Modeling and Design Adaptive Double Neural Network Controller for Eight-Rotor Micro Aircraft Vehicle

Di Li,Zhihong Lu,Xiang-jian Chen 보안공학연구지원센터 2014 International Journal of Control and Automation Vol.7 No.11

In this article, a dynamic model of a six degrees of freedom (6 DOF) Eight-Rotor MAV (micro aerial vehicles) is derived on the basis of the Newton-Euler formalism. The derivation comprises determining equations of the motion of the Eight-Rotor MAV in three dimensions and approximating the actuation forces through the modelling of aerodynamic coefficients and electric motor dynamics. For Eight-Rotor MAV is inherently unstable as they are highly sensitive to external perturb, here provides an adaptive double neural network controller for the motion control of the Eight-Rotor MAV autonomous flight. This controller is developed in three parts around each of the variables. The experimental results show that the proposed adaptive double neural network controller outperforms the conventional PID controller due to its fast adaptive qualities in the presence of sensor measurement noise and the parameters variations of Eight-Rotor MAV.

Modeling and designing intelligent adaptive sliding mode controller for an Eight-Rotor MAV

Xiang-jian Chen,Di Li 한국항공우주학회 2013 International Journal of Aeronautical and Space Sc Vol.14 No.2

This paper focuses on the modeling and intelligent control of the new Eight-Rotor MAV, which is used to solve the problem of the low coefficient proportion between lift and gravity for the Quadrotor MAV. The Eight-Rotor MAV is a nonlinear plant, so that it is difficult to obtain stable control, due to uncertainties. The purpose of this paper is to propose a robust, stable attitude control strategy for the Eight-Rotor MAV, to accommodate system uncertainties, variations, and external disturbances. First, an interval type-Ⅱ fuzzy neural network is employed to approximate the nonlinearity function and uncertainty functions in the dynamic model of the Eight-Rotor MAV. Then, the parameters of the interval type-Ⅱ fuzzy neural network and gain of sliding mode control can be tuned on-line by adaptive laws based on the Lyapunov synthesis approach, and the Lyapunov stability theorem has been used to testify the asymptotic stability of the closed-loop system. The validity of the proposed control method has been verified in the Eight-Rotor MAV through real-time experiments. The experimental results show that the performance of the interval type-Ⅱ fuzzy neural network based adaptive sliding mode controller could guarantee the Eight- Rotor MAV control system good performances under uncertainties, variations, and external disturbances. This controller is significantly improved, compared with the conventional adaptive sliding mode controller, and the type-Ⅰ fuzzy neural network based sliding mode controller.

Modeling and designing intelligent adaptive sliding mode controller for an Eight-Rotor MAV

Chen, Xiang-Jian,Li, Di The Korean Society for Aeronautical and Space Scie 2013 International Journal of Aeronautical and Space Sc Vol.14 No.2

This paper focuses on the modeling and intelligent control of the new Eight-Rotor MAV, which is used to solve the problem of the low coefficient proportion between lift and gravity for the Quadrotor MAV. The Eight-Rotor MAV is a nonlinear plant, so that it is difficult to obtain stable control, due to uncertainties. The purpose of this paper is to propose a robust, stable attitude control strategy for the Eight-Rotor MAV, to accommodate system uncertainties, variations, and external disturbances. First, an interval type-II fuzzy neural network is employed to approximate the nonlinearity function and uncertainty functions in the dynamic model of the Eight-Rotor MAV. Then, the parameters of the interval type-II fuzzy neural network and gain of sliding mode control can be tuned on-line by adaptive laws based on the Lyapunov synthesis approach, and the Lyapunov stability theorem has been used to testify the asymptotic stability of the closed-loop system. The validity of the proposed control method has been verified in the Eight-Rotor MAV through real-time experiments. The experimental results show that the performance of the interval type-II fuzzy neural network based adaptive sliding mode controller could guarantee the Eight-Rotor MAV control system good performances under uncertainties, variations, and external disturbances. This controller is significantly improved, compared with the conventional adaptive sliding mode controller, and the type-I fuzzy neural network based sliding mode controller.

Optimal type II fuzzy neural network controller for Eight-Rotor MAV

Xiangjian CHEN,Di Li,Xi-Bei Yang,Yuecheng Yu 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.4

This paper focuses on modeling and intelligent control of the new eight-rotor MAV which is used to solvethe problem of low coefficient proportion between lift and gravity for QuadrotorMAV. The dynamic and kinematicalmodeling for the eight-rotor MAV.Neuro-Fuzzy adaptive controller is proposed which is composed of two type-IIfuzzy neural networks (T-IIFNNs) and one PD controller: The PD controller is adopted to control the attitude, oneof the T-IIFNNs is designed to learn the inverse model of eight-rotor MAV on-line, the other one is the copy of theformer one to compensate for model errors and external disturbances, both structure and parameters of T-IIFNNs aretuned on-line at the same time, and then the stability of the eight-rotor MAV closed-loop control system is provedusing Lyapunov stability theory. Meanwhile ,in order to reduce the computation work, the type-reduction andmodel construction process have been improved. For the issue of type reduction, a novel improved EKM algorithmis developed for improving the EKM algorithm. The proposed algorithm provides two improvements on the EKMalgorithm. For the issue of rules redundant, the concept of normalized difference is proposed to describe the changeof adjacent singular value so as to reflect the essential differences between redundant rules and important rules. Then the number of effective singular can be determined according to its critical point, and the type-2 fuzzy modelis constructed with rules located by TLS decomposition. Finally, the validity of the proposed control method hasbeen verified through real-time experiments. The experimental results show that the performance of Neuro-Fuzzyadaptive controller performs very well under sensor noise and external disturbances.

Modeling and Neuro-Fuzzy Adaptive Attitude Control for Eight-Rotor MAV

Xiangjian Chen,Di Li,Yue Bai,Zhijun Xu 제어·로봇·시스템학회 2011 International Journal of Control, Automation, and Vol.9 No.6

This paper focuses on modeling and intelligent control of the new Eight-Rotor MAV which is used to solve the problem of low coefficient proportion between lift and gravity for Quadrotor MAV. The dynamical and kinematical modeling for the Eight-Rotor MAV was developed which has never been proposed before. Based on the achieved dynamic modeling, two types of controller were presented. One type, a PID controller is derived in a conventional way with simplified dynamics and turns out to be quite sensitive to sensor noise as well as external perturbation. The second type controller is the Neuro-Fuzzy adaptive controller which is composed of two type-II fuzzy neural networks (T-IIFNNs) and one PD controller: The PD controller is adopted to control the attitude, one of the T-IIFNNs is designed to learn the inverse model of Eight-Rotor MAV on-line, the other one is the copy of the former one to compensate for model errors and external disturbances, both structure and parameters of T-IIFNNs are tuned on-line at the same time, and then the stability of the Eight-Rotor MAV closed-loop control system is proved using Lyapunov stability theory. Finally, the validity of the proposed control method has been verified through real-time experiments. The experimental results show that the performance of Neuro-Fuzzy adaptive controller performs very well under sensor noise and external disturbances, and has more superiority than traditional PID controller.

H infinity control design for Eight-Rotor MAV attitude system based on identification by interval type II fuzzy neural network

Xiangjian CHEN,Kun SHU,Di LI 한국항공우주학회 2016 International Journal of Aeronautical and Space Sc Vol.17 No.2

In order to overcome the influence of system stability and accuracy caused by uncertainty, estimation errors and external disturbances in Eight-Rotor MAV, L2 gain control method was proposed based on interval type II fuzzy neural network identification here. In this control strategy, interval type II fuzzy neural network is used to estimate the uncertainty and nonlinearity factor of the dynamic system, the adaptive variable structure controller is applied to compensate the estimation errors of interval type II fuzzy neural network, and at last, L2 gain control method is employed to suppress the effect produced by external disturbance on system, which is expected to possess robustness for the uncertainty and non-linearity. Finally, the validity of the L2 gain control method based on interval type II fuzzy neural network identifier applied to the Eight-Rotor MAV attitude system has been verified by three prototy experiments.

H infinity control design for Eight-Rotor MAV attitude system based on identification by interval type II fuzzy neural network

CHEN, Xiangjian,SHU, Kun,LI, Di The Korean Society for Aeronautical and Space Scie 2016 International Journal of Aeronautical and Space Sc Vol.17 No.2

In order to overcome the influence of system stability and accuracy caused by uncertainty, estimation errors and external disturbances in Eight-Rotor MAV, L2 gain control method was proposed based on interval type II fuzzy neural network identification here. In this control strategy, interval type II fuzzy neural network is used to estimate the uncertainty and non-linearity factor of the dynamic system, the adaptive variable structure controller is applied to compensate the estimation errors of interval type II fuzzy neural network, and at last, L2 gain control method is employed to suppress the effect produced by external disturbance on system, which is expected to possess robustness for the uncertainty and non-linearity. Finally, the validity of the L2 gain control method based on interval type II fuzzy neural network identifier applied to the Eight-Rotor MAV attitude system has been verified by three prototy experiments.