A multiobjective control problem has been handled in many different ways such as fuzzy, neural network and reinforcement learning, etc. Among them, a reinforcement learning method solves a multiobjective control problem without any prior knowledge. In this article, a new reinforcement learning metho
On the design and modeling of microcontrolled fuzzy nuclear power plant controller
β Scribed by D. Kaur; R. Schroeder
- Publisher
- John Wiley and Sons
- Year
- 1998
- Tongue
- English
- Weight
- 706 KB
- Volume
- 11
- Category
- Article
- ISSN
- 0884-8173
No coin nor oath required. For personal study only.
β¦ Synopsis
The article presents a working model of Control rod insertion in nuclear power plr.nt based on fuzzy logic and implemented with MC68HCll microcontroller. The control rods are made of neutron absorbing metal. The amount of control rod insertion into a pressurized water reactor in a nuclear power plant controls the nuclear fission process by controlling the rate at which the chain reaction takes place. The temperature in the pressurized water tank varies with the load of power, the higher the demand of power the higher the temperature and vice versa. The control rods help to maintain the appropriate temperature in the core of the reactor. Nuclear Power plants use a very complicated control scheme, to vary the amount of control rod insertion which is very expensive and difficult to troubleshoot. We have developed a fuzzy model, tested its functionality by generating control surfaces, written the fuzzy inference software, downloaded in the EEPROM of MC68HCll, and interfaced it with 8 LED Bar to simulate control rod and two 8-bit DIP switches to enter inputs of Power Generation and Temperature of the core of the reactor.
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