Adaptive neural formation control of autonomous underactuated surface vessels based on disturbance observer with leader–follower strategy

2021 ◽  
pp. 014233122110224
Author(s):  
Chunqiang Wu ◽  
Meijiao Zhao ◽  
Cheng Min ◽  
Yueying Wang ◽  
Jun Luo
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Disturbance Observer ◽  
Formation Control ◽  
Time Varying ◽  
Adaptive Neural Network ◽  
Model Uncertainties ◽  
Dynamic Surface ◽  
Surface Vessels ◽  
Underactuated Surface Vessels

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.

scholarly journals Disturbance Observer-Based Adaptive Neural Network Control of Marine Vessel Systems with Time-Varying Output Constraints

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Wei Zhao ◽  
Li Tang ◽  
Yan-Jun Liu
Keyword(s):  
Neural Network ◽  
Disturbance Observer ◽  
Continuous Functions ◽  
Network Control ◽  
Time Varying ◽  
Adaptive Neural Network ◽  
External Disturbances ◽  
Adaptive Neural Network Control ◽  
Output Constraints ◽  
Novel Strategy

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.

scholarly journals Robust Adaptive Self-Structuring Neural Network Bounded Target Tracking Control of Underactuated Surface Vessels

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Haitao Liu ◽  
Jianfei Lin ◽  
Guoyan Yu ◽  
Jianbin Yuan
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Input Saturation ◽  
Approximate Model ◽  
Model Uncertainties ◽  
The Neural Network ◽  
Surface Vessels ◽  
Underactuated Surface Vessels ◽  
Unknown Disturbances ◽  
Robust Adaptive

This paper studies the target-tracking problem of underactuated surface vessels with model uncertainties and external unknown disturbances. A composite robust adaptive self-structuring neural-network-bounded controller is proposed to improve system performance and avoid input saturation. An extended state observer is proposed to estimate the uncertain nonlinear term, including the unknown velocity of the tracking target, when only the measurement values of the line-of-sight range and angle can be obtained. An adaptive self-structuring neural network is developed to approximate model uncertainties and external unknown disturbances, which can effectively optimize the structure of the neural network to reduce the computational burden by adjusting the number of neurons online. The input-to-state stability of the total closed-loop system is analyzed by the cascade stability theorem. The simulation results verify the effectiveness of the proposed method.

scholarly journals Leader–follower close formation control for underactuated surface vessel via terminal hierarchical sliding mode

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092101
Author(s):  
Huizi Chen ◽  
Yan Peng ◽  
Yueying Wang ◽  
Shaorong Xie ◽  
Huaicheng Yan
Keyword(s):  
Finite Time ◽  
Formation Control ◽  
Sliding Mode ◽  
Direct Method ◽  
Model Uncertainties ◽  
Adaptation Mechanism ◽  
Dynamic Surface ◽  
Comparison Results ◽  
Underactuated Surface Vessels ◽  
Hierarchical Sliding Mode

This article is concerned with the close formation problem of multiple underactuated surface vessels in the presence of model uncertainties, roll motion, and environmental disturbances. To effectively address these issues, a novel control scheme considering roll stabilization is designed by combing terminal hierarchical sliding mode control with Lyapunov direct method, which can quickly ensure a small formation error in a finite-time for vessels. Meanwhile, a new switching gain adaptation mechanism is utilized to reduce chattering and acquire faster adaptive rate without the excessive temporary tracking errors. Radial basis function neural network and finite-time observer are employed to deal with model uncertainties and disturbances, respectively. Furthermore, dynamic surface control technology is introduced to reduce the complexity of control law. Various simulations and comparison results are conducted to verify the effectiveness of theoretical results.

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.

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