scholarly journals Multivariable Tracking Control of a Bioethanol Process under Uncertainties

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
M. Cecilia Fernández ◽  
M. Nadia Pantano ◽  
Emanuel Serrano ◽  
Gustavo Scaglia
Keyword(s):  
Tracking Control ◽  
Alternative Fuels ◽  
Tracking Error ◽  
Performance Comparison ◽  
Model Errors ◽  
Fuel Production ◽  
Simple Modification ◽  
External Perturbations ◽  
Process Complexity

Bioethanol is one of the most studied alternative fuels nowadays. Due to its production process complexity and the low quality of the mathematical models that describe it, a reliable controller is needed to maximize the fuel production and minimize its environmental impact, even in the presence of uncertainty. Here, a controller for tracking optimal profiles considering model errors and external perturbations is proposed. This work is an improvement of a previously presented technique. To reduce the earlier mentioned uncertainties’ effect during the fermentation, some tracking error integrators are added in the control action calculation. This simple modification ensures the tracking error convergence to zero, even in the presence of uncertainties (demonstration available). Different tests are carried out and a performance comparison with the original controller is shown to highlight improvements in the tracking error of up to 98% when integrators are incorporated. Furthermore, a classical PI controller is contrasted with the proposed techniques.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Evaluation of Forecasts of a Convectively Generated Bore Using an Intensively Observed Case Study from PECAN

2018 ◽  
Vol 146 (9) ◽  
pp. 3097-3122 ◽  
Author(s):  
Aaron Johnson ◽  
Xuguang Wang ◽  
Kevin R. Haghi ◽  
David B. Parsons
Keyword(s):  
Cross Sections ◽  
Turbulent Mixing ◽  
Weather Research And Forecasting ◽  
Cold Pool ◽  
Model Errors ◽  
Near Surface ◽  
Cold Pools ◽  
Convection Model

Abstract This paper presents a case study from an intensive observing period (IOP) during the Plains Elevated Convection at Night (PECAN) field experiment that was focused on a bore generated by nocturnal convection. Observations from PECAN IOP 25 on 11 July 2015 are used to evaluate the performance of high-resolution Weather Research and Forecasting Model forecasts, initialized using the Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter. The focus is on understanding model errors and sensitivities in order to guide forecast improvements for bores associated with nocturnal convection. Model simulations of the bore amplitude are compared against eight retrieved vertical cross sections through the bore during the IOP. Sensitivities of forecasts to microphysics and planetary boundary layer (PBL) parameterizations are also investigated. Forecasts initialized before the bore pulls away from the convection show a more realistic bore than forecasts initialized later from analyses of the bore itself, in part due to the smoothing of the existing bore in the ensemble mean. Experiments show that the different microphysics schemes impact the quality of the simulations with unrealistically weak cold pools and bores with the Thompson and Morrison microphysics schemes, cold pools too strong with the WDM6 and more accurate with the WSM6 schemes. Most PBL schemes produced a realistic bore response to the cold pool, with the exception of the Mellor–Yamada–Nakanishi–Niino (MYNN) scheme, which creates too much turbulent mixing atop the bore. A new method of objectively estimating the depth of the near-surface stable layer corresponding to a simple two-layer model is also introduced, and the impacts of turbulent mixing on this estimate are discussed.

scholarly journals Adaptive Neural Tracking Control of Robotic Manipulators with Guaranteed NN Weight Convergence

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jun Yang ◽  
Jing Na ◽  
Guanbin Gao ◽  
Chao Zhang
Keyword(s):  
Adaptive Control ◽  
Tracking Control ◽  
Estimation Error ◽  
Tracking Error ◽  
Robotic Manipulators ◽  
Adaptive Laws ◽  
Filter Operation ◽  
Control Response ◽  
Unknown Nonlinear Dynamics ◽  
Acceleration Signals

Although adaptive control for robotic manipulators has been widely studied, most of them require the acceleration signals of the joints, which are usually difficult to measure directly. Although neural networks (NNs) have been used to approximate the unknown nonlinear dynamics in the robotic systems, the conventional adaptive laws for updating the NN weights cannot guarantee that the obtained NN weights converge to their ideal values, which could degrade the tracking control response. To address these two issues, a new adaptive algorithm with the extracted NN weights error is incorporated into adaptive control, where a novel leakage term is superimposed on the gradient method. By using the Lyapunov approach, the convergence of both the tracking error and the estimation error can be guaranteed simultaneously. In addition, two auxiliary functions are introduced to reformulate the robotic model for designing the adaptive law, and a filter operation is used to avoid measuring the acceleration signals. Comparisons to other well-recognized adaptive laws are given, and extensive simulations based on a 2-DOF SCARA robotic system are given to verify the effectiveness of the proposed control strategy.

scholarly journals Online Optimal Control of Robotic Systems with Single Critic NN-Based Reinforcement Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiaoyi Long ◽  
Zheng He ◽  
Zhongyuan Wang
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Tracking Control ◽  
Learning Algorithm ◽  
Tracking Error ◽  
Adaptive Dynamic Programming ◽  
Robotic Systems ◽  
Control Synthesis ◽  
Optimal Tracking ◽  
Optimal Tracking Control

This paper suggests an online solution for the optimal tracking control of robotic systems based on a single critic neural network (NN)-based reinforcement learning (RL) method. To this end, we rewrite the robotic system model as a state-space form, which will facilitate the realization of optimal tracking control synthesis. To maintain the tracking response, a steady-state control is designed, and then an adaptive optimal tracking control is used to ensure that the tracking error can achieve convergence in an optimal sense. To solve the obtained optimal control via the framework of adaptive dynamic programming (ADP), the command trajectory to be tracked and the modified tracking Hamilton-Jacobi-Bellman (HJB) are all formulated. An online RL algorithm is the developed to address the HJB equation using a critic NN with online learning algorithm. Simulation results are given to verify the effectiveness of the proposed method.

scholarly journals A comparative analysis of non­reference assessment methods

Connectivity ◽  
2020 ◽  
Vol 148 (6) ◽  
Author(s):  
V. V. Grebenyuk ◽  
◽  
O. A. Dibrivnyy ◽  
O. V. Nehodenko
Keyword(s):  
Comparative Analysis ◽  
Image Quality ◽  
Image Quality Assessment ◽  
Excellent Result ◽  
Video Quality ◽  
Mean Value ◽  
Performance Comparison ◽  
Quality Level ◽  
Reference Methods

A comparative analysis of functions to assess image quality in the absence of a sample: no-reference (NR) measure or NR-type methods. The availability of NR-methods is very important for assessing the quality of streaming video such as television, game streaming, online conferences, web-chatting, etc. (because on the side of the recipient of the video there is no standard for quality comparison) and assessing the results of transformations aimed at improving video, and choosing the parameters of these transformations (brightness change, semitone and others). The human visual system (HVS) is able to visually assessing video quality, but If required to visually assess the quality of dozens or hundreds of videos or ranking them by quality level it will be needed a huge amount of time. Six types of experiments were performed to analyze the correlation of calculated quantitative estimates with visual assessments of the quality of the tested video files. Three of them are fundamentally new: comparing video after gamma correction and changing the contrast with different parameters, as well as blurring, which may be the result of defocusing the camcorder. A hybrid method (or reduced-reference (RR) measure) and a full-reference (FR) measure or FR-type method were also added for comparison. It has been experimentally shown that none of the studied non-reference methods of image quality assessment is universal, and the calculated assessment cannot be converted into a quality scale without taking into account the factors influencing the distortion of image quality. Moreover, all NR-type methods could not cope with the experiment of changing the contrast, believing that the best result is the most contrasting image but the original. Instead, the reference methods showed an excellent result (except one, which showed partial ineffectiveness). Also, it has been shown performance comparison between methods. It is shown that most of the studied methods calculate local estimates for each frame, and their arithmetic mean value is an estimate of the quality of the entire video file. If the video is dominated by large areas of uniform evaluation, methods of this type may give incorrect quality evaluations that do not coincide with the visual evaluations.

scholarly journals An approach of constant false alarm ratio for improved target tracking

2020 ◽  
Vol 70 (3) ◽  
pp. 17-23
Author(s):  
Zvonko Radosavljević ◽  
Dejan Ivković
Keyword(s):  
False Alarm ◽  
Tracking Error ◽  
Probability Of Detection ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Moving Targets ◽  
False Alarm Ratio ◽  
Motion Models ◽  
Areas Of Interest

Each radar has the function of surveillance of certain areas of interest. In particular, the radar also has the function of tracking moving targets in that territory with some probability of detection, which depends on the type of detector. Constant false alarm ratio (CFAR) is a very commonly used detector. Changing the probability of target detection can directly affect the quality of tracking the moving targets. The paper presents the theoretical basis of the influence of CFAR detectors on the quality of tracking, as well as an approach to the selection of CFAR detectors, CATM CFAR, which enables better monitoring by the Interacting Multiple Model (IMM) algorithm with two motion models. Comparative analysis of CA and CATM algorithm realized by numerical simulations has shown that CATM CFAR gives less tracking error with proportionally the same computer resources.

scholarly journals A model reference adaptive variable impedance control method for robot

2021 ◽  
Vol 336 ◽  
pp. 03005
Author(s):  
Xinchao Sun ◽  
Lianyu Zhao ◽  
Zhenzhong Liu
Keyword(s):  
Real Time ◽  
Tracking Control ◽  
Control Method ◽  
Impedance Control ◽  
Tracking Error ◽  
Reference Trajectory ◽  
Control Parameters ◽  
Model Reference ◽  
Force Tracking ◽  
Model Reference Adaptive

As a simple and effective force tracking control method, impedance control is widely used in robot contact operations. The internal control parameters of traditional impedance control are constant and cannot be corrected in real time, which will lead to instability of control system or large force tracking error. Therefore, it is difficult to be applied to the occasions requiring higher force accuracy, such as robotic medical surgery, robotic space operation and so on. To solve this problem, this paper proposes a model reference adaptive variable impedance control method, which can realize force tracking control by adjusting internal impedance control parameters in real time and generating a reference trajectory at the same time. The simulation experiment proves that compared with the traditional impedance control method, this method has faster force tracking speed and smaller force tracking error. It is a better force tracking control method.

scholarly journals Alternative solid fuels combustion in small heat source

2018 ◽  
Vol 168 ◽  
pp. 08002 ◽  
Author(s):  
Michal Holubčík ◽  
Nikola Kantová ◽  
Jozef Jandačka ◽  
Zuzana Kolková
Keyword(s):  
Heat Source ◽  
Alternative Fuels ◽  
Beech Wood ◽  
Major Influence ◽  
Heat Output ◽  
Heat Sources ◽  
Gaseous Emissions ◽  
Output Efficiency ◽  
Negative Factors

Air quality is related to the using of solid fuel based heat sources in which the human factor has a major influence on the quality of combustion, which can lead to higher emissions into the air. One of the negative factors is the use of alternative fuels in heat sources. The article deals with the combustion of various alternative fuels, on a waste basis, in small heat sources. There were tested 4 types of fuels: beech wood pieces, 2 types of solid alternative fuel on the base of municipal waste and wood waste. In the experiment, it was tested the influence of used fuel in the fireplace on the heat output, efficiency, production of gaseous emissions and particulate matter. The results confirmed that combustion of fuels not recommended by the heat source manufacturer reduces the efficiency of combustion and significantly increases all monitored emissions.

Practical Tracking Control of a Class of Uncertain Surface Vessels Under Weak Assumptions on Uncertainties and Reference Trajectories

2021 ◽  
Author(s):  
Jian Li ◽  
Wenqing Xu ◽  
Zhaojing Wu ◽  
Yungang Liu
Keyword(s):  
Tracking Control ◽  
Broad Class ◽  
Tracking Error ◽  
Reference Trajectory ◽  
Unknown Parameters ◽  
Related Literature ◽  
Surface Vessels ◽  
Control Scheme ◽  
Reference Trajectories ◽  
Theoretical Results

Abstract This paper is devoted to the tracking control of a class of uncertain surface vessels. The main contributions focus on the considerable relaxation of the severe restrictions on system uncertainties and reference trajectory in the related literature. Specifically, all the system parameters are unknown and the disturbance is not necessarily to be differentiable in the paper, but either unknown parameters or disturbance is considered but the other one is excluded in the related literature, or both of them are considered but the disturbance must be continuously differentiable. Moreover, the reference trajectories in the related literature must be at least twice continuously differentiable and themselves as well as their time derivatives must be known for feedback, which are generalized to a more broad class ones that are unknown and only one time continuously differentiable in the paper. To solve the control problem, a novel practical tracking control scheme is presented by using backstepping scheme and adaptive technique, and in turn to derive an adaptive state-feedback controller which guarantees that all the states of the resulting closed-loop system are bounded while the tracking error arrives at and then stay within an arbitrary neighborhood of the origin. Finally, simulation is provided to validate the effectiveness of the proposed theoretical results.

Adaptive sliding tracking control for nonlinear uncertain robotic systems with unknown actuator nonlinearities

Robotica ◽  
2021 ◽  
pp. 1-20
Author(s):  
Shubo Liu ◽  
Guoquan Liu ◽  
Shengbiao Wu
Keyword(s):  
Control Problem ◽  
Prior Knowledge ◽  
Control Strategy ◽  
Tracking Control ◽  
Disturbance Observer ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Robotic Systems ◽  
Actuator Nonlinearities ◽  
Simulation Results

Abstract This study is concerned with the tracking control problem for nonlinear uncertain robotic systems in the presence of unknown actuator nonlinearities. A novel adaptive sliding controller is designed based on a robust disturbance observer without any prior knowledge of actuator nonlinearities and system dynamics. The proposed control strategy can guarantee that the tracking error eventually converges to an arbitrarily small neighborhood of zero. Simulation results are included to demonstrate the effectiveness and superiority of the proposed strategy.

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