Optimum Input for System Identification for Systems With Actuator Saturation Using Genetic Algorithms

Schoen, Marco P.; Chinvorarat, Sinchai; Schoen, Gerhard M.

doi:10.1115/imece2002-33429

Cited by 3 publications

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“…The drawback of that method is that it generally results in large magnitudes of the input signal and requires a second experiment. Schoen (2002) proposed an ongoing iterative input design algorithm that employs a predictive filter and a GA to optimize the future input based on the information content in the information matrix. In this paper, we will compare the most popular input design algorithms using a proposed architecture and a GA.…”

Section: Introductionmentioning

confidence: 99%

Application of Genetic Algorithms to Observer Kalman Filter Identification

Schoen

2008

Journal of Vibration and Control

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In this paper several applications of genetic algorithm (GA) as an aid to the system identification process are presented. First, GAs are used in a set of covariance-based optimum input signal algorithms using a proposed architecture suitable for online system identification. The optimal signals are computed recursively using a predictive filter. The efficiency of these algorithms are compared based on a set of simulations. Second, a novel input design for a two-step identification scheme is presented. Constraint systems, such as commonly found in structural and biomedical engineering applications, are considered for the input design algorithm. This paper presents a novel approach that induces a learning scheme into the input design computation and allows for considerations of the given constraints of the system. The optimization of the new input signal is accomplished using a simple elitism based genetic algorithm. Simulation results indicate the proposed piecewise adaptive input design algorithm performs well compared to the general white-noise-based estimation results. In the third portion of this paper proof is given that no dynamic controller can reduce the noise influence in linear system identification. A new selection scheme of the corresponding singular values is proposed for the eigensystem realization portion of the Observer Kalman filter IDentification algorithm in noisy systems. The selection is done using a GA. Simulation results of the proposed algorithm in comparison with the traditional used method are presented. The results indicate an improved ability to extract system models from highly noise corrupted data.

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Section: Introductionmentioning

confidence: 99%

Application of Genetic Algorithms to Observer Kalman Filter Identification

Schoen

2008

Journal of Vibration and Control

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Dynamic Compensation of Intelligent Sensors

Schoen

2007

IEEE Trans. Instrum. Meas.

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Frequency Based Optimal Input Signal Determination for ARX Parameter Estimation of Constraint Systems

Thumati

Schoen

2005

Dynamic Systems and Control, Parts a and B

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Modern parametric based eigensystem realization algorithms (ERA) used in system identification algorithms depend heavily on the quality of the estimated parameters. Generally, parameters are estimated experimentally by exciting all the modes of the system. The generated input/output data is processed using least-squares or maximum likelihood algorithms to fit the parameters of the assumed model to the gathered data. Since the locations of the system modes are not known at the beginning of the system identification experiment, band-limited white noise signals are used as the input. Input design or experimental design for system identification addresses the input signal computation for optimum parameter estimation. Constraint systems, such as commonly found in biomedical engineering applications, represent a particular challenge to the input design algorithm. This paper presents a novel approach that induces learning into the input design computation and allows for considerations of the given constraints of the system. In particular, the proposed algorithm adapts to the information gathered during the first step of the identification experiment and uses this gained information to form a new input that targets the system modes. The input signal starts with a band-limited white noise and based on the response uses the frequency information contained in the preliminary estimated ARX model to shape the new input signal. This new input is computed such that more energy is directed towards the system modes. Since the objective is to accurately estimate the ARX model parameters and therefore the system model, the preliminary estimated ARX parameters posses some uncertainty. The optimization of the new input signal is accomplished using a simple Genetic Algorithm. Simulation results indicate the proposed piecewise adaptive input design algorithm performs well compared to the general white noise based estimation results

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Optimum Input for System Identification for Systems With Actuator Saturation Using Genetic Algorithms

Cited by 3 publications

References 0 publications

Application of Genetic Algorithms to Observer Kalman Filter Identification

Application of Genetic Algorithms to Observer Kalman Filter Identification

Dynamic Compensation of Intelligent Sensors

Frequency Based Optimal Input Signal Determination for ARX Parameter Estimation of Constraint Systems

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