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Neural-Network-based Finite-Set Model Predictive Control of an Autonomous Surface Vehicle Powered by an Electrical Motor

机译:基于神经网络的电动水面车辆有限集模型预测控制

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This paper is dedicated to a problem of surge speed control for an autonomous surface vehicle (ASV) powered by an electrical motor. The surge dynamics, propeller model, and motor model are unknown. A surge speed controller is developed based on a finite-set model predictive control and a neural predictor design. Firstly, two predictors based on neural networks are developed to estimate the unknown nonlinear functions existing in electrical motor model, propeller model and surge dynamics. The stability analysis is provided on the basis of input-to-state stability (ISS) theory. Then, a model predictive control scheme is proposed for surge speed control with a possible finite voltage control set and a predefined cost function. The simulation results are provided to illustrate the validity of the proposed surge velocity controller for the ASV.
机译:本文致力于解决由电动机驱动的自主地面车辆(ASV)的喘振速度控制问题。喘振动力学,螺旋桨模型和电动机模型是未知的。基于有限集模型预测控制和神经预测器设计开发了喘振速度控制器。首先,开发了两个基于神经网络的预测器,以估计电动机模型,螺旋桨模型和喘振动力学中存在的未知非线性函数。稳定性分析是根据输入状态稳定性(ISS)理论提供的。然后,提出了一种模型预测控制方案,用于具有可能的有限电压控制集和预定义成本函数的喘振速度控制。提供仿真结果以说明所提出的喘振速度控制器对于ASV的有效性。

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