The feedforward control can effectively improve the servo performance in applications with high requirements of velocity and acceleration. The iterative feedforward tuning method (IFFT) enables the possibility of both...
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The feedforward control can effectively improve the servo performance in applications with high requirements of velocity and acceleration. The iterative feedforward tuning method (IFFT) enables the possibility of both removing the need for prior knowledge of the system plant in model-based feedforward and improving the extrapolation capability for varying tasks of iterative learning control. However, most IFFT methods require to set the number of basis functions in advance, which is inconvenient to the system design. To tackle this problem, an adaptive data-driven IFFT based on a fast recursive algorithm (IFFT-FRA) is developed in this article. Explicitly, based on FRA, the proposed approach can adaptively tune the feedforward structure, which significantly increases the intelligence of the approach. Additionally, a data-based iterative tuning procedure is introduced to achieve the unbiased estimation of parameters optimization in the presence of noise. Comparative experiments on a linear motor confirm the effectiveness of the proposed approach.
Lithium-ion batteries (LiBs) are well-known power sources due to their higher power and energy densities, longer cycle life and lower self-discharge rate features. Hence, these batteries have been widely used in vario...
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Lithium-ion batteries (LiBs) are well-known power sources due to their higher power and energy densities, longer cycle life and lower self-discharge rate features. Hence, these batteries have been widely used in various portable electronic devices, electric vehicles and energy storage systems. The primary challenge in applying a Lithium-ion battery (LiB) system is to guarantee its operation safety under both normal and abnormal operating conditions. To achieve this, temperature management of batteries should be placed as a priority for the purpose of achieving better lifetime performance and preventing thermal failures. In this paper, fibre Bragg Grating (FBG) sensor technology coupling with machine learning (ML) has been explored for battery temperature monitoring. The results based on linear and nonlinear models have confirmed that the novel methods can estimate temperature variations reliably and accurately.
In a recent trend, electric vehicles (EV) have been facing various power quality issues, so fuel cells (FC) are considered the best choice for integrating EV technology to enhance performance. A fuel cell electric veh...
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In a recent trend, electric vehicles (EV) have been facing various power quality issues, so fuel cells (FC) are considered the best choice for integrating EV technology to enhance performance. A fuel cell electric vehicle (FCEV) is a type of EV that uses a fuel cell combined with a small battery or super-capacitor to power its on-board electric motor. However, the power obtained from the FC system is much less and is not enough to drive the EV. So, another energy source is required to deliver the demanded power, which should contain high voltage gain with high conversion efficiency. The traditional converter produces a high output voltage at a high duty cycle, which generates various problems, such as reverse recovery issues, voltage spikes, and less lifespan. High switching frequency and voltage gain are essential for the propulsion of FC-based EV. Therefore, this paper presents an improved radial basis function (RBF)-based high-gain converter (HGC) to enhance the voltage gain and conversion efficiency of the entire system. The RBF neural model was constructed using the fast recursive algorithm (FRA) strategy to prune redundant hidden-layer neurons. The improved RBF technique reduces the input current ripple and voltage stress on the power semiconductor devices to increase the conversion ratio of the HGC without changing the duty cycle value. In the end, the improved RBF with HGC achieved an efficiency of 98.272%, vehicle speed of 91 km/h, and total harmonic distortion (THD) of 3.12%, which was simulated using MATLAB, and its waveforms for steady-state operation were analyzed and compared with existing methods.
This paper proposes a diagonal recurrent neural network (DRNN) based identification scheme to handle the complexity and nonlinearity of high-power continuous microwave heating system (HPCMHS). The new DRNN design invo...
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This paper proposes a diagonal recurrent neural network (DRNN) based identification scheme to handle the complexity and nonlinearity of high-power continuous microwave heating system (HPCMHS). The new DRNN design involves a two-stage training process that couples an efficient forward model selection technique with gradient-based optimization. In the first stage, an impact recurrent network structure is obtained by a fast recursive algorithm in a stepwise forward procedure. To ensure stability, update rules are further developed using Lyapunov stability criterion to tune parameters of reduced size model at the second stage. The proposed approach is tested with an experimental regression problem and a practical HPCMHS identification, and the results are compared with four typical network models. The results show that the new design demonstrates improved accuracy and model compactness with reduced computational complexity over the existing methods.
The Organic Rankine Cycle(ORC) is a promising technique to recover low grade waste heat,and thus helps to improve the overall thermal efficiency of a process and reduce the environmental impact of large consumption of...
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ISBN:
(纸本)9781538629185
The Organic Rankine Cycle(ORC) is a promising technique to recover low grade waste heat,and thus helps to improve the overall thermal efficiency of a process and reduce the environmental impact of large consumption of fossil fuels.A proper control strategy is a key for the safe and efficient operation of the ORC *** this paper,the key components in the ORC system are introduced *** a constrained generalized predictive control(CGPC) is designed to control the process.A controlled auto-regressive integrated moving average(CARIMA) model is used as the online self-tuning predictive model for the GPC controller,while the structure of the CARIMA model is optimized by the fast recursive algorithm(FRA).Simulation results confirm that the developed ORC control strategy is capable of achieving desirable set-point tracking performance,while also has a satisfactory disturbance rejection capability.
Accurate battery internal temperature estimation is a key for safe battery operation of electric vehicles. In this paper, a novel hybrid data-driven method combining a linear neural network (NN) model and an extended ...
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ISBN:
(纸本)9781467398916
Accurate battery internal temperature estimation is a key for safe battery operation of electric vehicles. In this paper, a novel hybrid data-driven method combining a linear neural network (NN) model and an extended Kalman filter (EKF) is developed to estimate the internal temperature of a LiFePo4 battery. In order to select the proper input terms of the linear NN model and estimate the associated parameters, a fast recursive algorithm (FRA) is firstly used. Then an EKF with a battery lumped thermal model as the state function is used to filter out the outliers and reduce the errors in estimating the internal temperature based on the linear NN model. The test results from two different experiment data demonstrate that the hybrid method can achieve good estimation accuracy, and the method can be easily applied to other type of batteries.
Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obst...
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Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O（L2） from O（L4）. The effectiveness of the proposed algorithm is illustrated by experimental results.
Recently, a two-dimensional (2-D) Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstac...
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Recently, a two-dimensional (2-D) Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O(L2) from O(L4). The effectiveness of the proposed algorithm is illustrated by experimental results.
This paper describes the application of an improved nonlinear principal component analysis (PCA) to the detection of faults in polymer extrusion processes. Since the processes are complex in nature and nonlinear relat...
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This paper describes the application of an improved nonlinear principal component analysis (PCA) to the detection of faults in polymer extrusion processes. Since the processes are complex in nature and nonlinear relationships exist between the recorded variables, an improved nonlinear PCA, which incorporates the radial basis function (RBF) networks and principal curves, is proposed. This algorithm comprises two stages. The first stage involves the use of the serial principal curve to obtain the nonlinear scores and approximated data. The second stage is to construct two RBF networks using a fast recursive algorithm to solve the topology problem in traditional nonlinear PCA. The benefits of this improvement are demonstrated in the practical application to a polymer extrusion process.
In order to further improve the accuracy and real-time performance of image segmentation,a new method for image segmentation,which combines the virtues of 2D bound histogram and maximum between-cluster average deviati...
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In order to further improve the accuracy and real-time performance of image segmentation,a new method for image segmentation,which combines the virtues of 2D bound histogram and maximum between-cluster average deviation,is ***,construct the bound set and limit the histogram to region of interest for purpose of reducing the interference of background and *** then,the optimal segmenting threshold is obtained by searching in the region of interest for image segmentation using the maximum between-cluster average ***,the fast recurring algorithm,which is brought forward by the reference,is improved for accelerating the running speed of the *** types of infrared images are selected to compare the results of purposed algorithm and the algorithms in *** show that the proposed algorithm has better segmentation effect,higher segmentation accuracy,less segmentation failure and the running speed is enhanced by more than thirty *** bound histogram can be constructed aiming at concrete problem,based on prior knowledge,so it has better generality.
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