An alternative update formula for non-linear model-based iterative learning control

Eksteen, Johannes Jacobus Arnoldi; Heyns, P.S.

doi:10.1080/17415977.2015.1088536

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…Xu et al (2013) combine moving horizon idea of MPC and constrained Gauss- Newton method to formulate nonlinear inverse model-based ILC. Eksteen and Heyns (2016) present an alternative ILC method for nonlinear inverse model based ILC. For this purpose, the nonlinear system is identified as NARX model and use an iterative and numerical inversion method in order to get inverse model.…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy iterative learning control for 4-poster test rig

Dursun

Cansever

Üstoğlu

2020

Transactions of the Institute of Measurement and Control

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In this paper, a new control method is presented for the 4-poster test systems. The primary aim of the paper is to improve the convergence speed and decrease the error rate for model-based iterative learning control (ILC), a widely used method as a tracking control. First, the dynamic equations of the system are generated, and the control problem is formulated. Then, an inverse model of the system is established directly through the adaptive neuro-fuzzy inference system (ANFIS) with auxiliary parameter (piston position) as a serial combination of two sub-models. In order to construct a neuro-fuzzy ILC (NFILC) structure, these sub-models are integrated into the neuro-fuzzy inverse controller (NFIC). Because of this new structure, the modified ILC rule has two layers. In the first layer, the controlled parameter, namely, the acceleration is iterated, whereas, in the second layer, the auxiliary parameter is iterated. The outcomes of the proposed control method are scrutinized by testing through a numerical simulation. Finally, it is demonstrated that the modified ILC rule dramatically increase the convergence speed and reduce the final error rate.

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Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy iterative learning control for 4-poster test rig

Dursun

Cansever

Üstoğlu

2020

Transactions of the Institute of Measurement and Control

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“…The Adaptive Inverse Plant Modeling (AIPM) technique is used by Moten et al [16] to identify the system. 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].…”

Section: Introductionmentioning

confidence: 99%

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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“…A nonlinear inverse model-based ILC is also presented to compensate the dynamics of test rig. 19 However, the nonlinear model requires numerical optimization and is still computationally infeasible for the long time signal.…”

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 alternative update formula for non-linear model-based iterative learning control

Cited by 8 publications

References 18 publications

Neuro-fuzzy iterative learning control for 4-poster test rig

Neuro-fuzzy iterative learning control for 4-poster 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

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