Disturbance compensation for iterative control of suspension durability test rigs

Muller, Tino; Vögele, Ulrich; Endisch, Christian

doi:10.1109/aim.2016.7577011

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…In Figure 2, a McPherson suspension is mounted on the fixture and the hydraulic test rig is controlled by the PID controller [21]. In practice, the PID controller is usual in the vehicle durability testing [6], [14]. Due to the frequent replacement of specimen in the durability testing, the PID controller is more suitable and easy to tune.…”

Section: Experimental Multi-axial Suspension Test Rigmentioning

confidence: 99%

“…In practice, the physical system model G cannot be acquired exactly so the experimentally identified FRF matrix G can substitute for it. Based on Equation 9, the drive can be updated (14) It is obvious that the classical offline ILC method is a particular example of optimization problem.…”

Section: The Offline Ilc Strategy With Quasi-newton Optimization mentioning

confidence: 99%

“…The NARX model-based ILC also has been presented in several literature [17]- [20]. The parametric model can use shorter measurement data to be identified, but it is laborious to choose a reasonable order and structure for each part of the multivariable model [14], [15]. The parametric model based ILC method is more suited to smaller numbers of channels [10] to be a supplement to the classical ILC.…”

Section: Introductionmentioning

confidence: 99%

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Modified Quasi-Newton Optimization Algorithm-Based Iterative Learning Control for Multi-Axial Road Durability Test Rig

et al. 2019

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The iterative learning control (ILC) based on the linear frequency-domain model has been employed to replicate the road conditions for the vehicle durability testing in the laboratory. Generally, the vehicle and the multi-axial hydraulic test rig behave strong nonlinearities, which requires a large number of iterations to correct the tracking error. Hence, the process of drive file (i.e., the input signals which drive the actuators of the test rig) generation is time-lengthy and tedious. A method that combines the ILC with the Quasi-Newton algorithm over the complex space (QNILC) is developed to speed up the drive file construction for the multi-axial vibration test rig. The impedance matrix can be updated with Broyden's method to reduce the modeling errors and make the iteration more robust. An auxiliary estimating loop is inserted into the iteration process to attain an optimal learning gain. The convergence of the proposed method has been proven to be monotonic. This approach is validated through simulation, where the target signals are the real-life spindle forces gathered from the wheel force transducer. The simulation results demonstrate that the QNILC can improve the convergence rate and increase the tracking accuracy than the current offline ILC. The QNILC reduces the iteration number from nine down to five to converge to the desirable index compared with the offline ILC using gain 0.5. The new method based on the optimization algorithm can extend to other repetitive tracking processes. INDEX TERMS Road durability testing, iterative learning control, Broyden's method, optimal learning gain, the multi-axial road test rig.

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Section: Experimental Multi-axial Suspension Test Rigmentioning

confidence: 99%

Section: The Offline Ilc Strategy With Quasi-newton Optimization mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modified Quasi-Newton Optimization Algorithm-Based Iterative Learning Control for Multi-Axial Road Durability Test Rig

et al. 2019

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“…11 Moten et al 12 make use of adaptive inverse plant modeling technique to identify the system where the length of finite impulse response filters must be sufficient. Muller et al [13][14][15] identify directly the time domain inverse model of multiaxial test rig without inverting the model like traditional method and the structure of model is optimized based on the correlation of the outputs leading to more control stability. Although parametric time model can achieve comparable accuracy with shorter measurement data, an order has to be selected for each part of the multivariable model which is onerous.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Cong

Yang

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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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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An estimation based iterative learning control approach for cross-coupled vehicle test systems

Muller

Endisch

2017

2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

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Disturbance compensation for iterative control of suspension durability test rigs

Cited by 3 publications

References 16 publications

Modified Quasi-Newton Optimization Algorithm-Based Iterative Learning Control for Multi-Axial Road Durability Test Rig

Modified Quasi-Newton Optimization Algorithm-Based Iterative Learning Control for Multi-Axial Road Durability Test Rig

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

An estimation based iterative learning control approach for cross-coupled vehicle test systems

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