A Moving Target Tracking Control and Obstacle Avoidance of Quadrotor UAV Based on Sliding Mode Control Using Artificial Potential Field and RBF Neural Networks

2020 ◽  
Author(s):  
Xuan Chen ◽  
Wentao Xue ◽  
Haiyang Qiu ◽  
Hui Ye
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Target Tracking ◽  
Potential Field ◽  
Tracking Control ◽  
Sliding Mode ◽  
Artificial Potential Field ◽  
Moving Target ◽  
Rbf Neural Networks ◽  
Quadrotor Uav

Observer-based adaptive neural sliding mode trajectory tracking control for remotely operated vehicles with thruster constraints

2021 ◽  
pp. 014233122110048
Author(s):  
Zhenzhong Chu ◽  
Yunsai Chen ◽  
Daqi Zhu ◽  
Mingjun Zhang
Keyword(s):  
Neural Networks ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Auxiliary System ◽  
Rbf Neural Networks ◽  
Remotely Operated Vehicle ◽  
Trajectory Tracking Control ◽  
Control Approach ◽  
Saturation Factor

For a class of remotely operated vehicle (ROV) systems with thruster constraints, immeasurable states, and unknown nonlinearities, the trajectory tracking control problem was discussed in this paper. The unknown nonlinear functions were approximated by radial basis function (RBF) neural networks. An adaptive state observer based on neural networks was designed and the immeasurable states were estimated. Considering the problem of thruster saturation constraints, an auxiliary system for saturation compensation was designed and a saturation factor was constructed by the auxiliary system state. By applying the backstepping design method, an adaptive neural sliding mode trajectory tracking controller was developed, in which the saturation factor is contained in adaptive laws. It was proved that the uniformly ultimately bounded (UUB) of trajectory tracking errors can be obtained. Finally, the effectiveness of the proposed trajectory tracking control approach was checked by simulations.

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