Identification of Systems Containing Nonlinear Element Using Constant Response Vibration Tests

Nonlinear behavior is important in the vibration test of engineering structures. In this study, a constant response vibration test is proposed for nonlinear element extraction. The method is based on the principle of Harmonic Balance Method (HBM). The stiffness or damping can be regarded as constant for particular steady displacement or velocity response. The displacement or velocity is controlled as a constant in the test. Then the measured Frequency Response Function(FRF) is obtained. The equivalent stiffness or damping is estimated using FRFs for a particular vibration response level. The displacement-dependent stiffness and velocity-dependent damping are fitted to describe the unknown non-linearity. The nonlinear spring and damping force can be obtained by combining the fitting results with HBM using first-order expansion. Constant response vibration test is illustrated through experimental setup to verify its effectiveness. Experimental results show that the procedure is capable of achieving an accurate parameter identification of nonlinear damping and stiffness, which is hopeful for industrial application.

Closed-loop identification of systems using hybrid Box–Jenkins structure and its application to PID tuning

The paper describes a closed-loop system identification procedure for hybrid continuous-time Box–Jenkins models and demonstrates how it can be used for IMC based PID controller tuning. An instrumental variable algorithm is used to identify hybrid continuous-time transfer function models of the Box–Jenkins form from discretetime prefiltered data, where the process model is a continuous-time transfer function, while the noise is represented as a discrete-time ARMA process. A novel penalized maximum-likelihood approach is used for estimating the discrete-time ARMA process and a circulatory noise elimination identification method is employed to estimate process model. The input–output data of a process are affected by additive circulatory noise in a closedloop. The noise-free input–output data of the process are obtained using the proposed method by removing these circulatory noise components. The process model can be achieved by using instrumental variable estimation method with prefiltered noise-free input–output data. The performance of the proposed hybrid parameter estimation scheme is evaluated by the Monte Carlo simulation analysis. Simulation results illustrate the efficacy of the proposed procedure. The methodology has been successfully applied in tuning of IMC based flow controller and a practical application demonstrates the applicability of the algorithm.

Identification of time-varying system and energy-based optimization of adaptive control in seismically excited structure

The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.

Identification of the Asymmetric Transmission Error and Gear Mesh Dynamic Parameters using Full-Spectrum Responses in a Geared-Rotor System

A dominant source of vibration in geared-rotor systems is the gear mesh fault parameters.They include the asymmetric transmission error(TE),phases of TE,the gear mesh stiffness,the gear mesh damping,and the gear runouts.The present work deals with the experimental identification of the aforementioned parameters.A mathematical model of a geared-rotor system has been developed using Lagrangian dynamics.Equations of motion are transformed into the frequency domain using the full-spectrum response analysis.These transformed equations are used to develop an identification algorithm(IA)based on least-squares fit to estimate the TE and gear mesh dynamic parameters.The system IA is initially verified using numerical simulations.The robustness of the algorithm is checked by introducing white Gaussian noise in the simulated responses.A geared-rotor experimental rig was developed and used to measure responses at gear locations in two orthogonal directions.Measured responses are transformed in the frequency domain using the full-spectrum analysis and used in the present novel IA to identify the gear parameters.The identified parameters are validated by comparing the numerically generated full-spectrum response using experimentally estimated parameters and that from the experimental rig.

Operational modal identification of suspension bridge based on structural health monitoring system

An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The methods are applied to the operational modal identification system of the Runyang Suspension Bridge, which can be used to obtain the modal parameters of the bridge from out-only data sets collected by its structural health monitoring system （SHMS）. As an example, the vibration response data of the deck, cable and tower recorded during typhoon Matsa excitation are used to illustrate the program application. Some of the modal frequencies observed from deck vibration responses are also found in the vibration responses of the cable and the tower. The results show that some modal shapes of the deck are strongly coupled with the cable and the tower. By comparing the identification results from the operational modal system with those from field measurements, a good agreement between them is achieved, but some modal frequencies identified from the operational modal identification system （OMIS）, such as L1 and L2, obviously decrease compared with those from the field measurements.

Identification algorithm for a kind of fractional order system

The state-space representation of linear time-invariant (LTI) fractional order systems is introduced, and a proof of their stability theory is also given. Then an efficient identification algorithm is proposed for those fractional order systems. The basic idea of the algorithm is to compute fractional derivatives and the filter simultaneously, i.e., the filtered fractional derivatives can be obtained by computing them in one step, and then system identification can be fulfilled by the least square method. The instrumental variable method is also used in the identification of fractional order systems. In this way, even if there is colored noise in the systems, the unbiased estimation of the parameters can still be obtained. Finally an example of identifying a viscoelastic system is given to show the effectiveness of the aforementioned method.

PARAMETER IDENTIFICATION OF LUGRE FRICTION MODEL FOR FLIGHT SIMULATION SERVO SYSTEM BASED ON ANT COLONY ALGORITHM

In light of the high nonlinearity of LuGre friction model, a novel method based on ant colony algorithm(ACA) for identifying the friction parameters of flight simulation servo system is proposed. ACA is a parallelized bionic optimization algorithm inspired from the behavior of real ants, and a kind of positive feedback mechanism is adopted in ACA. On the basis of brief introduction of LuGre friction model, a method for identifying the static LuGre friction parameters and the dynamic LuGre friction parameters using ACA is derived. Finally, this new friction parameter identification scheme is applied to a electric-driven flight simulation servo system with high precision. Simulation and application results verify the feasibility and the effectiveness of the scheme. It provides a new way to identify the friction parameters of LuGre model.

FREE VIBRATION ANALYSIS AND PHYSICAL PARAMETER IDENTIFICATION OF NON-UNIFORM BEAM CARRYING SPRING-MASS SYSTEMS

To analyze a multibody system composed of non-uniform beam and spring-mass subsystems, the model discretization is carried on by utilizing the finite element method（FEM）, the dynamic model of non-uniform beam is developed by using the transfer matrix method of multibody system（MS-TMM）, the transfer matrix of non-u- niform beam is derived, and the natural frequencies are computed. Compared with the numerical assembly method （NAM）, the results by MS-TMM have good agreement with the results by FEM, and are better than the results by NAM. When using the high precision method, the global dynamic equations of the complex multibody system are not needed and the orders of involved system matrices are decreased greatly. For the investigation on the re- verse problem of the physical parameter identification of multibody system, MS-TMM and the optimization tech- nology based on genetic algorithms（GAs） are combined and extended. The identification problem is exchanged for an optimization problem, and it is formulated as a global minimum solution of the objective function with respect to natural frequencies of multibody system. At last, the numerical example of non-uniform beam with attach- ments is discussed, and the identification results indicate the feasibility and the effectivity of the proposed aop- proach.

Identification of dynamic systems using support vector regression neural networks

A novel adaptive support vector regression neural network （SVR-NN） is proposed, which combines respectively merits of support vector machines and a neural network. First, a support vector regression approach is applied to determine the initial structure and initial weights of the SVR-NN so that the network architecture is easily determined and the hidden nodes can adaptively be constructed based on support vectors. Furthermore, an annealing robust learning algorithm is presented to adjust these hidden node parameters as well as the weights of the SVR-NN. To test the validity of the proposed method, it is demonstrated that the adaptive SVR-NN can be used effectively for the identification of nonlinear dynamic systems. Simulation results show that the identification schemes based on the SVR-NN give considerably better performance and show faster learning in comparison to the previous neural network method.

Frequency domain identification of non-integer order dynamical systems

Two new methods, the generalized Levy method and the weighted iteration method, are presented for identification of non-integer order systems. The first method generalizes the Levy identification method from the integer order systems to the non-integer order systems. Then, the weighted iteration method is presented to overcome the shortcomings of the first method. Results show that the proposed methods have better performance compared with the integer order identification method. For the non-integer order systems, the proposed methods have the better fitting for the system frequency response. For the integer order system, if commensurate order scanning is applied, the proposed methods can also achieve the best integer order model which fits the system frequency response. At the same time, the proposed algorithms are more stable.

IMPROVEMENT ACCURACY OF SYSTEM IDENTIFICATION BASED ON MORLET WAVELET

A time frequency de-noising method is presented in the frequency response function （FRF） preprocessing based on the continuous wavelet transform. Morlet wavelet is employed to construct a filter bank to reduce the noise. The filter bank is a finite impulse response （FIR） linear phase filter thus maintaining phase consistency. A modified Morlet base function is proposed to meet the time frequency resolution by using transient excitation. Numerical simulation is conducted using a Group for Aeronautical Research and Technology in Europe （GARTEUR） aircraft model excited by the transient input. The white noise is added to the simulated data. Results show that the accuracy of the system identification is improved. The estimated error of the mode damping is decreased by 30% compared with that obtained from the noise-corrupted signal.

Identification of a Tobacco Disease on Flat Tray System

[ Objective] The pathogen of a new disease found in floating seedlings of tobacco was isolated and identified to provide the basis for the control of the disease. [ Method] The pathogenicity of the isolated strain was determined according to Koch＇s rules, and the pathogen was identified through Biolog system, 16S rDNA sequence analysis and physiological and biochemical methods. [ Result] Through Koch＇s test, the isolated 3 -3 strain was verified to be the pathogen causing floating seedling disease, which was consistent with the characteristics of Pectobacterium carotovo- rum subsp. Carotovorum through Biolog determination and the other physiological and biochemical methods. 16S rDNA sequence analysis showed that 3 -3 strain had the highest similarity with P. carotovorum subsp. Carotovorum strains Kun28213 （accession number GU936996）, reaching 99. 9%. [Conclusion] Base on the identification results of several methods, the pathogen causing floating seedling disease was P. carotovorum subsp. Carotovorum, and the disease was first reported in China. According to the English name of the disease, the disease was called as tobacco blackleg disease.

SYSTEM IDENTIFICATION OF ADAPTIVE TRUSS STRUCTURES USING PIEZOELECTRIC ACTIVE MEMBERS

Adaptive truss structures are a new kind of structures with integrated active members,whose dynamic characteristies can be beneficially modified to meet mission requirements.Active members containing actuating and sensing units are the major components of adaptive truss structures.Modeling of adaptive truss structures is a key step to analyze the structural dynamic characteristics.A new experimental modal analysis approach,in which active members are used as excitatiDn sources for modal test,has been proposed in this paper.The excitation forces generated by the active members, which are different from the excitation forces exerted on structures in the conventional modal test,are internal forces for the truss structures.The relation between internal excitation forces and external forces is revealed such that the traditional identification method can be adopted to obtain modal parameters of adaptive structures.Placement problem of the active member in adaptive truss structures is also discussed in this work. Modal test and analysis are conducted with a planar adaptive truss structure by using piezoelectric active members in order to verify the feasibility and effectiveness of the proposed method.

Identification and Control of Dynamical Systems Using Modified Neural Networks

Aim To study the identification and control of nonlinear systems using neural networks. Methods A new type of neural network in which the dynamical error feedback is used to modify the inputs of the network was employed to reduce the inherent network approximation error. Results A new identification model constructed by the proposed network and stable filters was derived for continuous time nonlinear systems, and a stable adaptive control scheme based on the proposed networks was developed. Conclusion Theory and simulation results show that the modified neural network is feasible to control a class of nonlinear systems.

Species identification of sympatric cuckoo nestlings in a multiple-cuckoo system,China

Interaction between a parasite and its host could lead to a co-evolutionary arms race. Cuckoo-host system is among the most studied of all brood parasite systems, but the cuckoos of Asia, on the other hand, are much less well known. China has the most abundant cuckoo species in Asia. Many of these co-occur in sympatric areas, posing a potential risk of mis-identification of cuckoo nestlings, especially in Cuculus species. In this study we have provided a practical criterion to identify cuckoo nestlings species in the field and performed molecular phylogeny to confirm our empirical results. These results indicate that two distinct characteristics of cuckoo nestlings, i.e., the gape color pattern and feather traits can be considered as reliable species identification. To our knowledge, this is the first report for species identification of Cuculus nestlings through molecular analysis.

Model Identification of Water Purification Systems Using RBF Neural Network

Aim The RFB (radial hats function) netal network was studied for the model indentificaiton of an ozonation/BAC system. Methods The optimal ozone's dosage and the remain time in carbon tower were analyzed to build the neural network model by which the expected outflow CODM can be acquired under the inflow CODM condition. Results The improved self-organized learning algorithm can assign the centers into appropriate places , and the RBF network's outputs at the sample points fit the experimental data very well. Conclusion The model of ozonation /BAC system based on the RBF network am describe the relationshipamong various factors correctly, a new prouding approach tO the wate purification process is provided.

IDENTIFICATION OF NONLINEAR TIME VARYING SYSTEM USING FEEDFORWARD NEURAL NETWORKS

As it is well known,it is difficult to identify a nonlinear time varying system using traditional identification approaches,especially under unknown nonlinear function.Neural networks have recently emerged as a successful tool in the area of identification and control of time invariant nonlinear systems.However,it is still difficult to apply them to complicated time varying system identification.In this paper we present a learning algorithm for identification of the nonlinear time varying system using feedforward neural networks.The main idea of this approach is that we regard the weights of the network as a state of a time varying system,then use a Kalman filter to estimate the state.Thus the network implements nonlinear and time varying mapping.We derived both the global and local learning algorithms.Simulation results demonstrate the effectiveness of this approach.

Identification and evaluation of shale oil micromigration and its petroleum geological significance

Taking the Lower Permian Fengcheng Formation shale in Mahu Sag of Junggar Basin,NW China,as an example,core observation,test analysis,geological analysis and numerical simulation were applied to identify the shale oil micro-migration phenomenon.The hydrocarbon micro-migration in shale oil was quantitatively evaluated and verified by a self-created hydrocarbon expulsion potential method,and the petroleum geological significance of shale oil micro-migration evaluation was determined.Results show that significant micro-migration can be recognized between the organic-rich lamina and organic-poor lamina.The organic-rich lamina has strong hydrocarbon generation ability.The heavy components of hydrocarbon preferentially retained by kerogen swelling or adsorption,while the light components of hydrocarbon were migrated and accumulated to the interbedded felsic or carbonate organic-poor laminae as free oil.About 69% of the Fengcheng Formation shale samples in Well MY1 exhibit hydrocarbon charging phenomenon,while 31% of those exhibit hydrocarbon expulsion phenomenon.The reliability of the micro-migration evaluation results was verified by combining the group components based on the geochromatography effect,two-dimension nuclear magnetic resonance analysis,and the geochemical behavior of inorganic manganese elements in the process of hydrocarbon migration.Micro-migration is a bridge connecting the hydrocarbon accumulation elements in shale formations,which reflects the whole process of shale oil generation,expulsion and accumulation,and controls the content and composition of shale oil.The identification and evaluation of shale oil micro-migration will provide new perspectives for dynamically differential enrichment mechanism of shale oil and establishing a“multi-peak model in oil generation”of shale.

Model Parameters Identification and Backstepping Control of Lower Limb Exoskeleton Based on Enhanced Whale Algorithm

Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.

NECESSARY CONDITION OF IDENTIFICATION FOR A DISTRIBUTED PARAMETER SYSTEM

This paper considers the necessary condition of the parameter identification problem dudt=(A+B(q))u u(0)=x x∈X with the cost functional J(q)≡12∫ T 0‖Cu(t;q)-y(t)‖ 2 H d t It is proved that the optimal estimate q 0 is determined by the optimal system which consists of the sate equation,the adjoint equation and the optimal condition.