scholarly journals Adaptive Neural Network Sliding Mode Control for Quad Tilt Rotor Aircraft

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yanchao Yin ◽  
Hongwei Niu ◽  
Xiaobao Liu
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Approximation Error ◽  
Model Parameters ◽  
Control Law ◽  
The Novel ◽  
Adaptive Neural Network ◽  
Mode Control ◽  
Attitude Tracking

A novel neural network sliding mode control based on multicommunity bidirectional drive collaborative search algorithm (M-CBDCS) is proposed to design a flight controller for performing the attitude tracking control of a quad tilt rotors aircraft (QTRA). Firstly, the attitude dynamic model of the QTRA concerning propeller tension, channel arm, and moment of inertia is formulated, and the equivalent sliding mode control law is stated. Secondly, an adaptive control algorithm is presented to eliminate the approximation error, where a radial basis function (RBF) neural network is used to online regulate the equivalent sliding mode control law, and the novel M-CBDCS algorithm is developed to uniformly update the unknown neural network weights and essential model parameters adaptively. The nonlinear approximation error is obtained and serves as a novel leakage term in the adaptations to guarantee the sliding surface convergence and eliminate the chattering phenomenon, which benefit the overall attitude control performance for QTRA. Finally, the appropriate comparisons among the novel adaptive neural network sliding mode control, the classical neural network sliding mode control, and the dynamic inverse PID control are examined, and comparative simulations are included to verify the efficacy of the proposed control method.

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.

scholarly journals Neural Network PID Algorithm for a Class of Discrete-Time Nonlinear Systems

2018 ◽  
Vol 14 (02) ◽  
pp. 103 ◽  
Author(s):  
Huifang Kong ◽  
Yao Fang
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Sliding Mode Control ◽  
Discrete Time ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Model Parameters ◽  
Pid Algorithm ◽  
Mode Control ◽  
Model Free

<p class="0abstract"><span lang="EN-US">The control of nonlinear system is the hotspot in the control field. The paper proposes an algorithm to solve the tracking and robustness problem for the discrete-time nonlinear system. The completed control algorithm contains three parts. First, the dynamic linearization model of nonlinear system is designed based on Model Free Adaptive Control, whose model parameters are calculated by the input and output data</span><span lang="EN-US"> of system</span><span lang="EN-US">. Second, the model error is estimated using the Quasi-sliding mode control algorithm</span><span lang="EN-US">, hence, the whole model of system is estimated</span><span lang="EN-US">. Finally, the neural network </span><span lang="EN-US">PID </span><span lang="EN-US">controller is designed to get the optimal control law. The convergence and BIBO stability of the control system is proved by the Lyapunov function. The simulation results </span><span lang="EN-US">in</span><span lang="EN-US"> the </span><span lang="EN-US">linear and </span><span lang="EN-US">nonlinear system validate the effectiveness and robustness of the algorithm.</span><span lang="EN-US"> The robustness </span><span lang="EN-US">effort </span><span lang="EN-US">of </span><span lang="EN-US">Quasi-sliding mode control algorithm</span><span lang="EN-US"> in nonlinear system is also verified in the paper.</span></p>

scholarly journals Nonsingular Integral Sliding Mode Attitude Control for Rigid-Flexible Coupled Spacecraft with High-Inertia Rotating Appendages

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Gaowang Zhang ◽  
Xueqin Chen ◽  
Ruichen Xi ◽  
Huayi Li
Keyword(s):  
Sliding Mode Control ◽  
Control Problem ◽  
Attitude Control ◽  
Sliding Mode ◽  
Control Law ◽  
Solar Panels ◽  
Integral Sliding Mode ◽  
Complex Control ◽  
Mode Control ◽  
Attitude Tracking

This study addresses the challenge of attitude tracking control for a rigid-flexible spacecraft with high-inertia rotating appendages. The Lagrange method was used to establish the kinematic and dynamic models of the spacecraft. The translation and rotation of the spacecraft, vibrations of solar panels, and imbalance caused by the rotating appendages, which cause a complex control problem, were considered. To address the complex control problem, a novel, fast nonsingular integral sliding mode control method is proposed to perform the attitude tracking function of spacecraft. A sliding mode control law was established for the high-inertia appendages to maintain an appropriate angular velocity during rotation. Finally, the effectiveness of the proposed attitude control law was verified by numerical simulations for a spacecraft with high-inertia rotating appendages and symmetrical flexible solar panels.

scholarly journals RBF Neural Network Sliding Mode Control Method Based on Backstepping for an Electro-hydraulic Actuator

2020 ◽  
Vol 66 (12) ◽  
pp. 697-708
Author(s):  
Wending Li ◽  
Guanglin Shi ◽  
Chun Zhao ◽  
Hongyu Liu ◽  
Junyong Fu
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Control Strategy ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Servo System ◽  
Control Law ◽  
Hydraulic Actuator ◽  
Mode Control ◽  
Non Linear System

Aiming at the interference problem and the difficulty of model parameter determination caused by the nonlinearity of the valve-controlled hydraulic cylinder position servo system, this study proposes a radial basis function (RBF) neural network sliding mode control strategy based on a backstepping strategy for the electro-hydraulic actuator. First, the non-linear system model of the third-order position electro-hydraulic control servo system is established on the basis of the principle analysis. Second, the model function RBF adaptive law and backstepping control law are designed according to Lyapunov’s stability theorem to solve the problem of external load disturbance and modelling uncertainty, combined with sliding mode control strategy and virtual control law. Finally, simulation and experiment on MATLAB Simulink and semi-physical experimental platform are accomplished to show the effectiveness of the proposed method. Moreover, results show that the designed controller has high tracking accuracy to the given signal.

scholarly journals The adaptive neural network sliding mode control for angle/force tracking of the dexterous hand

2021 ◽  
Vol 13 (8) ◽  
pp. 168781402110380
Author(s):  
Xing Qin ◽  
Heng Shi ◽  
Xin Gao ◽  
Xiyu Li
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
High Precision ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Adaptive Neural Network ◽  
Estimation Errors ◽  
Mode Control ◽  
Order Of Magnitude ◽  
Dexterous Hand

In order to achieve high precision control of the dexterous hand, an adaptive neural network sliding mode control algorithm based on the U-K (Udwadia-Kalaba) equation is proposed. Firstly, based on the U-K equation and considering the ideal and non-ideal constrained force at each link of the dexterous hand, the detailed dynamic equation is derived. Secondly, considering the uncertainty of the non-ideal constrained force (mainly the friction force on each link of the dexterous hand) and the chattering phenomenon when using sliding mode control alone, the adaptive neural network and the sliding mode control algorithm are combined to realize the high-precision tracking and estimation of each link angle trajectory and the non-ideal constrained force. Finally, in order to verify the correctness and rationality of the proposed algorithm, the 3-DOF spatial dexterous hand is taken as the simulated object. The simulation results show that the tracking and estimation errors of each link angle and the non-ideal constrained force are 10−2 order of magnitude.

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