Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles

Abstract

To improve the tracking stability control of unmanned surface vehicles (USVs), an intelligent control algorithm was proposed on the basis of an optimized radial basis function (RBF) neural network. The design process was as follows. First, the adaptation value and mutation probability were modified to improve the traditional optimization algorithm. Then, the improved genetic algorithms (GA) were used to optimize the network parameters online to improve their approximation performance. Additionally, the RBF neural network was used to approximate the function uncertainties of the USV motion system to eliminate the chattering caused by the uninterrupted switching of the sliding surface. Finally, an intelligent control law was introduced based on the sliding mode control with the Lyapunov stability theory. The simulation tests showed that the intelligent control algorithm can effectively guarantee the control accuracy of USVs. In addition, a comparative study with the sliding mode control algorithm based on an RBF network and fuzzy neural network showed that, under the same conditions, the stabilization time of the intelligent control system was 33.33% faster, the average overshoot was reduced by 20%, the control input was smoother, and less chattering occurred compared to the previous two attempts.

Differential Evolution Algorithm‐Based Iterative Sliding Mode Control of Underactuated Ship Motion

“…So, DEA-based adaptive ISMC is designed for ship's path tracking, as shown in Figure 2 . In the parameter optimizer, an optimization system [ 27 , 28 ] is designed, which takes the track deviation y e as the input to realize the adaptive adjustment of the control parameters k 0 ∼ k 5 . Its input is the track deviation y e and the control rudder angle .…”

Adaptive Neural Network Iterative Sliding Mode Course Tracking Control for Unmanned Surface Vessels

“…e particle swarm optimization algorithm is applied to the online optimization of the parameters of the course controller of USVs in [16], which improves the controller's adaptive ability and anti-interference ability. In view of the uncertain factors in the sliding mode control law designed with RBF network, the improved genetic algorithm is adopted to optimize the parameters of RBF network [17] online for obtaining the ideal sliding mode control law [18] to reach the accurate course tracking control for USV. It is a fact that compared with genetic algorithm (GA) and particle swarm algorithm (PSA) in the same situation, the differential evolution algorithm (DEA) [19] is the fastest evolutionary algorithm.…”

Adaptive Backstepping Sliding Mode Tracking Control for Underactuated Unmanned Surface Vehicle With Disturbances and Input Saturation

“…As for the tracking control method of USV, the common methods are backstepping control [4], [5], neural network control [6], [7], adaptive control [8], etc. Each of the above control methods has its own characteristics and limitations.…”