A Real-time Proportional Feedback Controller for Sustaining Uniform Inertial Cavitation Dynamics of Flowing Bubbles

2020 ◽  
Author(s):  
Chunjie Tan ◽  
Bo Yan ◽  
Tao Han ◽  
Alfred C.H. Yu ◽  
Peng Qin
Keyword(s):  
Real Time ◽  
Feedback Controller ◽  
Inertial Cavitation ◽  
Proportional Feedback

Perception as the Output of a Proportional Feedback Controller: Evidence from the Stabilized Image

Perception ◽  
1977 ◽  
Vol 6 (3) ◽  
pp. 355-356
Author(s):  
Peter Walker ◽  
Frederick M Toates
Keyword(s):  
Feedback Controller ◽  
Proportional Feedback

Hardware Implementation of Chaos Control Using a Proportional Feedback Controller

2019 ◽  
pp. 153-163
Author(s):  
Benjamin K. Rhea ◽  
R. Chase Harrison ◽  
D. Aaron Whitney ◽  
Frank T. Werner ◽  
Andrew W. Muscha ◽  
...  
Keyword(s):  
Chaos Control ◽  
Hardware Implementation ◽  
Feedback Controller ◽  
Proportional Feedback

FM 2: A REAL-TIME SENSOR-BASED FEEDBACK CONTROLLER FOR MOBILE ROBOTS

2009 ◽  
Vol 24 (1) ◽  
Author(s):  
S. Garrido ◽  
L. Moreno ◽  
M. Abderrahim ◽  
D. Blanco
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Feedback Controller

scholarly journals Biped Walking Based on Stiffness Optimization and Hierarchical Quadratic Programming

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1696
Author(s):  
Xuanyang Shi ◽  
Junyao Gao ◽  
Yizhou Lu ◽  
Dingkui Tian ◽  
Yi Liu
Keyword(s):  
Quadratic Programming ◽  
Real Time ◽  
Inverse Dynamics ◽  
Reaction Force ◽  
Feedback Controller ◽  
Whole Body ◽  
Real Time Control ◽  
Velocity Feedback ◽  
Landing Point ◽  
Multiple Variables

The spring-loaded inverted pendulum model is similar to human walking in terms of the center of mass (CoM) trajectory and the ground reaction force. It is thus widely used in humanoid robot motion planning. A method that uses a velocity feedback controller to adjust the landing point of a robot leg is inaccurate in the presence of disturbances and a nonlinear optimization method with multiple variables is complicated and thus unsuitable for real-time control. In this paper, to achieve real-time optimization, a CoM-velocity feedback controller is used to calculate the virtual landing point. We construct a touchdown return map based on a virtual landing point and use nonlinear least squares to optimize spring stiffness. For robot whole-body control, hierarchical quadratic programming optimization is used to achieve strict task priority. The dynamic equation is given the highest priority and inverse dynamics are directly used to solve it, reducing the number of optimizations. Simulation and experimental results show that a force-controlled biped robot with the proposed method can stably walk on unknown uneven ground with a maximum obstacle height of 5 cm. The robot can recover from a 5 Nm disturbance during walking without falling.

Image-Based Feedback Control for a Coaxial Spray

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
Julie K. Bothell ◽  
Timothy B. Morgan ◽  
Theodore J. Heindel
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
Golden Section ◽  
Feedback Controller ◽  
Spray Angle ◽  
Air Flow Rate ◽  
Feedback Controllers ◽  
Golden Section Search ◽  
Active Feedback ◽  
Successful Design

Abstract Optimization of jet engine sprays has the potential to improve efficiency and reduce environmental impact. Sprays can be continually optimized in multivariate scenarios using real-time feedback control, but a method of controlling the sprays based on physical properties must first be established. In this study, a spray controller was developed to optimize the spray angle obtained from shadowgraphs, with the assumption that the largest angle is desired. The spray angle was used as an example, as it is a physically important parameter which is easily found through shadowgraph imaging. Varying ratios of swirled air to straight air, determined by the image-based feedback controller were introduced into the air portion of a coaxial airblast nozzle while keeping the total air flow rate constant. A golden section search converged on the swirled air ratio that provided the largest angle and was validated from the distribution of spray angle versus swirled air ratio. The ratio that produced a spray with the greatest angle of 25.8 ± 2 deg was found at a swirled air ratio of 0.66 ± 0.03 for a spray with a momentum ratio of 6. The successful design and implementation of this image-based feedback controller is intended to provide a foundation for developing real-time active feedback controllers for sprays.

scholarly journals Sistem kendali posisi motor DC menggunakan state feedback controller dan real-time operating system

2021 ◽  
Vol 1 (1) ◽  
pp. 69-78
Author(s):  
Martin Martin
Keyword(s):  
Operating System ◽  
Real Time ◽  
Pulse Width ◽  
State Feedback ◽  
Pulse Width Modulation ◽  
Feedback Controller ◽  
Real Time Operating System ◽  
State Feedback Controller

Motor DC merupakan sistem penggerak yang paling banyak digunakan di bidang industri, otomasi, robotika, ataupun lainnya. Penggunaan sistem kendali banyak diterapkan untuk pengaturan pergerakan kecepatan ataupun posisi dari motor DC. Pada penelitian ini, state feedback controller dan penambahan kendali integral dengan estimator digunakan untuk mengendalikan posisi motor DC. Sistem dibuat berbasis real-time operarting system (RTOS) untuk pembacaan sensor, perhitungan matematis kendali, dan pengiriman sinyal pulse width modulation (PWM). Pengendalian dilakukan pada motor DC Quanser yang terhubung dengan Arduino Mega 2560 untuk membaca sensor encoder dan menampilkan data pengujian. Hasil pengujian menunjukkan bahwa state feedback controller dapat mengendalikan posisi motor DC dengan nilai penguat K sebesar 2,66 dan 115,37, nilai penguat N_bar sebesar 0,49 dan nilai estimator sebesar 7,75 dan 0,26. Penggunaan RTOS sebagai inti pemrograman dapat menyelesaikan permasalahan dalam pengerjaan task-task­ seperti pembacaan sensor, perhitungan parameter kendali, dan pengiriman sinyal kendali tanpa terjadi error selama pengujian sistem. Hasil analisa menunjukan keluaran sistem kendali posisi memiliki nilai overshoot sebesar 2,63% pada pengujian pertama dan 2,66% pada pengujian kedua.

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