scholarly journals GoRoSoBo simplified: an accurate feedback control algorithm in real time for irrigation canals

2019 ◽  
Vol 21 (6) ◽  
pp. 945-961
Author(s):  
Enrique Bonet ◽  
Manuel Gómez ◽  
M. T. Yubero ◽  
J. Fernández-Francos
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
Hydraulic System ◽  
Control Algorithm ◽  
Estimation Algorithm ◽  
Computational Time ◽  
Irrigation Canal ◽  
Water Demands ◽  
Short Period ◽  
Control Diagram

Abstract An irrigation canal is a hydraulic system whose main objective is to convey water from a source (dam, river) to different users. Such systems can be very large (several tens or hundreds of kilometers), characterized by time delays and non-linear dynamics, strong unknown perturbations and interactions among subsystems. In order to fulfill the requirements of canal users, the water manager must control all water deliveries during the irrigation cycle (or irrigation program) calculating the gate positions of the canal according to the water demands in real time. Initially, our overall control diagram in real time is mainly represented by two algorithms, the canal survey estimation algorithm (this algorithm estimates the water level and velocity along the irrigation canal during a past time horizon) and GoRoSoBo algorithm (feedback control algorithm operating in real time). Regarding long canals with several gates and pumps operating in a short period of time for a long predictive horizon, the initial version of GoRoSoBo algorithms would spend too much time calculating the canal gate position in real time. This is the reason why we have upgraded the code of the GoRoSoBo algorithm, saving in computational time around 85%, in order to operate in real time.

Use of Real-Time Programming Modules for Control of Actuator Positioning in Mechatronic System with Pneumatic Artificial Muscles

2015 ◽  
Vol 18 ◽  
pp. 44-53
Author(s):  
Miroslav Rimár ◽  
Peter Šmeringai ◽  
Marcel Fedák
Keyword(s):  
Control System ◽  
Real Time ◽  
Control Algorithm ◽  
Experimental Device ◽  
Computational Time ◽  
Artificial Muscles ◽  
Mechatronic System ◽  
Real Time Control ◽  
Time Control ◽  
Pneumatic Artificial Muscles

This paper explains proposed real-time control system algorithm modifications for control of drives containing the artificial muscles. The aim of these modifications is to create control algorithm more effective in spite to provide effective computational time to prepare measurement chain to add further artificial muscles to ensure movement in another directions. Described are devices, hardware and software equipment, as well as design of their involvement in the experimental device.

scholarly journals GoRoSoBo: an overall control diagram to improve the efficiency of water transport systems in real time

2017 ◽  
Vol 19 (3) ◽  
pp. 364-384 ◽  
Author(s):  
Enrique Bonet ◽  
Manuel Gómez ◽  
M. T. Yubero ◽  
J. Fernández-Francos
Keyword(s):  
Real Time ◽  
Water Transport ◽  
Food Chain ◽  
Control Algorithm ◽  
Crop Yields ◽  
Transport Systems ◽  
The Real ◽  
Level Data ◽  
Control Diagram ◽  
Canal Management

Agriculture plays an important part in the food chain and water resources for agriculture are essential. A problem is that the water transport systems present low efficiencies in practice. Crop yields must be optimized, and the goal of an operational water manager is to deliver water to irrigation sites accurately and efficiently. In order to fulfill this objective, we propose a centralized overall control diagram to optimize the management of the canal. Our control diagram in real time is mainly composed of two algorithms, CSE and GoRoSoBo. The first one is a powerful tool in canal management, and is able to estimate the real extracted flow in the canal and the hydrodynamic canal state from measured level data at selected points. The second one is an essential tool in the management of the canal, a feedback control algorithm operating in real time. The GoRoSoBo algorithm (Gómez, Rodellar, Soler, Bonet) is able to calculate the optimum gates trajectories for a predictive horizon taking into account the current canal state and the real extracted flow, both obtained by CSE.

scholarly journals A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part II: Performance Verification and Case Study Analysis

2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Estimation Algorithm ◽  
Differential Phase ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Bias Ratio ◽  
Using Data ◽  
Precipitation Events

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

scholarly journals Real‐time feedback control of voice in cochlear implant recipients

2020 ◽  
Vol 5 (6) ◽  
pp. 1156-1162
Author(s):  
Anirudh Gautam ◽  
Jason A. Brant ◽  
Michael J. Ruckenstein ◽  
Steven J. Eliades
Keyword(s):  
Feedback Control ◽  
Cochlear Implant ◽  
Real Time

scholarly journals RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3955
Author(s):  
Jung-Cheng Yang ◽  
Chun-Jung Lin ◽  
Bing-Yuan You ◽  
Yin-Long Yan ◽  
Teng-Hu Cheng
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
High Frequency ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Sampling Rate ◽  
Outdoor Environment ◽  
Position Accuracy ◽  
Graph Optimization ◽  
Pose Graph Optimization

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

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