Soft-computing techniques for the development of adaptive helicopter flight controller

In this paper we design an on-line controller which is able to modify and adapt the rule base of the system with just only qualitative knowledge about the plant to be controlled. Since flying a helicopter is an extremely difficult task, the fuzzy logic controller was necessarily quite complex. In fact, the control tasks were distributed over four individual control units, each of which had its own rules and associated membership functions. Because the fuzzy logic controller was large, and because the rules implemented in the individual control units were not necessarily those a human pilot would use, an efficient technique for writing the rules was required. A genetic algorithm was used to discover rules that provided for effective control of the helicopter. Our study is focused on the module responsible for controlling the helicopter's altitude. For the simulations performed by the adaptive controller, we modify, in a dynamic way, the value of the mass of the helicopter. This would correspond, in real life, to an increase or decrease, for example, in the number of passengers, discharge of water in a fire, etc. On the basis of the nominal value of the helicopter's mass, various simulations are performed to modify the latter parameter within a 15% range. Faced with such a situation, the values of the consequences of the rules responsible for controlling the helicopter's altitude must vary, as otherwise it would not be possible to maintain a zero difference between the desired altitude and that measured by the sensors. Finally, due to the speed requirement, the controller is implemented in FPGA