scholarly journals An Adaptive Dynamic Surface Controller for Ultralow Altitude Airdrop Flight Path Angle with Actuator Input Nonlinearity

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Mao-long Lv ◽  
Xiu-xia Sun ◽  
Shu-guang Liu ◽  
Dong Wang
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Dead Zone ◽  
Tracking Error ◽  
Flight Path ◽  
Network Dynamic ◽  
Dynamic Surface ◽  
Input Nonlinearity ◽  
Atmospheric Disturbance ◽  
Flight Path Angle

In the process of ultralow altitude airdrop, many factors such as actuator input dead-zone, backlash, uncertain external atmospheric disturbance, and model unknown nonlinearity affect the precision of trajectory tracking. In response, a robust adaptive neural network dynamic surface controller is developed. As a result, the aircraft longitudinal dynamics with actuator input nonlinearity is derived; the unknown nonlinear model functions are approximated by means of the RBF neural network. Also, an adaption strategy is used to achieve robustness against model uncertainties. Finally, it has been proved that all the signals in the closed-loop system are bounded and the tracking error converges to a small residual set asymptotically. Simulation results demonstrate the perfect tracking performance and strong robustness of the proposed method, which is not only applicable to the actuator with input dead-zone but also suitable for the backlash nonlinearity. At the same time, it can effectively overcome the effects of dead-zone and the atmospheric disturbance on the system and ensure the fast track of the desired flight path angle instruction, which overthrows the assumption that system functions must be known.

scholarly journals Adaptive NN Control for Multisteering Plane Aircraft with Dead Zone or Backlash Input Nonlinearity

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Xiang-fei Meng ◽  
Ying Wang ◽  
Mao-long Lv
Keyword(s):  
Neural Network ◽  
Dead Zone ◽  
External Disturbance ◽  
Tracking Error ◽  
Network Control ◽  
Control Allocation ◽  
Input Nonlinearity ◽  
Adaptive Neural Network Control ◽  
Robust Adaptive ◽  
Backlash Nonlinearity

Considering that many factors such as actuator input dead zone, backlash, and external disturbance could affect the exactness of trajectory tracking, therewith a robust adaptive neural network control scheme on the basis of control allocation is proposed for the sake of tracking control of multisteering plane aircraft with actuator input dead zone or backlash nonlinearity. First of all, an actuator input dead zone or backlash nonlinearity control assignment model is established and the control allocation equation is derived. Secondly, the system nonlinear uncertainty is compensated by means of radial basis function neural network, and a robust term is introduced to achieve robustness against external disturbance and system errors. Finally, by utilizing Lyapunov stability theorem, it has been proved that all the signals in the closed-loop system are bounded, and the tracking error converges to a small residual set asymptotically. Simulation results on ICE101 multisteering plane aircraft demonstrate the outstanding tracking performance and strong robustness as well as effectiveness of the proposed approach, which can effectively overcome the adverse influence of dead zone, backlash nonlinearity, and external disturbance on the system.

scholarly journals Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

2021 ◽  
pp. 002029402110211
Author(s):  
Tao Chen ◽  
Damin Cao ◽  
Jiaxin Yuan ◽  
Hui Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Tracking Error ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Mode Control ◽  
The Stability

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

Modeling for tracking error of theodolite based on RBF neural network

2012 ◽  
Vol 20 (4) ◽  
pp. 818-825
Author(s):  
李淼 LI Miao ◽  
高慧斌 GAO Hui-bin
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Tracking Error

scholarly journals Neural networks-based robust adaptive flight path tracking control of large transport

2018 ◽  
Vol 38 (3) ◽  
pp. 268-278
Author(s):  
Maolong Lv ◽  
Xiuxia Sun ◽  
G. Z. Xu ◽  
Z. T. Wang
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Nonlinear Modeling ◽  
Dynamic Surface Control ◽  
Continuous Functions ◽  
Network Dynamic ◽  
Dynamic Surface ◽  
The Neural Network ◽  
Neural Network Dynamic ◽  
Robust Adaptive

For the ultralow altitude airdrop decline stage, many factors such as actuator nonlinearity, the uncertain atmospheric disturbances, and model unknown nonlinearity affect the precision of trajectory tracking. A robust adaptive neural network dynamic surface control method is proposed. The neural network is used to approximate unknown nonlinear continuous functions of the model, and a nonlinear robust term is introduced to eliminate the actuator’s nonlinear modeling error and external disturbances. From Lyapunov stability theorem, it is rigorously proved that all the signals in the closed-loop system are bounded. Simulation results confirm the perfect tracking performance and strong robustness of the proposed method.

scholarly journals Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Santiago Rómoli ◽  
Mario Serrano ◽  
Francisco Rossomando ◽  
Jorge Vega ◽  
Oscar Ortiz ◽  
...  
Keyword(s):  
Neural Network ◽  
Closed Loop ◽  
Rbf Neural Network ◽  
Control Strategies ◽  
Tracking Error ◽  
Closed Loop Control ◽  
Online Information ◽  
Fed Batch ◽  
Loop Control ◽  
Batch Bioreactor

The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.

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