second bending frequency. In that case, even a small residual mass will lead to large unbalance vibrations. Thus, it is of great significance to study balancing methods for the system. As the turbine rotor is designed to be suspended by active magnetic bearings (AMBs), unbalance compensation could be achieved by adequate control strategies. In the paper, unbalance compensation for the Multi-Input and Multi-Output (MIMO) active magnetic bearing (AMB) system using frequency-domain iterative learning control (ILC) is analyzed. Based on the analysis, an ILC controller for unbalance compensation of the full scale test rig, which is designed for the rotor and AMBs in HTR-10GT, is designed. Simulation results are reported which show the efficiency of the ILC controller for attenuating the unbalance vibration of the full scale test rig. This research can offer valuable design criterion for unbalance compensation of the turbine machinery in HTR-10GT.

scholarly journals Application of the Dynamic Iterative Learning Control to the Heteroplanar Active Magnetic Bearing

2020 ◽  
Vol 53 (2) ◽  
pp. 1511-1516
Author(s):  
Lukasz Hladowski ◽  
Arkadiusz Mystkowski ◽  
Krzysztof Galkowski ◽  
Eric Rogers ◽  
Bing Chu
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Magnetic Bearing ◽  
Iterative Learning ◽  
Active Magnetic Bearing

Iterative learning control with complex conjugate gradient optimization algorithm for multiaxial road durability test rig

2018 ◽  
Vol 233 (7) ◽  
pp. 2349-2360 ◽  
Author(s):  
Xiao Wang ◽  
Dacheng Cong ◽  
Zhidong Yang ◽  
Shengjie Xu ◽  
Junwei Han
Keyword(s):  
Conjugate Gradient ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Gradient Algorithm ◽  
Complex Conjugate ◽  
Test Rig ◽  
Learning Gain ◽  
Durability Test ◽  
Optimal Learning

Service load replication performed on multiaxial hydraulic test rigs has been widely applied in automotive engineering for durability testing in laboratory. The frequency-domain off-line iterative learning control is used to generate the desired drive file, i.e. the input signals which drive the actuators of the test rig. During the iterations an experimentally identified linear frequency-domain system model is used. As the durability test rig and the specimen under test have a strong nonlinear behavior, a large number of iterations are needed to generate the drive file. This process will cause premature deterioration to the specimen unavoidably. In order to accelerate drive file construction, a method embedding complex conjugate gradient algorithm into the conventional off-line iterative learning control is proposed to reproduce the loading conditions. The basic principle and monotone convergence of the method is presented. The drive signal is updated according to the complex conjugate gradient and the optimal learning gain. An optimal learning gain can be obtained by an estimate loop. Finally, simulations are carried out based on the identified parameter model of a real spindle-coupled multiaxial test rig. With real-life spindle forces from the wheel force transducer in the proving ground test to be replicated, the simulation results indicate that the proposed conventional off-line iterative learning control with complex conjugate gradient algorithm allows generation of drive file more rapidly and precisely compared with the state-of-the-art off-line iterative learning control. Few have been done about the proposed method before. The new method is not limited to the durability testing and can be extended to other systems where repetitive tracking task is required.

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