Experimental verification of a frequency domain evaluation index‐based compensation for real‐time hybrid simulation

Xu, Weijie; Guo, Tong; Chen, Cheng; Chen, Menghui; Chen, Kai

doi:10.1002/stc.2641

Cited by 5 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of AIC and WFEI has been reported in previous study 41 when RD1 and RD2 are used as input signals. Here, only performance of WFEI is presented for comparison in Table 7 as the performance of AIC is influenced by initial estimated delay.…”

Section: Performance Evaluation With Predefined Displacementsmentioning

confidence: 69%

“…Real‐time tests are conducted with two predefined random signals (referred to hereafter as RD1 and RD2, respectively) as well as a chirp signal with frequency from 0.1 to 10 Hz with the duration of 60 s. F‐NARX, S‐NARX, and F‐NARX with weight are conducted with different window length of 64, 128, 256, 512, and 1,024. To systematically evaluate the efficacy of the NARX model‐based compensation, four indices are considered as evaluation criteria for actuator tracking assessment in both frequency and time domain, 41 as

E 1 goodbreak= (‖‖, \sum_{j = 1}^{nf} ({}, \frac{italicfft {[d^{m}]}_{j}}{italicfft {[d^{c}]}_{j}} \cdot \frac{{(‖‖, italicfft {[d^{c}]}_{j})}^{2}}{\sum_{i = 1}^{nf} {‖fft {([], d^{c})}_{i}‖}^{2}})),

E 2 goodbreak= goodbreak- \frac{\frac{atan (Im (\sum_{j = 1}^{italicnf} \{\frac{fft {([], d^{m})}_{j}}{fft {([], d^{c})}_{j}} \cdot \frac{{‖fft {([], d^{c})}_{j}‖}^{2}}{\sum_{i = 1}^{italicnf} {(‖‖, italicfft {[d^{c}]}_{i})}^{2}}\})}{Re (\sum_{j = 1}^{italicnf} \{\frac{fft {([], d^{m})}_{j}}{fft {([], d^{c})}_{j}} \cdot \frac{{‖fft {([], d^{c})}_{j}‖}^{2}}{\sum_{i = 1}^{italicnf} {(‖‖, italicfft {[d^{c}]}_{i})}^{2}}\}))}}{2 π (())}

…”

Section: Performance Evaluation With Predefined Displacementsmentioning

confidence: 99%

“…Its single parameter makes it easy for laboratory implementation and further adaptation such as adaptive inverse compensation method (AIC) and windowed frequency domain evaluation index‐based compensation (WFEI) 38 . The former incorporates the tracking indicator to update the IC parameter, while the latter utilizes the equivalent time delay calculated by frequency domain evaluation index (FEI) for the parameter adaptation 38,40–42 . Despite good performance reported in previous studies, further improvement of these adaptive methods is necessary.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data‐driven nonlinear autoregressive with external input model‐based compensation for real‐time testing

Chen

Gao

et al. 2022

Structural Contr & Hlth

Self Cite

View full text Add to dashboard Cite

Actuator control plays an important role for real‐time testing based experimental techniques such as real‐time hybrid simulation (RTHS). Accurate modeling of actuator dynamics can be challenging due to the complexity and nonlinearity of the servo‐hydraulic system. In this study, nonlinear autoregressive with external input (NARX) modeling is introduced to emulate the servo‐hydraulic dynamics. The command and measured displacements of the actuator are used as input and output of the NARX model. The coefficients of the NARX model of different orders are determined online through ordinary least square regression in a real‐time manner. Data weight is further proposed to account for most recent variation in command and measured displacements. Real‐time tests with predefined random and chirp signals are conducted to evaluate the performance of proposed NARX model‐based compensation of different orders, window length, and data weight. The efficacy and robustness of proposed NARX model‐based compensation are further verified through computational simulation of a RTHS benchmark model. Both numerical simulation and laboratory experiments demonstrate that the proposed method enables effective negation of servo‐hydraulic dynamics for real‐time testing.

show abstract

Section: Performance Evaluation With Predefined Displacementsmentioning

confidence: 69%

E 1 goodbreak= (‖‖, \sum_{j = 1}^{nf} ({}, \frac{italicfft {[d^{m}]}_{j}}{italicfft {[d^{c}]}_{j}} \cdot \frac{{(‖‖, italicfft {[d^{c}]}_{j})}^{2}}{\sum_{i = 1}^{nf} {‖fft {([], d^{c})}_{i}‖}^{2}})),

E 2 goodbreak= goodbreak- \frac{\frac{atan (Im (\sum_{j = 1}^{italicnf} \{\frac{fft {([], d^{m})}_{j}}{fft {([], d^{c})}_{j}} \cdot \frac{{‖fft {([], d^{c})}_{j}‖}^{2}}{\sum_{i = 1}^{italicnf} {(‖‖, italicfft {[d^{c}]}_{i})}^{2}}\})}{Re (\sum_{j = 1}^{italicnf} \{\frac{fft {([], d^{m})}_{j}}{fft {([], d^{c})}_{j}} \cdot \frac{{‖fft {([], d^{c})}_{j}‖}^{2}}{\sum_{i = 1}^{italicnf} {(‖‖, italicfft {[d^{c}]}_{i})}^{2}}\}))}}{2 π (())}

…”

Section: Performance Evaluation With Predefined Displacementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data‐driven nonlinear autoregressive with external input model‐based compensation for real‐time testing

Chen

Gao

et al. 2022

Structural Contr & Hlth

Self Cite

View full text Add to dashboard Cite

show abstract

“…To compensate for the transmission delay of the actuator and the transducers of the hardware-in-the-loop simulator in the 1D vibration motion, Sahua [28] presented a delay estimation model on the basis of the linear phase system and demonstrated that the direct velocity feedback strategy is the most effective in improving system stability. Xu [29] established a first-order discrete transfer function model to represent 1D servo-hydraulic dynamics and investigated an easy-to-implement compensation method for real-time hybrid simulation. Qi [30] analyzed the delay in force measurement and dynamic calculation caused by communication delays, and conducted a simulation and experimental verification on the 1D vertical motion of a linear undamped spring mass system.…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis and Delay Compensation for Space Instable Target Simulator

Bai,

Li,

Zhao

et al. 2024

Actuators

View full text Add to dashboard Cite

The space instable target simulator (SITS) is a vital actuator for ground verification of on-orbital capture technology, the motion performance of which directly affects simulation credibility. Different delays reduce the stability of SITS and ultimately lead to its divergence. In order to achieve high-fidelity simulation, the impacts of force measurement delay, the discrete control cycle, and simulator response delay on stability are analyzed first. Then, the dynamic equation and transfer function identification model of the hybrid simulator is constructed, and the necessary and sufficient conditions of its stability and convergence are obtained using the Routh criterion. After that, a novel switching compensator with variable gain is proposed to reduce the superimposed effects of the three delays, the compensation principle diagram of which was built, and its mathematical model including the energy observer and nonlinear tracking differentiator is also established. Finally, three sets of numerical simulations were conducted to validate the correctness of the stability analysis and effectiveness of the proposed compensation method. The simulation results show that all three types of delays can cause SITS to lose stability under critical stable motion states, and the delay in force measurement has the greatest impact, followed by the influence of the control cycle. Compared with the force applied to the simulated target, the velocity, and the recovery coefficient of the space instable target using fixed gain and linear gain compensation, the proposed compensator has significantly better performance.

show abstract

“…To improve closed‐loop stability and performance of RTHS, researchers have developed a variety of techniques for compensating the actuator time delay or more generally the frequency‐dependent magnitude and phase lag of the actuator transfer system. The techniques range from extrapolation techniques in Horiuchi et al 2 and Ahmadizadeh et al, 8 virtual coupling in Christenson and Lin, 5 and inverse compensation in Chen and Ricles, 9 as well as adaptive techniques in Chen et al, 10 Chae et al, 11 and Zhou et al 12 Carrion and Spencer 13 used a control systems approach to develop model‐based feedforward‐feedback control for actuator dynamics compensation, extended by Phillips and Spencer, 14 Nakata and Stehman, 15 Gao et al, 16 Nakata and Stehman, 15 Ou et al, 17 and Maghareh et al 18 Further, a great body of work has explored active controlled actuators embedded in structures and where the actuator dynamics are effectively compensated Soong 19 McGreevy, 20 Chung et al, 21 Chung et al, 22 Liu et al, 23 Xu et al, 24 and Li et al 25 The ultimate goal of these compensation techniques is to provide effective displacement tracking of single and multi‐actuator transfer systems, reducing both magnitude and phase error over the RTHS control band.…”

Section: Introductionmentioning

confidence: 99%

A robust stability and performance analysis method for multi‐actuator real‐time hybrid simulation

Botelho

Gao

Avci

et al. 2022

Structural Contr & Hlth

View full text Add to dashboard Cite

Real-time hybrid simulation (RTHS) is a relatively new method of vibration testing for characterizing the system-level dynamic response of physical hardware components or substructures. With RTHS, a structural dynamic system can be partitioned into separate physical and numerical substructures and interfaced together in real time as a cyber-physical system similar to hardwarein-the-loop testing. Control actuation and sensing are used to enforce the compatibility and equilibrium conditions between the physical and numerical substructures. Since RTHS involves a feedback loop, the frequency-dependent dynamics of the actuator transfer system can introduce inaccuracy and potential instability during closed-loop testing. This paper presents a robust stability and performance analysis method for multi-actuator RTHS based on concepts from robust stability theory for multiple-input multiple-output (MIMO) feedback control. This method involves casting the actuator dynamics as a multiplicative uncertainty and applying the small gain theorem to derive the sufficient conditions for robust stability and performance. The attractive feature of this method is that it accommodates linearized modeled or measured frequency response functions for the physical substructure and actuator dynamics. The method is demonstrated using Network for Earthquake Engineering Simulation (NEES) experimental data from a two-actuator RTHS test of a seismically excited two-story steel frame structure. Results show that the robust stability and performance analysis method provides a versatile and useful tool for pre-test planning and post-test diagnostics of RTHS tests involving multiple actuators.

show abstract

Experimental verification of a frequency domain evaluation index‐based compensation for real‐time hybrid simulation

Cited by 5 publications

References 43 publications

Data‐driven nonlinear autoregressive with external input model‐based compensation for real‐time testing

Data‐driven nonlinear autoregressive with external input model‐based compensation for real‐time testing

Stability Analysis and Delay Compensation for Space Instable Target Simulator

A robust stability and performance analysis method for multi‐actuator real‐time hybrid simulation

Contact Info

Product

Resources

About