Topic
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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TL;DR: In this paper, the authors describe some of the issues that motivate plant-friendly identification and present an overview of some approaches that have been proposed in this topic. And the problem of identification test monitoring is presented as a novel means for accomplishing plant friendly identification.
106 citations
TL;DR: This work proposes a data-based system-identification technique for modelling the flow of amplifier flows that avoids the model-based shortcomings by directly incorporating noise influences into an auto-regressive (ARMAX) design and should result in effective compensators that maintain performance in a realistic disturbance environment.
Abstract: Control of amplifier flows poses a great challenge, since the influence of environmental noise sources and measurement contamination is a crucial component in the design of models and the subsequent performance of the controller. A model-based approach that makes a priori assumptions on the noise characteristics often yields unsatisfactory results when the true noise environment is different from the assumed one. An alternative approach is proposed that consists of a data-based system-identification technique for modelling the flow; it avoids the model-based shortcomings by directly incorporating noise influences into an auto-regressive (ARMAX) design. This technique is applied to flow over a backward-facing step, a typical example of a noise-amplifier flow. Physical insight into the specifics of the flow is used to interpret and tailor the various terms of the auto-regressive model. The designed compensator shows an impressive performance as well as a remarkable robustness to increased noise levels and to off-design operating conditions. Owing to its reliance on only time-sequences of observable data, the proposed technique should be attractive in the design of control strategies directly from experimental data and should result in effective compensators that maintain performance in a realistic disturbance environment.
106 citations
TL;DR: A fault-tolerant control framework for a class of nonlinear networked control systems (NCSs) is presented, where the plant is transformed into two subsystems with one of them decoupled from the system fault.
Abstract: In this paper, we present a fault-tolerant control (FTC) framework for a class of nonlinear networked control systems (NCSs). Firstly, the plant is transformed into two subsystems with one of them decoupled from the system fault. Then, the nonlinear observer is designed to provide the estimation of unmeasurable state and modelling uncertainty, which are used to construct fault estimation algorithm. Considering the sampling intervals occurred by net, a fault-tolerant control method is proposed for such nonlinear NCSs using the impulsive system techniques. The controller gain and the maximum sampling interval, which make the faulty system stable are given. An example is included to show the efficiency of the proposed method.
106 citations
TL;DR: A hierarchical least-squares based iterative identification algorithm is derived for multivariable systems with moving average noises and makes full use of all data at each iteration and thus can generate highly accurate parameter estimates.
106 citations
TL;DR: PSO with quantum infusion (PSO-QI) is used in identification of benchmark IIR systems and a real world problem in power systems and the results show that PSO- QI has better performance over these algorithms in identifying dynamical systems.
106 citations