Bilateral Control with Disturbance Observer and Adaptive Neural Network Compensation

Author(s):  
Jing Wen ◽  
Dapeng Tian
Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Wei Zhao ◽  
Li Tang ◽  
Yan-Jun Liu

This article investigates an adaptive neural network (NN) control algorithm for marine surface vessels with time-varying output constraints and unknown external disturbances. The nonlinear state-dependent transformation (NSDT) is introduced to eliminate the feasibility conditions of virtual controller. Moreover, the barrier Lyapunov function (BLF) is used to achieve time-varying output constraints. As an important approximation tool, the NN is employed to approximate uncertain and continuous functions. Subsequently, the disturbance observer is structured to observe time-varying constraints and unknown external disturbances. The novel strategy can guarantee that all signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, the simulation results verify the benefit of the proposed method.


Author(s):  
Chunqiang Wu ◽  
Meijiao Zhao ◽  
Cheng Min ◽  
Yueying Wang ◽  
Jun Luo

In this paper, a leader–follower formation control strategy is presented based on adaptive neural network and disturbance observer, which is aimed at resolving model uncertainties as well as the time-varying disturbances for autonomous underactuated surface vessels. The model uncertainties which can be expressed by unknown nonlinear functions are approximated and compensated by the adaptive neural network. The disturbance observer introduced can estimate time-varying disturbances and compensate them to the feedforward control loop, so as to make the external time-varying disturbances suppressed and the robustness of controller against the disturbances improved. The dynamic surface control technology is applied in the procedure of designing the controller through utilizing the backstepping method, which solves the computational explosion of the derivative of virtual control signals. Finally, through Lyapunov analysis, the stability of adaptive neural formation control system is proved and all the error signals uniformly converge to a very small range ultimately. The excellent performance of the presented formation control strategy is demonstrated through numerical simulations.


Author(s):  
Yu Ma ◽  
Yuanli Cai ◽  
Zhenhua Yu

In this paper, a novel constrained nonsingular fast terminal sliding mode control scheme based on adaptive neural network disturbance observer is proposed for a flexible air-breathing hypersonic vehicle in the presence of diverse disturbances and actuator constraints. Firstly, velocity and altitude subsystems in the strict feedback formulations are obtained by decomposing the longitudinal dynamics of flexible air-breathing hypersonic vehicle, while uncertainties with regard to flexible effects, aerodynamic parameter uncertainties, modeling errors, and external disturbances are formed as the lumped disturbances which are excellently estimated by the proposed adaptive neural network disturbance observer with the adaptive regulation laws of weight matrices. Then based on the nonsingular fast terminal sliding mode control, the proposed scheme integrated with adaptive neural network disturbance observer is developed to design the controllers with nonsingularity and fast convergent rate in order to provide robust and fast tracking performance of velocity and altitude. Furthermore, to tackle the saturation effects caused by the constraints of actuators, the auxiliary systems constructed in the proposed scheme are conducted to compensate the desired controllers timely. Lyapunov stability analyses prove that the stable tracking errors of velocity and altitude are bounded with the sufficiently small regions around zero even when flexible air-breathing hypersonic vehicle is subject to lumped disturbances and actuator constraints. Finally, the contrastive simulation results demonstrate that the proposed scheme provides the superior tracking performance and the effectiveness in tackling actuator constraints and counteracting lumped disturbances.


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