International Journal of Engineering Science and Technology 01/2011;
Source: DOAJ

ABSTRACT The system identification process in servo system with frictional force seems to be a complex task becauseof its non-linear nature. For such non-linear systems, a good choice is system identification in frequencydomain. However, most of the techniques are manual and are inappropriate for determination of systemparameters. This makes system identification ineffective for servo systems with frictional force. Toovercome this issue, a hybrid technique is proposed in this paper. The proposed technique exploits neuralnetwork and genetic algorithm to determine the system parameters of servo systems with friction. In theproposed technique, the target parameters are determined from the transfer function derived for thesystem. Subsequently, the system parameters are identified by a process formed by blending the neuralnetwork and genetic algorithm techniques. Prior to performing the identification procedure, backpropagation training is given to the neural network using a pre-examined dataset. Then with thecombined operation of neural network and genetic algorithm, the system parameters that are closer tothe target parameters for the servo system with frictional force are determined. The technique isimplemented and compared with the existing frequency domain identification technique. From thecomparative results, it is evident that the proposed technique outperforms the existing technique.

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    ABSTRACT: Mechanical devices usually come with undesirable nonlinearities, such as friction, backlashes, and saturations. Under the assumption of linear systems, the commonly seen identification schemes utilize sinusoidal excitation signals for parameter identification. However, the data needed for identification are unavoidably distorted by the aforementioned nonlinearities and the identification result may not be satisfactory. In the paper, binary test signals are used to perform identification, thus simplifying the behavior of friction. An identification method based on the difference of binary multifrequency excitation signals is proposed. The modified identification algorithm does not suffer from the problem of nonlinear distortions in the signal shape and is able to determine the nonlinear friction such that an accurate servo system model can be derived. A high-precision ball-screw table with asymmetric friction is identified as a test plant for this approach. The results prove that the method can be used very successfully
    IEEE Transactions on Control Systems Technology 10/2002; DOI:10.1109/TCST.2002.801804 · 2.52 Impact Factor
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    ABSTRACT: In this paper, we introduce an adaptive algorithm for nonlinear system identification in the short-time Fourier transform (STFT) domain. The adaptive scheme consists of a parallel combination of a linear component, represented by crossband filters between subbands, and a quadratic component, which is modeled by multiplicative cross-terms. We adaptively update the model parameters using the least-mean-square (LMS) algorithm, and derive explicit expressions for the transient and steady-state mean-square error (MSE) in frequency bins for white Gaussian inputs. We show that estimation of the nonlinear component improves the MSE performance only when the power ratio of nonlinear to linear components is relatively high. Furthermore, as the number of crossband filters increases, a lower steady-state MSE may be obtained at the expense of slower convergence. Experimental results support the theoretical derivations.
    IEEE Transactions on Signal Processing 11/2009; 57(10-57):3891 - 3904. DOI:10.1109/TSP.2009.2021713 · 3.20 Impact Factor
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    ABSTRACT: Friction is responsible for several servomechanism problems, and their elimination is always a challenge for control engineers. In this paper, feedback model-based compensation of friction is used for servomechanism set point and tracking tasks. Basic friction models are tested and their influence on system response is examined using describing function analysis. Analytical predictions are compared to simulations and experimental results. Various control laws using friction compensation are compared experimentally. Results showed that for both types of tasks, the best response is obtained by a model-based control law with friction compensation using the general kinetic friction model.
    Intelligent Robots and Systems, 2002. IEEE/RSJ International Conference on; 02/2002
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