基于高阶递归神经网络的AUV鲁棒控制方法

目的 提出一种基于高阶递归神经网络的AUV鲁棒控制方法。方法 利用结构简单但逼近效果优越的高阶递归神经网络,对建模不确定性和外部未知干扰进行估计,并将其补偿到输入控制律中,以提高控制性能。之后,基于HJI理论和Lyapunov稳定性分析导出神经网络权重自适应更新律和AUV自适应控制律,设计反步滑模方法作为对比方法,并进行仿真实验。结果 设计的基于高阶递归神经网络的AUV鲁棒控制方法的跟踪误差、调节时间等控制指标均优于反步滑模方法。设计的鲁棒控制方法可以控制AUV精确跟踪目标轨迹,同时具有优秀的控制性能和鲁棒性。结论 这一研究为AUV轨迹跟踪控制领域提供了一种高效且有效的方法,有望在复杂、不确定的水下环境中得到应用。

Solution of the HJI equations for nonlinear H_∞ control design by state-dependent Riccati equations approach

The relationship between the technique by state- dependent Riccati equations （SDRE） and Hamilton-Jacobi-lsaacs （HJI） equations for nonlinear H∞ control design is investigated. By establishing the Lyapunov matrix equations for partial derivates of the solution of the SDREs and introducing symmetry measure for some related matrices, a method is proposed for examining whether the SDRE method admits a global optimal control equiva- lent to that solved by the HJI equation method. Two examples with simulation are given to illustrate the method is effective.

HJI理论下滑模鲁棒控制对磁浮列车悬浮特性影响研究

针对负载扰动和轨道不平顺激励等干扰下磁浮列车悬浮系统的悬浮稳定性问题,提出了一种基于二类李雅普诺夫函数的滑模控制策略使其能够严格保证系统维持在目标悬浮间隙附近。设计了期望悬浮间隙并将其设为虚拟输入,在动力学方程中采取滑模控制算法实现悬浮误差的跟踪控制。根据垂向悬浮间隙的相应约束条件引入HJI理论和李雅普诺夫函数设计相应控制器,使其能够在各种干扰下保证悬浮稳定性。基于李雅普诺夫稳定性定理证明闭环系统稳定性,使系统在受到扰动时能够尽可能的保证悬浮精度并进行误差整定,将悬浮误差收敛到无穷小。对比了现有PID控制算法在同样工况下的悬浮性能,对比了仿真验证所提出控制律的有效性和鲁棒性。

基于HJI理论的双半球胶囊机器人姿态非线性控制方法

考虑到自主研发的双半球胶囊机器人系统不确定性以及外界扰动等非线性特征问题,提出了一种基于Hamilton-Jacobi不等式(HJI)理论的双半球胶囊机器人滑模鲁棒非线性控制策略。通过拉格朗日动力学原理建立双半球胶囊机器人被动模态的动力学模型,并将Hamilton-Jacobi不等式理论应用于设计滑模鲁棒的控制律来确保控制系统的稳定性。此外,通过Lyapunov方程验证了该双半球胶囊机器人基于HJI理论的滑模鲁棒控制系统的有效性,并对所设计的控制器进行了仿真实验,仿真和实验结果表明所设计的控制策略能有效地抑制外部干扰对控制误差的影响,提高了双半球胶囊机器人系统的控制精度。

基于HJI理论的四旋翼飞行器姿态滑模控制设计

由于四旋翼无人机存在建模不确定性以及飞行过程中的外部未知扰动,使得四旋翼无人机姿态控制成为时下的热点问题。本文充分考虑到了上述问题,基于HJI(Hamilton-Jacobi Inequality)理论,设计了相应的非线性滑模控制器,并基于Lyapunov稳定性判别方法给出了严格的理论证明。其数值仿真结果显示,本文提出的基于HJI理论的非线性滑模控制算法针对系统未建模动态以及外部扰动具有较好的鲁棒性,四旋翼飞行器姿态跟踪性能较好。

基于反馈线性化H_∞理论的风电系统桨距角控制

风电系统工作在额定风速以上时,扰动风速会导致输出功率出现脉动,这会对电网的稳定性产生不利影响。为了解决这个实际问题,将非线性干扰抑制理论应用到桨距角控制律的设计中,建立了整个机组含风速扰动项的非线性数学模型;采用精确线性化方法将问题转化为标准的H∞控制问题,以避免HJI不等式的求解,同时还可以保证系统在工况点大范围变动时仍具有较好的控制性能。利用MATLAB/Simulink进行了对比仿真,结果表明该控制策略对风速扰动具有抑制作用,能够实现功率的平稳输出,适应工况点大范围变动的情形,控制性能要优于基于单个工况点近似线性化模型的控制方法。

基于对角递归神经网络的AUV非线性H∞控制

为了提高自主式潜航器(autonomous underwater vehicle, AUV)在作业期间的运动控制精度、鲁棒性能,提出一种融合HJI理论和递归神经网络的运动控制策略。考虑多种扰动因素建立动力学模型,引入对角递归神经网络实现系统中存在的多种不确定性和控制输入受限的有效补偿。以HJI不等式为基础设计鲁棒控制律,借助李雅普诺夫第二法证明设计的控制系统具有稳定性。仿真结果表明该方法的可行性和有效性,与对比方法相比,位置跟踪误差平均值减少50%以上,具有更高的控制精度、抗干扰能力,实现了对非线性轨迹的稳定跟踪控制。

A physics-informed deep learning framework for spacecraft pursuit-evasion task assessment

Qualitative spacecraft pursuit-evasion problem which focuses on feasibility is rarely studied because of high-dimensional dynamics,intractable terminal constraints and heavy computational cost.In this paper,A physics-informed framework is proposed for the problem,providing an intuitive method for spacecraft threat relationship determination,situation assessment,mission feasibility analysis and orbital game rules summarization.For the first time,situation adjustment suggestions can be provided for the weak player in orbital game.First,a dimension-reduction dynamics is derived in the line-of-sight rotation coordinate system and the qualitative model is determined,reducing complexity and avoiding the difficulty of target set presentation caused by individual modeling.Second,the Backwards Reachable Set(BRS)of the target set is used for state space partition and capture zone presentation.Reverse-time analysis can eliminate the influence of changeable initial state and enable the proposed framework to analyze plural situations simultaneously.Third,a time-dependent Hamilton-Jacobi-Isaacs(HJI)Partial Differential Equation(PDE)is established to describe BRS evolution driven by dimension-reduction dynamics,based on level set method.Then,Physics-Informed Neural Networks(PINNs)are extended to HJI PDE final value problem,supporting orbital game rules summarization through capture zone evolution analysis.Finally,numerical results demonstrate the feasibility and efficiency of the proposed framework.

H-infinity control for air-breathing hypersonic vehicle based on online simultaneous policy update algorithm

Purpose–The air-breathing hypersonic vehicle(AHV)includes intricate inherent coupling between the propulsion system and the airframe dynamics,which results in an intractable nonlinear system for the controller design.The purpose of this paper is to propose an H1 control method for AHV based on the online simultaneous policy update algorithm(SPUA).Design/methodology/approach–Initially,the H1 state feedback control problem of the AHV is converted to the problem of solving the Hamilton-Jacobi-Isaacs(HJI)equation,which is notoriously difficult to solve both numerically and analytically.To overcome this difficulty,the online SPUA is introduced to solve the HJI equation without requiring the accurate knowledge of the internal system dynamics.Subsequently,the online SPUA is implemented on the basis of an actor-critic structure,in which neural network(NN)is employed for approximating the cost function and a least-square method is used to calculate the NN weight parameters.Findings–Simulation study on the AHV demonstrates the effectiveness of the proposed H1 control method.Originality/value–The paper presents an interesting method for the H1 state feedback control design problem of the AHV based on online SPUA.