Predictive control based target tracking control for a carangiform robotic fish

2016 ◽  
Author(s):  
Siyuan Chen ◽  
Songlin Chen ◽  
Chang Liu ◽  
Baoqing Yang ◽  
Feitian Zhang
Keyword(s):  
Target Tracking ◽  
Predictive Control ◽  
Tracking Control ◽  
Robotic Fish

Backstepping-Based Hybrid Target Tracking Control for a Carangiform Robotic Fish

2013 ◽  
Author(s):  
Songlin Chen ◽  
Jianxun Wang ◽  
Xiaobo Tan
Keyword(s):  
Target Tracking ◽  
Tracking Control ◽  
Open Loop ◽  
Rigid Body Dynamics ◽  
Robotic Fish ◽  
Target Point ◽  
Hybrid Controller ◽  
Control Scheme ◽  
Body Theory ◽  
Hybrid Target

In this paper we apply backstepping technique to develop a novel hybrid target-tracking control scheme for a carangiform robotic fish, based on a dynamic model that combines rigid-body dynamics with Lighthill’s large-amplitude elongated-body theory. This hybrid controller consists of an open-loop turning controller and a closed-loop approaching controller. A hysteretic switching strategy based on the orientation error is designed. Using Lyapunov analysis, we show that the trajectory of the robotic fish will converge to the target point. The effectiveness of the proposed control strategy is demonstrated through both simulations and experiments.

scholarly journals An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 105
Author(s):  
Zhenzhong Chu ◽  
Da Wang ◽  
Fei Meng
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Real Time ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Working Environment ◽  
Gray Wolf ◽  
Trajectory Tracking Control ◽  
System Output

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

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