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1.
The purpose of this study is to identify a novel 6-DOF precision positioning table, which is assembled by two different 3-DOF precision positioning tables: a plane-type 3-DOF (X, Y, θz) precision positioning table and a cylinder-type 3-DOF (θx, θy, Z) one. According to the dynamics of a mechanical mass-spring system, we establish simple mathematical equations that contain linear mass (inertia), viscous friction, and spring stiffness associated with cross-coupling effects due to mechanical bending. In system identification, we identify parameters of this 6-DOF and two 3-DOF precision positioning tables driven by piezoelectric actuators with hysteresis phenomenon, which is described by Bouc–Wen model. The identification method based on the real-coded genetic algorithm (RGA) has the advantages to identify all the parameters of the table and the hysteresis model simultaneously. From experimental results and numerical simulations, it is found that the numerically identified parameters are almost the same as those of the real system. In comparison of the identified results between the integral and individual tables, it is found that the integral table has better performance than those from the individual table.  相似文献   

2.
To reduce vibration and noise, a damping mechanism is often required in mechanical systems. Many types of dampers are currently used. In this paper, several typical damping models, i.e., structural damping, frictional damping, and viscoelastic damping, are illustrated, and their parameters are identified for multibody dynamic simulation. Linear damping, widely adopted for structural damping, is applied to beam deflection. Quadratic damping including air resistance is applied to plate deflection. To model stick phenomenon in mechanical dampers, a STV (stick-transition velocity) model was first introduced. To identify parameters, an optimization process is applied to the damping parameters. A new MSTV (modified stick-transition velocity) model is proposed for a friction damper. A modified Kelvin–Voight model is suggested for a rubber bushing model used in vehicle dynamics, and its parameters are identified. A modified Bouc–Wen model is also proposed; it includes the hysteretic behavior of an elastomer, and optimized results with parameter identification are compared to test results.  相似文献   

3.
The objective of a base isolation system is to decouple the building from the damaging components of the earthquake by placing isolators between the superstructure and the foundation. The correct identification of these devices is, therefore, a critical step towards reliable simulations of base-isolated systems subjected to dynamic ground motion. In this perspective, the parametric identification of seismic isolators from experimental dynamic tests is here addressed. In doing so, the focus is on identifying Bouc–Wen model parameters by means of particle swarm optimization and differential evolution. This paper is especially concerned with the assessment of these non-classical parametric identification techniques using a standardized experimental protocol to set out the dynamic loading conditions. A critical review of the obtained outputs demonstrates that particle swarm optimization and differential evolution can be effectively exploited for the parametric identification of seismic isolators.  相似文献   

4.
This paper deals with a new methodology concerning parametric identification designed for non-linear uncertain systems. Non-measurable states of the model are restored through a variable structure observer which converges in a finite time. Thanks to this latter property, it is possible to derive equations of the model in order to obtain a parametric estimation law which converges in finite time to the nominal values of the parameters without the use of the classical persistent excitation for the input signal. The main interest of the approach is its robustness with respect to parameter uncertainties and additive measurement noise. This method will be compared to some classical ones. Finally, simulation results will illustrate the use of the algorithm with an application to a synchronous machine.  相似文献   

5.
It is difficult to model a distributed parameter system (DPS) due to the infinite-dimensional time/space nature and unknown nonlinear uncertainties. A low-dimensional and simple nonlinear model is often required for practical applications. In this paper, a spatio-temporal Volterra model is proposed with a series of spatio-temporal kernels for modeling unknown nonlinear DPS. To estimate these kernels, they are expanded onto spatial and temporal bases with unknown coefficients. To reduce the model dimension and parametric complexity in the spatial domain, the Karhunen–Loève (KL) method is used to find the dominant spatial bases. To reduce the parametric complexity in the temporal domain, the Laguerre polynomials are selected as temporal bases. Next, using the Galerkin method, this spatio-temporal modeling becomes a linear regression problem. Then unknown parameters can be easily estimated using the least-squares method in the temporal domain. After the time/space synthesis, the spatio-temporal Volterra model can be constructed. The convergence of parameter estimation can be guaranteed under certain conditions. This model has a low-dimensional and simple nonlinear structure, which is useful for the prediction and control of the DPS. The simulation and experiment demonstrate the effectiveness of the proposed modeling method.  相似文献   

6.
This paper considers certain practical aspects of the identification of nonlinear empirical models for chemical process dynamics. The primary focus is the identification of second-order Volterra models using input sequences that offer the following three advantages: (1) they are “plant friendly;” (2) they simplify the required computations; (3) they can emphasize certain model parameters over others. To provide a quantitative basis for discussing the first of these advantages, this paper defines a friendliness index f that relates to the number of changes that occur in the sequence. For convenience, this paper also considers an additional nonlinear model structure: the Volterra–Laguerre model. To illustrate the practical utility of the input sequences considered here, second-order Volterra and Volterra–Laguerre models are developed that approximate the dynamics of a first-principles model of methyl methacrylate polymerization.  相似文献   

7.
The original ARMarkov identification method explicitly determines the first μ Markov parameters from plant input–output data and approximates the slower dynamics of the process by an ARX model structure. In this paper, the method is extended to include a disturbance model and an ARIMAX structure is used to approximate the slower dynamics. This extended ARMarkov model is then used to formulate a predictive controller. As the number of Markov parameters in the model varies from one to P (prediction horizon)+1, the controller changes from generalized predictive control (GPC) to dynamic matrix control (DMC). The advantages of the proposed ARM-MPC are the consistency of the Markov parameters estimated by the ARMarkov method, independent tuning of the controller for servo and regulatory responses and the ability to combine the characteristics of GPC and DMC. The theoretical results are illustrated through simulation examples.  相似文献   

8.
Kinematic model identification of industrial manipulators   总被引:1,自引:0,他引:1  
The aim of the work presented in this paper is to improve the off-line programming capability of industrial robots by improving their accuracy. Rather than impose more strict manufacturing tolerances, it is widely accepted that a method of identifying kinematic parameters specific to each individual robot provides a cost effective way of improving accuracy. A procedure is presented for identification of actual kinematic parameters, which uses the plane of rotation and centre of rotation introduced by Stone. The procedure differs from that of Stone in that it makes use of the radius of rotation and also introduces a translation of the plane of rotation along the axis of rotation. This allows for the direct identification of the D–H model parameters which are more widely accepted and easier to interpret than the S model parameters. It is shown that, unlike the original method of Stone, the new procedure can also deal with the situation when two consecutive joint axes are parallel. The method is validated on both simulated data and real measured data for a Puma 560 robot, showing an improvement in positioning accuracy of around 80%.  相似文献   

9.
In this paper, we use system identification methods for abnormal condition detection in a cement rotary kiln. After selecting proper inputs and output, an input–output model is identified for the plant’s normal conditions. A novel approach is used in order to estimate the delays of the input channels of the kiln before identification part. This method eases the identification since with determining the input channels delays, the dimension of search space in the identification part reduces. Afterward, to identify the kiln’s model, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOcally LInear MOdel Tree (LOLIMOT) algorithm which is an incremental tree-structure algorithm. Finally, with the model for normal condition of the kiln, the incident of abnormalities in output are detected based on the length of duration and magnitude of difference between the real output and model’s output. We distinguished three abnormal conditions in the kiln, two of which are known as common abnormal conditions and another one which was not characteristically known for cement experts either.  相似文献   

10.
王卓  苑明哲  王宏 《计算机仿真》2007,24(10):322-325
针对传统维纳模型辨识方法存在算法复杂、精度低的问题,通过对最小二乘支持向量机建模原理和维纳模型结构特点的分析,提出一种基于最小二乘支持向量机的维纳模型辨识新方法.该方法充分利用了维纳模型中具有线性环节这一先验知识,实现了线性和非线性环节参数的同时辨识.对于多变量维纳模型,该方法同样适用.给出并证明了该方法存在唯一解的约束条件 - 参数部分列满秩.仿真实验表明了该方法的有效性,与标准最小二乘支持向量机辨识方法相比,该方法具有更高的精度.  相似文献   

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