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System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
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01 Jan 2009TL;DR: The need of the partial knowledge of the nonlinear system dynamics is relaxed in the development of a novel approach to ADP using a two part process: online system identification and offline optimal control training.
Abstract: The optimal control of linear systems accompanied by quadratic cost functions can be achieved by solving the well-known Riccati equation. However, the optimal control of nonlinear discrete-time systems is a much more challenging task that often requires solving the nonlinear Hamilton―Jacobi―Bellman (HJB) equation. In the recent literature, discrete-time approximate dynamic programming (ADP) techniques have been widely used to determine the optimal or near optimal control policies for affine nonlinear discrete-time systems. However, an inherent assumption of ADP requires the value of the controlled system one step ahead and at least partial knowledge of the system dynamics to be known. In this work, the need of the partial knowledge of the nonlinear system dynamics is relaxed in the development of a novel approach to ADP using a two part process: online system identification and offline optimal control training. First, in the system identification process, a neural network (NN) is tuned online using novel tuning laws to learn the complete plant dynamics so that a local asymptotic stability of the identification error can be shown. Then, using only the learned NN system model, offline ADP is attempted resulting in a novel optimal control law. The proposed scheme does not require explicit knowledge of the system dynamics as only the learned NN model is needed. The proof of convergence is demonstrated. Simulation results verify theoretical conjecture.
131 citations
21 Jun 1989
TL;DR: This paper uses a fractional representation approach to state and solve theclosed-loop experiment design problem in terms of variables which are at the designer's disposal: the closed-loop inputs and the initial controller.
Abstract: An important aspect of system identification is the problem of experiment design. This paper uses a fractional representation approach to state and solve the closed-loop experiment design problem in terms of variables which are at the designer's disposal: the closed-loop inputs and the initial controller. Results of computer simulations are presented which compare optimal versus several non-optimal identification experiments.
131 citations
TL;DR: In this paper, the authors show that both of these methods can be obtained as special cases of maximum likelihood estimation under normal theory and recommend that the parameters of the identified submodel be estimated by maximum likelihood.
Abstract: Recent developments in quality engineering methods have led to considerable interest in the analysis of dispersion effects from designed experiments. A commonly used method for identifying important dispersion effects from replicated experiments is based on least squares analysis of the logarithm of the within-replication variance (Bartlett and Kendall 1946). Box and Meyer (1986) introduced a pooling technique for unreplicated two-level experiments. We extend this to replicated two-level experiments and compare its performance with the least squares analysis. We show that both of these methods can be obtained as special cases of maximum likelihood estimation under normal theory. The pooling technique is generally biased and is not recommended for model identification. The least squares analysis performs well as a model identification tool, but the estimators can be inefficient. In such cases we recommend that the parameters of the identified submodel be estimated by maximum likelihood. We derive some prop...
131 citations
TL;DR: This work treats fMRI data analysis as a spatiotemporal system identification problem and addresses issues of model formulation, estimation, and model comparison, presenting a new model that includes a physiologically based hemodynamic response and an empirically derived low-frequency noise model.
131 citations
TL;DR: A high-performance ripple-free dynamic torque controller for a variable-reluctance (VR) motor intended for trajectory tracking in robotic applications is designed and a modeling approach that simplifies the design of the controller is investigated.
Abstract: A high-performance ripple-free dynamic torque controller for a variable-reluctance (VR) motor intended for trajectory tracking in robotic applications is designed. A modeling approach that simplifies the design of the controller is investigated. Model structure and parameter estimation techniques are presented. Different approaches to the overall torque controller design problem are discussed, and the solution adopted is illustrated. A cascade controller structure consisting of a feedforward nonlinear torque compensator, cascaded to a nonlinear flux or current closed-loop controller is considered, and optimization techniques are used for its design. Although developed for a specific commercial motor, the proposed modeling and optimization strategies can be used for other VR motors with magnetically decoupled phases, both rotating and linear. Laboratory experiments for model validation and preliminary simulation results of the overall torque control system are presented. >
131 citations