Concise leader-follower formation control of underactuated unmanned surface vehicle with output error constraints

2021 ◽  
pp. 014233122110471
Author(s):  
Meijiao Zhao ◽  
Yan Peng ◽  
Yueying Wang ◽  
Dan Zhang ◽  
Jun Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Formation Control ◽  
Control Method ◽  
Dynamic Surface Control ◽  
Small Range ◽  
Dynamic Surface ◽  
Unmanned Surface Vehicle ◽  
Control Approach ◽  
Output Error ◽  
Ocean Environment

In this paper, a concise leader-follower formation control approach is presented for a group of underactuated unmanned surface vehicle with dynamic system uncertainties and external environment disturbances, where the output errors are required to be within constraints. To settle the output error constraints, a standard barrier Lyapunov function (BLF) is incorporated into the backstepping control method. Furthermore, the “differential explosion” problem of virtual control laws is avoided by introducing the dynamic surface control. To estimate the unknown dynamic terms, an adaptive neural network is designed and a nonlinear disturbance observer is adopted to compensate for the approximation errors of neural network and ocean environment disturbances. Under the constraint of output error, the presented controller based on standard BLF has simpler structure and better control performance than depended on tan-type BLF. The presented controller can ensure that the formation errors converge to a small range around zero, while the output error constraint requirements are met. All signals in the closed-loop system are bounded, and the numerical simulation further shows the effectiveness of the presented control scheme.

scholarly journals Actor Critic Neural Network-Based Adaptive Control for MEMS Gyroscopes Suffering from Multiresource Disturbances

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Chao Jing ◽  
Gangzhu Qiao
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Method ◽  
Control Policy ◽  
Dynamic Surface Control ◽  
Order System ◽  
Dynamic Surface ◽  
Control Approach ◽  
Mems Gyroscopes ◽  
Critic Neural Network

In this paper, an actor critic neural network-based adaptive control scheme for micro-electro-mechanical system (MEMS) gyroscopes suffering from multiresource disturbances is proposed. Faced with multiresource interferences consisting of parametric uncertainties, strong couplings between axes, Coriolis forces, and variable external disturbances, an actor critic neural network is introduced, where the actor neural network is employed to estimate the packaged disturbances and the critic neural network is utilized to supervise the system performance. Hence, strong robustness against uncertainties and better tracking properties can be derived for MEMS gyroscopes. Aiming at handling the nonlinearities inherent in gyroscopes without analytically differentiating the virtual control signals, dynamic surface control (DSC) rather than backstepping control method is employed to divide the 2nd order system into two 1st order systems and design the actual control policy. Moreover, theoretical analyses along with simulation experiments are conducted with a view to validate the effectiveness of the proposed control approach.

Neural network formation control of a team of tractor–trailer systems

Robotica ◽  
2017 ◽  
Vol 36 (1) ◽  
pp. 39-56 ◽  
Author(s):  
Khoshnam Shojaei
Keyword(s):  
Neural Network ◽  
Formation Control ◽  
Network Formation ◽  
Direct Method ◽  
Dynamic Surface Control ◽  
Loop Formation ◽  
Dynamic Surface ◽  
Control Approach ◽  
On Line ◽  
Virtual Leader

SUMMARYThis paper addresses the formation tracking control of a group of tractor–trailer systems in the presence of model uncertainties. A virtual leader–follower formation technique is used to design a controller in order to force a team of tractor–trailer systems to construct a desired formation configuration. Since tractor–trailer systems have a nonlinear multi-input multi-output model with strong couplings, multi-layer neural networks are employed to overcome unknown nonlinearities and uncertain parameters by using on-line weight tuning algorithms. Neural network approximation errors and external disturbances are also compensated with adaptive robust signals. The dynamic surface control approach has been used to reduce the complexity of the proposed controller effectively. Lyapunov’s direct method proves that all signals in the closed-loop formation control system are bounded and tracking errors converge to a neighborhood of the origin whose size is adjustable. Finally, simulation results will be provided to illustrate the efficiency of the proposed controller.

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.

Adaptive neural network control for course-keeping of ships with input constraints

2017 ◽  
Vol 41 (4) ◽  
pp. 1010-1018 ◽  
Author(s):  
Qingling Wang ◽  
Changyin Sun ◽  
Yangyang Chen
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Dynamic Surface Control ◽  
Backstepping Technique ◽  
Input Constraints ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Controlled System ◽  
Surface Control ◽  
Adaptive Neural Network Control

In this paper, an adaptive neural network (NN) control method is proposed for the problem of nonlinear course control of ships with input constraints and unknown direction control gains. Specifically, dynamic surface control is used to overcome the problem of explosion of complexity inherent in the backstepping technique, and the Nussbaum function is employed to deal with the unknown signs of control gains. It is proved that the proposed adaptive NN control method, which is composed of dynamic surface control and a backstepping technique with the Nussbaum gain function, is able to guarantee uniform ultimate boundedness of all the signals in the controlled system. In addition, the tracking error between the output of the controlled system and a desired trajectory is shown to converge to a small neighbourhood of the origin. Finally, one example is introduced to illustrate the proposed theoretical results.

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