Alireza Sharifi; Mahdi Foroughi; H. Nobahari
Volume 10, Issue 4 , March 2018, , Pages 9-17
Abstract
In this paper, an adaptive-neuro-fuzzy controller is implemented online for a temperature control system using model-based design. First, the time domain identification approaches are utilized for the dynamic model identification. Then, the identified model is used in the adaptive-neuro-fuzzy controller. ...
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In this paper, an adaptive-neuro-fuzzy controller is implemented online for a temperature control system using model-based design. First, the time domain identification approaches are utilized for the dynamic model identification. Then, the identified model is used in the adaptive-neuro-fuzzy controller. The simulated model of the proposed controller, created in the Simulink environment, is translated into C code using Simulink Coder. The generated C code is compiled into a hardware device and is successfully embedded on a microcontroller. In the next step, the experimental setup of a temperature controller is done to verify the adaptive-neuro-fuzzy controller. Finally, a comparison was made between the proposed controller and a classical proportional-integral-derivative controller to investigate the performance of the proposed approach. The results demonstrate that the proposed approach provides an excellent performance for a temperature control system.
S. M. SalehiAmiri; A. A. Nikkhah; H. Nobahari
Volume 7, Issue 3 , October 2014, , Pages 1-8
Abstract
This paper presents a method for calculation the non observable states in alignment and calibration process in gimballed inertial navigation system, using estimation method in static linear system and heuristic optimization algorithms. The non observable constant states in alignment process are horizontal ...
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This paper presents a method for calculation the non observable states in alignment and calibration process in gimballed inertial navigation system, using estimation method in static linear system and heuristic optimization algorithms. The non observable constant states in alignment process are horizontal accelerometers biases and azimuth gyroscope drift. In order to use the estimation method in static system, the observations are recorded in necessary time duration to convert the dynamic alignment process to static process. Simulation results show appropriate accuracy of purposed method for calculation the non observable states. Although the case study is the alignment process for gimballed inertial navigation system, the purposed method can be used for calibration and alignment of any inertial navigation systems.In purposed method the genetic heuristic optimization algorithm is used.
