基于事件触发机制的动力定位系统神经自适应控制

Neural adaptive control of a dynamic positioning system based onan event-triggered mechanism

针对含模型参数不确定和环境干扰的水面船动力定位控制问题,本文提出了一种基于事件触发机制的神经自适应控制算法。结合径向基函数神经网络和最小学习参数算法设计自适应项补偿环境干扰和模型参数不确定。设计的自适应项仅有3个在线学习参数,减少了传统神经网络自适应技术的参数学习个数。再结合动态面控制技术和事件触发机制设计动力定位控制器,其中引入一种事件触发机制降低控制器到执行机构的信息传输负担,同时降低执行机构的动作次数。使用Lyapunov稳定性理论证明了闭环系统的稳定性。通过仿真试验和对比分析验证了提出控制律的有效性。

This paper proposes a neural adaptive control algorithm based on an event-triggered mechanism for solving the dynamic positioning control of surface vessels with model parameter uncertainties and environmental disturbances.First,an adaptive item is designed to compensate for the environmental disturbances and model parameter uncertainties by using the radial basis function neural network and the minimum learning parameter algorithm.The designed adaptive item has only three online learning parameters,thereby reducing the number of learning parameters of the traditional neural network adaptive methods.A dynamic positioning controller is designed by combining the dynamic surface control technology and an event-triggering mechanism,wherein the latter is introduced to reduce the load of information communication from the controller to the actuator and concurrently lower the execution rate of the actuators.Then,the stability of the closed-loop system is analyzed using the Lyapunov theory.Finally,the effectiveness of the proposed control law is verified through simulation and comparative analysis.