Journal of Physics & Astronomy

Open Access Open Access  Restricted Access Subscription Access

Integrating Both a Conventional Integrator and a Fuzzy-Neural-Based PD-Type Controller for an Astronomical Antenna Tracking System Design with Parameter Variations and Servo Hysteresis Effects


Affiliations
1 Department of Electronic Engineering, Chung-Hua University, Hsin-Chu, 30012, Taiwan, Province of China
2 College of Engineering, Chung-Hua University, Hsin-Chu, 30012, Taiwan, Province of China
 

Firstly, the tracking and the stabilization loops for an astronomical antenna tracking system were designed according to the requirements such as bandwidth and phase margin. However, the performances would be degraded if either the tacking loop gain was reduced or the hysteresis effect of servo was enlarged. Thus the PID-based fuzzy-neural controlled was applied the next. But the computing time was very large with 343 rules. This paper proposed two methods to solve this problem. Firstly, a new optimization method was proposed by applying various neural network training algorithms, e.g. gradient descent (GD), scaled conjugate gradient (SCG), and Levenberg-Marquardt (LM) optimization methods are applied alternatively in each step of iteration to determine the optimal parameters of the neural controller. Secondly, a hybrid controller was applied by integrating both a conventional integrator and a fuzzy-neural-based PD-type controller, so that not only the computing time could be reduced, but the error at steady state can be equal zero. Note that the system performances obtained by the proposed method were better not only for parameter variations but hysteresis effects in the system. Thus it was more robust.

Keywords

Antenna Tracking System, Fuzzy-Neural Controller, PID Controller, Parameter Variations, Hysteresis Effects.
User
Notifications
Font Size

  • Densmore AC, Jamnejad V. Two WKa-band mechanically steered and mobile antennas for the NASA ACTS mobile terminal. Proceeding Advanced Communications Technology Satellite Program Conference. NASA, Wash. D.C. 1992.

  • Jamnejad V, Densmore AC. A dual frequency WKa-band small reflector antenna for use in mobile experiments with the NASA advanced satellite communications technology. IEEE APSIURSI Joint Intern. Sym. URSI Digest. 1992:1540.

  • Densmore AC, Jamnejad V. A satellite-tracking K- and Ka-band mobile vehicle antenna system. IEEE Trans. on Vehicular Technology. 1993;42:502-13.

  • Tseng HC, Teo DW. Ship mounted satellite tracking antenna with fuzzy logic control. IEEE Transactions on Aerospace and Electronic Systems. 1998;342:639-45.

  • Zhang Y, Hearn GE, Sen PA. A neural network approach to ship track-keeping control. IEEE Journal of Oceanic Engineering. 1996;21:513-27.

  • Zhang H, Liu D. Fuzzy Modeling and fuzzy control. New York. Springer-Verlag. 2006.

  • Pheysey RL. AIM-9L. Simulation parameters. Naval Weapons Center: China Lake. Calif. NWC.1975.

  • Foy W. Position-location solutions by Taylor series estimation. IEEE Transactions on Aerospace and Electronic Systems. AES-12. 1976:187-93.

  • Torrieri D. Statistical theory of passive location systems. IEEE Transactions on Aerospace and Electronic Systems. AES-20. 1984:183-97.

  • Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323:533-536.

  • Chang PK, Lin JM. Integrating traditional and fuzzy controllers for mobile satellite antenna tracking system design. in Proceedings of WSEAS Conference on Advances in Applied Mathematics, Systems, Communications and Computers selected papers from Circuits, Systems and Signals 2008, June 1-3, 2008. Marathon Beach. Attica. Greece. 2008:102-8.

  • Venkatachalan AR, Sohl JE. An intelligent model selection and forecasting system. Journal of Forecasting. 1999; 18:167-80.

  • Van Rooyen P, Lotter M, Van Wyk D. Space-time processing for CDMA mobile communications. New York. Kluwer.2000.

  • Caffery J. A new approach to the geometry of TOA location. Proceedings of IEEE Vehicular Technology Conference. 24-28 September 2000. Boston. MA. USA. 2000:1943-49.

  • Møller MF. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks. 1993;6:525-33.

  • Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks. 1994;5:989-93.

  • Lin JM, Lin CH. Novel intelligent neural guidance law by using multi-optimization algorithms. Journal of Marine Science and Technology, 2016;24:1-14.


Abstract Views: 243

PDF Views: 3




  • Integrating Both a Conventional Integrator and a Fuzzy-Neural-Based PD-Type Controller for an Astronomical Antenna Tracking System Design with Parameter Variations and Servo Hysteresis Effects

Abstract Views: 243  |  PDF Views: 3

Authors

Jium-Ming Lin
Department of Electronic Engineering, Chung-Hua University, Hsin-Chu, 30012, Taiwan, Province of China
Cheng-Hung Lin
College of Engineering, Chung-Hua University, Hsin-Chu, 30012, Taiwan, Province of China

Abstract


Firstly, the tracking and the stabilization loops for an astronomical antenna tracking system were designed according to the requirements such as bandwidth and phase margin. However, the performances would be degraded if either the tacking loop gain was reduced or the hysteresis effect of servo was enlarged. Thus the PID-based fuzzy-neural controlled was applied the next. But the computing time was very large with 343 rules. This paper proposed two methods to solve this problem. Firstly, a new optimization method was proposed by applying various neural network training algorithms, e.g. gradient descent (GD), scaled conjugate gradient (SCG), and Levenberg-Marquardt (LM) optimization methods are applied alternatively in each step of iteration to determine the optimal parameters of the neural controller. Secondly, a hybrid controller was applied by integrating both a conventional integrator and a fuzzy-neural-based PD-type controller, so that not only the computing time could be reduced, but the error at steady state can be equal zero. Note that the system performances obtained by the proposed method were better not only for parameter variations but hysteresis effects in the system. Thus it was more robust.

Keywords


Antenna Tracking System, Fuzzy-Neural Controller, PID Controller, Parameter Variations, Hysteresis Effects.

References