Abstract
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.
Highlights
In order to verify the effectiveness of the proposed model identification based on RBF neural network (RBFNN), the random step signals are used as the excitation signal to obtain the state response of the underwater vehicle, which are taken as the model training sample
The tracking performance of adaptive gray wolf optimization (AGWO) is significantly improved in each degree of freedom compared with other optimization algorithms
An adaptive RBF-nonlinear model predictive control (NMPC) trajectory tracking control algorithm is proposed for underwater vehicles
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The real-time performance of MPC is affected by dynamic model complexity and the rolling optimization algorithm [13]. How to establish an accurate nonlinear model while ensuring real-time performance is important in the development of NMPC. (2) In the real-time control stage, the neural network parameters are adjusted and updated online according to the prediction error, which improves the adaptive ability of the controller in the complex underwater environment. (3) Based on the traditional gray wolf optimization (GWO) algorithm, the idea of adaptive weight and worst-case crossover is added to improve the global search ability and convergence speed, so as to ensure the real-time performance of NMPC.
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