Robust control application for a three-axis road simulator

Kim, J. W.; Xuan, Dongji; Kim, Y. B.

doi:10.1007/s12206-010-1104-y

Cited by 13 publications

(14 citation statements)

References 10 publications

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“…Employing equation (36), the RMS error of the conventional and proposed controller after every iteration is calculated and plotted in Figure 15. As shown in the figure, the RMS error of the conventional controller after each iteration is 34.78%, 16.96%, and 14.59%, respectively, while that for the proposed controller is 26.95%, 11.11%, and 9.36%.…”

Section: Experiments Resultsmentioning

confidence: 99%

Time waveform replication for electro-hydraulic shaking table incorporating off-line iterative learning control and modified internal model control

Shen

Zhu

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a combined control strategy incorporating off-line iterative learning control and modified internal model control is proposed for improving the time waveform replication performance of electro-hydraulic shaking table. To reduce the modeling error between the estimated inverse model and the actual system, a modified internal model control strategy is first utilized to cope with the modeling error by back absorbing the nominal model and the inverse controller into a direct through block. Due to the non-minimum phase property of the nominal model estimated by the recursive extended least square algorithm, the zero magnitude error tracking controller is exploited to obtain a stable inverse controller. Then, an off-line iterative learning control strategy involving a real-time feedback controller is conducted on the compensated system to further enhance the replication performance. Therefore, the proposed algorithm combines the merits of modified internal model control and off-line iterative learning control and simplifies the conventional iterative control process by eliminating consecutive computation of Fourier and inverse Fourier transforms. The combined strategy is first programmed in MATLAB/Simulink and then compiled to a real-time personal computer with xPC target technology for implementation. Experiment results demonstrate that a better tracking accuracy and a faster convergence rate are achieved with the proposed algorithm than conventional pure iterative learning controller.

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Section: Experiments Resultsmentioning

confidence: 99%

Time waveform replication for electro-hydraulic shaking table incorporating off-line iterative learning control and modified internal model control

Shen

Zhu

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…As shown in Figure 1, the MTS 329 6-DOF spindle-coupled test rig can reproduce the lateral, longitudinal, vertical, steer, brake, and camber movement to impose three force components and three torque components on each spindle, allowing more realistic service load replication. 21 Each six-axis mechanical movement of the test rig should have as little cross coupling as possible, 32 e.g. the unique configuration of the longitudinal and vertical channels provides a pure vertical straight-line locus at the spindle center, with minimal compensation from the longitudinal channel.…”

Section: Experimental Test Rig Descriptionmentioning

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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“…Usually, the lab simulation is implemented with the multi-axial hydraulic test rigs [1]. The main advantages of laboratory testing are that it cannot be limited by the driver's skill and traffic and weather conditions and can reduce running time and cost with an accelerated vibration test method [2].…”

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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Robust control application for a three-axis road simulator

Cited by 13 publications

References 10 publications

Time waveform replication for electro-hydraulic shaking table incorporating off-line iterative learning control and modified internal model control

Time waveform replication for electro-hydraulic shaking table incorporating off-line iterative learning control and modified internal model control

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

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

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