System identification

About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.

Output Tracking of High-Order Stochastic Nonlinear Systems with Application to Benchmark Mechanical System

Abstract: This note considers output tracking of high-order stochastic nonlinear systems without imposing any restriction on the high-order and the drift and diffusion terms. By using the backstepping design technique, a smooth state-feedback controller is given to guarantee that the solution process is bounded in probability and the error signal between the output and the reference signal can be regulated into a small neighborhood of the origin in probability. A practical example of stochastic benchmark mechanical system and simulation are provided to demonstrate the effectiveness of the control scheme.

Online Model Identification and State-of-Charge Estimate for Lithium-Ion Battery With a Recursive Total Least Squares-Based Observer

Abstract: The state-of-charge (SOC) observer with online model adaption generally has high accuracy and robustness. However, the unexpected sensing of noises is shown to cause the biased identification of model parameters. To address this problem, a novel technique which integrates a recursive total least squares (RTLS) with an SOC observer is proposed to enhance the online model identification and SOC estimate. An efficient method is exploited to solve the Rayleigh quotient minimization which lays the basis of the RTLS. The number of multiplies, divides, and square roots is elaborated to show the low computational complexity of the developed RTLS. Simulation and experimental results show that the proposed RTLS-based observer attenuates the model identification bias caused by noise corruption effectively, and, thereby, provides a more reliable estimation of SOC. The proposed method is further compared with several available methods to highlight its superiority in terms of accuracy and the robustness to noise corruption.

Fast Bayesian FFT Method for Ambient Modal Identification with Separated Modes

Abstract: Previously a Bayesian theory for modal identification using the fast Fourier transform (FFT) of ambient data was formulated. That method provides a rigorous way for obtaining modal properties as well as their uncertainties by operating in the frequency domain. This allows a natural partition of information according to frequencies so that well-separated modes can be identified independently. Determining the posterior most probable modal parameters and their covariance matrix, however, requires solving a numerical optimization problem. The dimension of this problem grows with the number of measured channels; and its objective function involves the inverse of an ill-conditioned matrix, which makes the approach impractical for realistic applications. This paper analyzes the mathematical structure of the problem and develops efficient methods for computations, focusing on well-separated modes. A method is developed that allows fast computation of the posterior most probable values and covariance matrix. The analysis reveals a scientific definition of signal-to-noise ratio that governs the behavior of the solution in a characteristic manner. Asymptotic behavior of the modal identification problem is investigated for high signal-to-noise ratios. The proposed method is applied to modal identification of two field buildings. Using the proposed algorithm, Bayesian modal identification can now be performed in a few seconds even for a moderate to large number of measurement channels.

Data-Based Adaptive Critic Designs for Nonlinear Robust Optimal Control With Uncertain Dynamics

Abstract: In this paper, the infinite-horizon robust optimal control problem for a class of continuous-time uncertain nonlinear systems is investigated by using data-based adaptive critic designs. The neural network identification scheme is combined with the traditional adaptive critic technique, in order to design the nonlinear robust optimal control under uncertain environment. First, the robust optimal controller of the original uncertain system with a specified cost function is established by adding a feedback gain to the optimal controller of the nominal system. Then, a neural network identifier is employed to reconstruct the unknown dynamics of the nominal system with stability analysis. Hence, the data-based adaptive critic designs can be developed to solve the Hamilton–Jacobi–Bellman equation corresponding to the transformed optimal control problem. The uniform ultimate boundedness of the closed-loop system is also proved by using the Lyapunov approach. Finally, two simulation examples are presented to illustrate the effectiveness of the developed control strategy.