# Bionic Intelligent Algorithms Used in Helicopter Individual Blade Control Optimization
###### Abstract  
Bionic algorithms are established by imitating human neural structures and animal social behaviors. As an important part of bionic technology, bionic algorithms are often used to solve the control problems of complex nonlinear systems, such as the rotor aeroelasticity dynamics model used in the helicopter individual blade control (IBC) optimization process. Two control methods based on bionic intelligent algorithms are introduced, respectively. The first method is to combine the fuzzy neural network and the classical PID control together. Compared with traditional PID control, the combined one was able to adjust the PID control parameters automatically by using the learning ability of the fuzzy neural network. The second method is to directly search the optimal control parameters by using the particle swarm algorithm. Both two methods demonstrate higher efficiency and accuracy; according to the results obtained by the algorithms, the vibration level was 80% less than without the applied high order harmonics. This indicates great application prospects for bionic intelligent algorithms in solving complex nonlinear system problems. bionic algorithm; artificial neural network (ANN); particle swarm algorithm (PSO); PID control; individual blade control (IBC); helicopter elastic aerodynamics +
Footnote †: journal: Applied  
than 90% in a single direction, or in some cases by more than 60% in all directions. In the best-case scenario encountered during open-loop testing, an IBC of 2/rev was able to reduce variable-pitch tie-rod loads by more than 30%. In 2002, [PERSON] et al. conducted a full-scale UH-60A monolithic blade control study in the 80 ft \(\times\) 120 ft wind tunnel at the NASA Ames Research Center to reduce flight noise and vibration [3]. The preliminary wind tunnel data obtained demonstrated that using a 3/rev harmonic of approximately 1.0\({}^{\circ}\) amplitude could reduce overall vibration by 70%, and using a 2/rev harmonic of 3.0\({}^{\circ}\) amplitude could reduce BVI noise by up to 12 dB (75%). Tests with real rotors are too costly; therefore, the research uses surrogate models to calculate the load on the rotor after acting on the harmonic. The surrogate model of the rotor, called the aeroelasticity dynamic model, is just like a black box without explicit transfer functions. The inputs of the black box are the harmonic acting on the blade, and the output of the black box is the load on the rotor. The aim of the optimization is to find the most suitable input parameters of the system (harmonic phase and amplitude) to obtain the minimal output; thus, the process of optimization can be regarded as a process of control. The artificial neural network (ANN) and particle swarm optimization (PSA) are used widely in solving black box problems. For example, [PERSON] et al. proposed a neural network controller with supervised learning to simulate the behavior of a proven controller only with system states [4]. [PERSON] et al. presented unsupervised learning using a variational autoencoder to monitor the wear of rolls in a hot strip mill, which is a part of a steel-making site [5]. [PERSON] et al. developed a simulation model to perform a sensitivity analysis of the energy consumption of an airport baggage handling system to change the resource allocation strategy [6]. However, there is not much application of bionic intelligent algorithms in the research of the aeroelasticity dynamic model. In this paper, two methods combined with the fuzzy neural network and particle swarm algorithm are presented, respectively. The first method is to combine the fuzzy neural network and the classical PID control together. Compared with the traditional PID control, the combined one can adjust the PID control parameters automatically by using the learning ability of the fuzzy neural network. The second method is to search the optimal control parameters directly by using the particle swarm algorithm.