control of turbocharged diesel engines is a challenging task due to system nonlinearities and constraints on the inputs and process variables. In this paper nonlinear model predictive control is applied to control a d...
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control of turbocharged diesel engines is a challenging task due to system nonlinearities and constraints on the inputs and process variables. In this paper nonlinear model predictive control is applied to control a diesel engine with a variable geometry turbocharger and an exhaust gas recirculation valve. The overall control objective is to regulate the setpoints of the air-fuel ratio and the amount of recirculated exhaust gas in order to obtain low exhaust emission values and low fuel consumption without smoke generation. Simulation results are presented to study the advantages and disadvantages of nonlinear model predictive control. The achieved performance is compared in simulations with a linear state feedback controller and an input-output linearization based control method. As shown, nonlinear model predictive control achieves good overall control performance and constraint satisfaction.
Canonical correlation analysis (CCA) and minimum variance distortionless response (MVDR) are typical nonparametric spectral estimation methods based on matched filterbank theory. To avoid the common signal mismatch pr...
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Canonical correlation analysis (CCA) and minimum variance distortionless response (MVDR) are typical nonparametric spectral estimation methods based on matched filterbank theory. To avoid the common signal mismatch problem, an algorithm combining CCA and MVDR is proposed for peak frequency modification. It consists of two stages: a coarse-peak search in CCA spectrum, and followed with a fine-peak search using dichotomous search strategy in MVDR spectrum. Furthermore the new algorithm is extended to the magnitude squared coherence (MSC) spectral estimation. Simulations show that the peak frequency estimation accuracy is improved simply and efficiently, with only slight computation increased.
The SUBNET neural network architecture has been developed to identify nonlinear state-space models from input-output data. To achieve this, it combines the rolled-out nonlinear state-space equations and a state encode...
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The SUBNET neural network architecture has been developed to identify nonlinear state-space models from input-output data. To achieve this, it combines the rolled-out nonlinear state-space equations and a state encoder function, both parameterised as neural networks The encoder function is introduced to reconstruct the current state from past input-output data. Hence, it enables the forward simulation of the rolled-out state-space model. While this approach has shown to provide high-accuracy and consistent model estimation, its convergence can be significantly improved by efficient initialization of the training process. This paper focuses on such an initialisation of the subspace encoder approach using the Best Linear Approximation (BLA). Using the BLA provided state-space matrices and its associated reconstructability map, both the state-transition part of the network and the encoder are initialized. The performance of the improved initialisation scheme is evaluated on a Wiener-Hammerstein simulation example and a benchmark dataset. The results show that for a weakly nonlinear system, the proposed initialisation based on the linear reconstructability map results in a faster convergence and a better model quality.
Based on PXI Express bus protocol and PXI Express hardware specification, PXI Express embedded controller is designed in this paper. MPC8536 is taken as a processor in the overall hardware program. FPGA and PXI Expres...
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ISBN:
(纸本)9781479965762
Based on PXI Express bus protocol and PXI Express hardware specification, PXI Express embedded controller is designed in this paper. MPC8536 is taken as a processor in the overall hardware program. FPGA and PXI Express Switch are taken to design PXI Express interface. Cut and customed VxWorks OS is taken in the software program. U-boot ensure normal operation of the embedded controller. BSP boot the VxWorks OS to complete the normal start up, then check the function of each part. Finally, a test platform of AD data acquisition module is built to verify the design.
The paper contributes to the derivation and analysis of accelerations in freeway traffic flow models. First, a solution based on fluid dynamics and on pure mathematical manipulations is given to express accelerations....
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In recent years, deep neural network has been widely used in computer vision, speech recognition and other fields. However, to obtain better performance, it needs to design a network with higher complexity, and the co...
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ISBN:
(数字)9781728185736
ISBN:
(纸本)9781728185743
In recent years, deep neural network has been widely used in computer vision, speech recognition and other fields. However, to obtain better performance, it needs to design a network with higher complexity, and the corresponding model calculation amount and storage space are also increasing. At the same time, the computing resources and energy consumption budget of mobile devices are very limited. Therefore, model compression is very important for deploying neural network models on mobile devices. Knowledge distillation technology based on transfer learning is an effective method to realize model compression. This study proposes: the model pruning technology is introduced into the student network design of knowledge distillation, and the super parameters (temperature T, scale factor λ, pruning rate Υ) are automatically optimized, and the optimal combination of parameters is selected as the final value according to the final performance. The results show that, compared with the commonly used pruning techniques, this method can effectively improve the accuracy of the network without increasing the network size, and the network performance can be further improved by adjusting the value of super parameters.
In the paper a fast technique of impulsive noise suppression in color images is described. The proposed method is utilizing the concept of digital paths which link the central pixel of a filtering window with its boun...
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New algorithms for direct adaptive control of non-minimum phase systems are presented. The algorithms are based on identification of models with special structure and pole-zero placement design. The models are such th...
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New algorithms for direct adaptive control of non-minimum phase systems are presented. The algorithms are based on identification of models with special structure and pole-zero placement design. The models are such that the residuals are bilinear in the parameters.
A simple second order feed-forward disturbance attenuation problem is analyzed. The problem has one free parameter, the control weight ρ in the loss function. It is found that controller structure and uniqueness for...
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A simple second order feed-forward disturbance attenuation problem is analyzed. The problem has one free parameter, the control weight ρ in the loss function. It is found that controller structure and uniqueness for H ∞ -control, in the optimal case, changes when ρ is varied. Sensitivity to initial conditions is also drastically changed. The example is simple enough to allow a solution by formula manipulation, but is rich enough to give physically reasonable controllers and insight into the behavior of both state-space and polynomial H ∞ -methods at optimality. Details can be found in [6].
The paper addresses the design of a robust controller with output feedback for uncertain linear systems in the time domain. The necessary and sufficient conditions for static output feedback stabilizability of linear ...
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The paper addresses the design of a robust controller with output feedback for uncertain linear systems in the time domain. The necessary and sufficient conditions for static output feedback stabilizability of linear continuous and discrete time systems are the basis for the proposed robust controller design procedure. The proposed approach does not employ matching conditions.
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