Urea selective catalytic reduction (urea-SCR) control is very important for diesel engine emissions. In this paper, a Continuous Stirred Tank Reactor(CSTR) is used to represent the SCR model, then a cascade control sy...
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Urea selective catalytic reduction (urea-SCR) control is very important for diesel engine emissions. In this paper, a Continuous Stirred Tank Reactor(CSTR) is used to represent the SCR model, then a cascade control system is deduced based on active disturbance rejection control (ADRC) strategy. The cascade control system consists of two ADRC controllers, and every ADRC controller contains a Extended State Observer (ESO) to estimate the total disturbance and a PID feedback control law to handle the ammonia coverage ratio tracking. Finally, the simulation results are given to demonstrate the effectiveness of the proposed control scheme tracking the reference and the robustness to the noise.
This paper deals with the problem of observer-based controller design for a class of nonlinear systems subject to unknown inputs. A novel method is presented to design a controller using estimated state variables whic...
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ISBN:
(纸本)9781479999767
This paper deals with the problem of observer-based controller design for a class of nonlinear systems subject to unknown inputs. A novel method is presented to design a controller using estimated state variables which guarantees all the state variables of the closed-loop system converge to the vicinity of the origin and stay there forever. This is done via satisfying several sufficient conditions in terms of nonlinear matrix inequalities. In light of linear algebra, particularly matrix decompositions, the achieved conditions will be converted to a Linear Matrix Inequality (LMI) problem to facilitate the procedure of computing the observer and controller gains. Finally, the effectiveness of the proposed method is illustrated by implementing on a highly nonlinear chaotic system.
Informative proteins are the proteins that play critical functional roles inside *** are the fundamental knowledge of translating bioinformatics into clinical *** methods of identifying informative biomarkers have bee...
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Informative proteins are the proteins that play critical functional roles inside *** are the fundamental knowledge of translating bioinformatics into clinical *** methods of identifying informative biomarkers have been developed which are heuristic and arbitrary,without considering the dynamics characteristics of biological *** this paper,we present a generative model of identifying the informative proteins by systematically analyzing the topological variety of dynamic protein-protein interaction networks(PPINs).In this model,the common representation of multiple PPINs is learned using a deep feature generation model,based on which the original PPINs are rebuilt and the reconstruction errors are analyzed to locate the informative *** were implemented on data of yeast cell cycles and different prostate cancer *** analyze the effectiveness of reconstruction by comparing different methods,and the ranking results of informative proteins were also compared with the results from the baseline *** method is able to reveal the critical members in the dynamic progresses which can be further studied to testify the possibilities for biomarker research.
automatic target recognition is typically deployed on infrared focal plane arrays with high resolution, which could be costly. Due to the compressibility of infrared images, compressive sensing allows us to reduce the...
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automatic target recognition is typically deployed on infrared focal plane arrays with high resolution, which could be costly. Due to the compressibility of infrared images, compressive sensing allows us to reduce the resolution requirements of a focal plane array while keeping the same target recognition ability. In this paper, we develop an iterative reweighted least squares algorithm with stochastically trained initial weights. Our simulations indicate that this method has higher automatic target recognition accuracy than conventional methods such as OMP, BP, and IRLS when applied to the U.S. Army Night Vision and Electronic Sensors Directorate (NVESD) dataset.
The problem of elimination of impulsive disturbances (such as clicks, pops, ticks, crackles, and record scratches) from archive audio recordings is considered and solved using autoregressive modeling. Three classical ...
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ISBN:
(纸本)9788361402282
The problem of elimination of impulsive disturbances (such as clicks, pops, ticks, crackles, and record scratches) from archive audio recordings is considered and solved using autoregressive modeling. Three classical noise pulse detection schemes are examined and compared: the approach based on open-loop multi-step-ahead signal prediction, the approach based on decision-feedback signal prediction, and the double threshold approach, based on analysis of residual errors. It is shown that the accuracy of the classical schemes can be significantly improved by means of combining the results of forward time and backward time signal processing.
We explore some connections between the Loewner approach to interpolation and realization, and that based on bilinear differential forms arising in the behavioral framework. We show that a crucial concept underlying b...
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ISBN:
(纸本)9781479978878
We explore some connections between the Loewner approach to interpolation and realization, and that based on bilinear differential forms arising in the behavioral framework. We show that a crucial concept underlying both approaches is that of duality of trajectories, and that many known results can be interpreted in its light.
This paper presents a new approach to synthesize a nominal constrained model-based predictive control law which can avoid the "non-robustness" curse in the presence of model uncertainty. This approach builds...
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Due to the nonlinearities, model uncertainties and disturbances of the two-tank liquid level cascade system, active disturbance rejection controller (ADRC) solution was developed. First, a nonlinear mechanism model an...
Due to the nonlinearities, model uncertainties and disturbances of the two-tank liquid level cascade system, active disturbance rejection controller (ADRC) solution was developed. First, a nonlinear mechanism model and its linearized model were built based on the theories of fluid dynamics for the system. Second, first-order linear ADRCs for the outer loop of the two cascade systems with an unknown order were designed, respectively. With a simple integrator as the normalized type of the feedback system, other parts of dynamic system which are different from the normalized type are treated as the total disturbances. The disturbances were estimated by the second-order linear extended state observer and compensated in the control law. Then the system, with nonlinearities, uncertainties and disturbances, is reduced to only a linear integrator. In comparison with the traditional PI controller, simulation results show that both the proposed ADRCs have better robustness and adaptability against model uncertainties.
Blind system identification is known to be an ill-posed problem and without further assumptions, no unique solution is at hand. In this contribution, we are concerned with the task of identifying an ARX model from onl...
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