Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Identification and control of power plant de-superheater using soft computing techniques
Engineering Applications of Artificial Intelligence
Combining fuzzy, PID and regulation control for an autonomous mini-helicopter
Information Sciences: an International Journal
Expert system for an INS/DGPS integrated navigator installed in a Bell 206 helicopter
Engineering Applications of Artificial Intelligence
Adaptive controller with fuzzy rules emulated structure and its applications
Engineering Applications of Artificial Intelligence
A fuzzy neural network with fuzzy impact grades
Neurocomputing
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In this research, a fuzzy knowledge-base controller is designed for yaw control of model helicopter. At the next stage, an adjusting algorithm is presented to reduce the influence of high inertia on fuzzy controlled systems. Inertia may cause significant overshoot, which is undesirable and difficult to eliminate. In order to solve this problem, a simple algorithm is presented to reduce the control input by adjusting the fuzzy controller parameters while the system is getting close to the desired condition. Implementing this approach (including a lateral algorithm to reset the parameters in special conditions) for yaw angle control of a model helicopter reduces the overshoot and energy consumption considerably without significant decrease of the settling time.