Rate-Dependent Hysteresis Modeling and Displacement Tracking Control Based on Least-Squares SVM for Axially Pre-Compressed Macro-Fiber Composite Bimorph

Hu, Kai-Ming; Ge, Hujian; Li, Hua; Xie, Songyun; Xu, Suan

doi:10.3390/ma15186480

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…The dSPACE1104 board was used to implement control algorithms. It is well-suited for rapid prototyping and complex control systems [36][37][38], as it has a high-speed processor (250 MHz), 32 MB SDRAM, and 8 MB flash memory. The dSPACE1104 was directly connected to a host computer containing software packages that were used to build control models, generate C code, and visualize data.…”

Section: Data Descriptionmentioning

confidence: 99%

Robust Tracking Control of Piezo-Actuated Nanopositioning Stage Using Improved Inverse LSSVM Hysteresis Model and RST Controller

et al. 2022

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Nanopositioning technology is widely used in high-resolution applications. It often uses piezoelectric actuators due to their superior characteristics. However, piezoelectric actuators exhibit a hysteresis phenomenon that limits their positioning accuracy. To compensate for the hysteresis effect, developing an accurate hysteresis model of piezoelectric actuators is very important. This task is challenging, requiring some considerations of the multivalued mapping of hysteresis loops and the generalization capabilities of the model. This challenge can be dealt with by developing a machine learning-based model, whose inverse model can be used to efficiently design an accurate feedforward controller for hysteresis compensation. However, this approach depends on model accuracy and the type of data used to train the model. Thus, accurate prediction of the hysteresis behavior may not be guaranteed in the presence of disturbances. In this paper, a machine learning-based model is used to design a hysteresis compensator and then combined with a robust feedback controller to enhance the robustness of a nanopositioning control system. The proposed model is based on hysteresis operators, the least square support vector machine (LSSVM) method, and particle swarm optimization (PSO) algorithm. The inverse model is used to design the feedforward controller, and the RST controller is employed to develop feedback control. Our main contribution is the introduction of a hybrid controller capable of compensating for the hysteresis effect, and at the same time, eliminating remaining modeling errors and rejecting disturbances. The performance of the proposed approach is evaluated through MATLAB simulation, as well as through real-time experiments. The experimental results of our approach demonstrate superior tracking performance compared with the PID-LSSVM controller.

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Section: Data Descriptionmentioning

confidence: 99%

Robust Tracking Control of Piezo-Actuated Nanopositioning Stage Using Improved Inverse LSSVM Hysteresis Model and RST Controller

et al. 2022

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“…The least squares support vector machine transforms the support vector machine problem into linear equations that can be used as solutions to improve the solution speed and reduce memory usage. At the same time, the error square sum loss function of the training sample is used as the empirical loss, which improves the convergence accuracy of the model [37] and is one of the most commonly used surrogate models.…”

Section: Road Risk Judgmentmentioning

confidence: 99%

Risk Assessment Model and Sensitivity Analysis of Ordinary Arterial Highways Based on RSR–CRITIC–LVSSM–EFAST

Wang

et al. 2022

Sustainability

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In this paper, in order to evaluate the traffic safety status of ordinary arterial highways, identify the sources of safety risks, and formulate safety development countermeasures for arterial highways to reduce accident risks, a combination method involving rank-sum ratio (RSR), criteria importance though intercriteria correlation (CRITIC), and least squares support vector machine (LVSSM) is adopted. The traffic safety risk index system and risk assessment model of ordinary arterial highways with two dimensions of risk severity and accident severity are established. Based on the global sensitivity analysis of the extended Fourier amplitude sensitivity test (EFAST), the resulting risk assessment model for ordinary arterial highways is proposed. Combined with the current traffic safety situation of ordinary arterial highways in Weinan City, Shaanxi Province, China, data collection and analyses were carried out from the perspectives of traffic operation status, personnel facilities management, road environment characteristics, and accident occurrence patterns. The results show that the risk level of ordinary arterial highways can be obviously divided into warning areas, control areas, and prompt areas. The proportion of roads through villages and the number of deceleration facilities belong to the highly sensitive indicators of the S107 safety risk, which need to be emphatically investigated. This analysis method based is on the RCLE (RSR-CRITIC-LVSSM-EFAST) risk assessment model and has high operability and adaptability. It can be adaptively divided according to the requirements of risk-level differentiation, and the road risk classification can be displayed more intuitively, which is conducive to formulating targeted improvement measures for arterial highway safety and ensuring the safe and orderly operation of arterial highway traffic.

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“…Such objects are the most difficult to manage. It is especially difficult to overcome the problems that arise in the presence of hysteresis, if this hysteresis is associated with some dynamic properties of the object, that is, in the mathematical model, the nonlinear and linear parts are inseparable, they cannot be represented by separate elements [1][2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Numerical Optimized Control of Object with Hysteresis with Smoothing Deviation

Zhmud

2023

Advances in Transdisciplinary Engineering

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Controlling an object containing a nonlinear element with hysteresis in its mathematical model is an extremely difficult task. In some cases, it is possible to control nonlinear objects using the sliding mode method, but this method is completely unsuitable if there is a pure delay in the object model, since the delay does not allow very fast switching with an extremely small amplitude in the system. Ideally, the oscillations should be negligible due to fast switching, but due to the influence of the delay, these oscillations have a period of at least twice the value of the delay, and therefore the amplitude of such oscillations increases. In addition, the parameters of these oscillations remain uncontrollable when using design methods focused on organizing the sliding mode. It is much more effective to introduce forced oscillations into the control loop by introducing a small deviation into the control signal. It is intuitively clear that the amplitude of the signals of this deviation should be close to the hysteresis value, however, the choice of the amplitude and frequency of this deviation can greatly affect the control result, and there are no analytical algorithms for such a choice. The task is complicated by the fact that an element with hysteresis, as a rule, always itself has some dynamic properties, that is, it has both a non-zero signal propagation time and a dependence of the signal amplitude on frequency, so changing the frequency of the deviation signal will require changing its amplitude. The article proposes a solution to this problem of controlling an object with hysteresis in a circuit having dynamic properties by means of numerical optimization, while not only the coefficients of the controller used are subject to calculation by this method, but also the amplitude of the smoothing deviation signal, and the selection of the deviation frequency is carried out by an iterative procedure. Simulation and optimization confirm the effectiveness and robustness of this controller design method.

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Rate-Dependent Hysteresis Modeling and Displacement Tracking Control Based on Least-Squares SVM for Axially Pre-Compressed Macro-Fiber Composite Bimorph

Cited by 6 publications

References 27 publications

Robust Tracking Control of Piezo-Actuated Nanopositioning Stage Using Improved Inverse LSSVM Hysteresis Model and RST Controller

Robust Tracking Control of Piezo-Actuated Nanopositioning Stage Using Improved Inverse LSSVM Hysteresis Model and RST Controller

Risk Assessment Model and Sensitivity Analysis of Ordinary Arterial Highways Based on RSR–CRITIC–LVSSM–EFAST

Numerical Optimized Control of Object with Hysteresis with Smoothing Deviation

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