Abstract
The performance of on telescope depend strongly on its operating conditions. During pointing, the telescope can move at a relatively high velocity, and the system can tolerate trajectory position errors higher than during tracking. On the contrary, during tracking, Alt-Az telescopes generally move slower but still in a large dynamic range. In this case, the position errors must be as close to zero as possible. Tracking is one of the essential factors that affects the quality of astronomical observations. In this paper, a hybrid Genetic-Fuzzy approach to control the movement of a two-link direct-drive Celestron telescope is introduced. The proposed controller uses the Genetic algorithm (GA) for optimizing a fuzzy logic controller (FLC) to improve the tracking of the 14-inch Celestron telescope of the Kottamia Astronomical Observatory (KAO). The fuzzy logic input is a vector of the position error and its rate of change, and the output is a torque. The GA objective function used here is the Integral Time Absolute Error (ITAE). The proposed method is compared with a conventional Proportional-Differential (PD) controller, an optimized PD controller with a GA, and a Fuzzy controller. The results show the effectiveness of the proposed controller to improve the dynamic response of the overall system.
References
- Attia, A. [1997] Fuzzy Logic Control for Electric Drive of Astronomical Telescope, M.Sc. thesis, Ain Shams University, Faculty of Engineering, Cairo, Egypt. Google Scholar
- [2004] “Adapted fuzzy controller for astronomical telescope tracking,” Exp. Astron. 18, 93–108. Crossref, ADS, Google Scholar
- [2012] “Adaptive neuro-fuzzy inference system based control of puma 600 robot manipulator,” Int. J. Elect. Comput. Eng. 2(1), 90. Google Scholar
- [2001] Operating Manual (Celestron International Company), pp. 1–26. Google Scholar
- [2011] “Optimal tuning of a PD controller for a single-link flexible robot arm using adaptive genetic algorithm,” in 14th Int. Conf. Aerospace Sciences & Aviation Technology, Vol. 14, p. 1. Google Scholar
- [1989] Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, US). Google Scholar
- [1993] Control of Robot Manipulators (Macmillan Publishing, New York). Google Scholar
- [1998] “A class of nonlinear PID global regulators for robot manipulators,” in Proc. 1998 IEEE Int. Conf. Robotics & Automation. Google Scholar
- [1995] Fuzzy Sets Syst. 71(3), 295. Crossref, Google Scholar
- [1965] Inf. Control 8, 338. Crossref, Google Scholar
