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.

Accurately identifying the peak value of impact load acting on the helicopter structure during weapon launch is of great significance to the design and finalization of weapon pylons. Firstly, a method of standardized preprocessing load signal is proposed by analyzing the vibration response and the characteristics of the impact load. Then, the test model of the weapon pylon is designed, and the position of the strain gauge is determined; the static load calibration test and the ground impact test are carried out on the test model. Next, the time-domain response measured by the strain gauge is filtered and de-noised. Impact load is processed by a standardized method. The response and load are used to train BP neural network and the mapping relationship between response and load is established. The impact load generated by a specific weapon is statistically processed to obtain the normalized average load time history, and the identified standard load is converted back to the original load pattern. Finally, the network that meets the error requirements is tested. Both the standardized pattern and the original pattern have high identification accuracy, which shows that an effective load identification model can be established based on the time-domain response signal and the standardized processed load signal.

A rotor that can realize individual blade pitch control was designed. This paper focuses on finding the trend of helicopter vibration loads after applying multiple high-order harmonic control. The Glauert inflow model was introduced to calculate the induced velocity of rotor blades in a rotor disk plane, and the Leishman Beddoes (L-B) unsteady dynamic model was employed to calculate the aerodynamic forces of each section of a rotor blade. It was found that the influence of each high-order harmonic control on individual blade vibration load reduction is similar in different advanced ratios. After these calculations, the genetic algorithm was used to calculate the best combination of amplitude and phase of the higher order harmonic under a specific flight state. Under the effect of high harmonic input, the vibration loads of the hub could be reduced by about 65%. These results can be theoretically applied to design control law to reduce helicopter vibration loads.

In order to further reduce the vibration level of helicopters, the active vibration control technology of helicopters has been extensively studied. Among them, individual blade control (IBC) independently applies high-order harmonics to each blade with an actuator, which can improve the aerodynamic environment of the blade and effectively reduce the vibration load of the hub. The rotor structural dynamics model based on the Hamilton energy variation principle and the medium deformation beam theory were established firstly, and the aerodynamic model based on the dynamic inflow model and the Leishman–Beddoes unsteady aerodynamic model were also established. The structural finite element method and the direct numerical integration method were used to calculate the vibration response of the rotor to determine the vibration load of the hub. After these, the steepest descent-golden section combinatorial optimization algorithm was used to find the optimization parameters of IBC. Based on this, the input parameters of fuzzy neural network PID control were determined, and the rotor hub vibration load control simulation was conducted. Under the effect of IBC, the vibration loads of the hub could be reduced by about 60%. The article gives the best control laws of individual harmonic pitch control and their combinations. These results can theoretically be applied to the design of control law to reduce helicopter vibration loads.

Impact load is a kind of aperiodic excitation with a short action time and large amplitude, it had more significant effect on the structure than static load. The reconstruction (or identification namely) of impact load is of great importance for validating the structural strength. The aim of this article was to reconstruct the impact load accurately. An impact load identification method based on impulse response theory (IRT) and BP (Back Propagation) neural network is proposed. The excitation and response signals were transformed to the same length by extracting the peak value (amplitude of sine wave) in the rising oscillation period of the response. First, we deduced that there was an approximate linear relationship between the discrete-time integral of impact load and the amplitude of the oscillation period of the response. Secondly, a BP neural network was used to establish a linear relationship between the discrete-time integral of the impact load and the peak value in the rising oscillation period of the response. Thirdly, the network was trained and verified. The error between the actual maximum amplitude of impact load and the identification value was 2.22%. The error between the actual equivalent impulse and the identification value was 0.67%. The results showed that this method had high accuracy and application potential.