On robustness analysis of a vibrational control system: Input-to-state practical stability

Author(s):  
Xiaoxiao Cheng ◽  
Ying Tan ◽  
Iven Mareels
2004 ◽  
Vol 37 (11) ◽  
pp. 725-730 ◽  
Author(s):  
Jia-Sheng Hu ◽  
Mi-Ching Tsai

ROTASI ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 200
Author(s):  
Prianggada Indra Tanaya

Subsumption control architecture is an control architecture based on parallel system. Input of information of sensors is directly connected through modules in the control system, and further the decision making is connected to actuators. Automated Guided Vehicle or AGV is an automated component within integrated manufacturing system. In this article, this control architecture will be designed and implemented to an AGV. Commands are designed based on Object-Oriented technology. The commands are arranged in subsumption, where a command higher subsumed other command of its lower level. GPFO (Greater Priority First Out) technique is implemed for executing the commands by using multi-threading. Experimentation is performed to have the characteristics of commands being executed. This work introduce our effort to design an operating system for an AGV.


1980 ◽  
Vol 51 (3) ◽  
pp. 759-764 ◽  
Author(s):  
Robert J. Jaeger

The requirement for a credible monkey-man extrapolation in the area of manual control systems led to the investigation of the predictor-operator phenomenon in rhesus monkeys performing in a manual control system. Two monkeys were trained to proficiency in a single-axis visual compensatory manual control system using a random (unpredictable) system input. Data were obtained for system performance in the random case. The system input was then changed to pure sinusoidal (predictable). Data were obtained for the sinusoidal case and compared with data for the random case. Unlike humans, monkeys were not able to improve system performance in the predictable versus unpredictable case.


1996 ◽  
Vol 19 (2) ◽  
pp. 430-437 ◽  
Author(s):  
F. Amato ◽  
G. Ambrosino ◽  
M. Mattei ◽  
L. Verde

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Ruting Jia ◽  
Vidya K. Nandikolla ◽  
Gary Haggart ◽  
Charles Volk ◽  
Daniel Tazartes

The research work evaluates the quality of the sensor to perform measurements and documents its effects on the performance of the system. It also evaluates if this performance changes due to the environments and other system parameters. These environments and parameters include vibration, system friction, structural resonance, and dynamic system input. The analysis is done by modeling a gimbal camera system that requires angular measurements from inertial sensors and gyros for stabilization. Overall, modeling includes models for four different types of gyros, the gimbal camera system, the drive motor, the motor rate control system, and the angle position control system. Models for friction, structural resonance, and vibration are analyzed, respectively. The system is simulated, for an ideal system, and then includes the more realistic environmental and system parameters. These simulations are run with each of the four types of gyros. The performance analysis depicts that for the ideal system; increasing gyro quality provides better system performance. However, when environmental and system parameters are introduced, this is no longer the case. There are even cases when lower quality sensors provide better performance than higher quality sensors.


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