随机开放量子系统的纯态开关反馈控制EI北大核心CSCD

针对目标态为纯态的情况,本文对有限维随机开放量子系统,提出一种同时适用于本征态和叠加态的开关控制,它是由常量控制和基于李雅普诺夫方法设计的控制律组成,实现随机开放量子系统的状态转移和收敛控制,其中,李雅普诺夫函数为系统的状态距离,常量控制用来驱动系统状态从初始状态进入含有目标态的收敛域中,李雅普诺夫控制用来使进入收敛域中的状态继续收敛到期望的目标态.将所提出的控制方法,应用于2比特随机开放量子系统进行了数值仿真实验,并与本征态开关控制律方法进行了性能对比,实验结果表明了所提出的控制律的优越性.     

李雅普诺夫函数优化:正交矩阵构造方案EI北大核心CSCD

本文研究了李雅普诺夫函数的优化问题.提出了一种正交矩阵构造方案,用于求解黎卡提不等式中的最优李雅普诺夫函数.通过分析系统H_(∞)范数的几何特征,本文将黎卡提不等式转换为近似等式,进而给出了最优李雅普诺夫函数的存在条件.基于所给最优李雅普诺夫函数存在条件,所提正交矩阵构造方案利用旋转变换,将非线性方程组的求解问题转换为幅值和角度的线性优化问题,进而实现李雅普诺夫函数参数的优化.研究结果弥补了目前的研究无法求解最优李雅普诺夫函数的不足,对系统性能分析和非保守控制的设计具有建设性.算例验证了所提正交矩阵构造方案的有效性.     

带有输出约束的柔性关节机械臂预设性能自适应控制

针对带有输出约束和模型不确定的柔性关节机械臂系统,提出一种基于时变障碍李雅普诺夫函数的预设性能自适应控制方法.通过构造指数衰减的时变约束边界,提出时变正切型障碍李雅普诺夫函数,能够同时适用于约束与非约束情况,进而拓宽传统对数型障碍李雅普诺夫函数的适用范围.此外,通过预先设置时变边界函数的相关参数,使得系统输出在初始阶段具有较小的超调量和较快的跟踪速度,并能够满足系统的稳态性能要求.在此基础上,结合反演法设计反馈控制律,保证系统的输出约束性能和轨迹跟踪精度.最后,基于李雅普诺夫稳定性定理证明所有闭环信号能够达到一致最终有界,并给出数值仿真对比验证所提出方法的有效性.     

关于Sontag-Type控制的注记

基于控制李雅普诺夫函数的Sontag-Type控制是仿射系统鲁棒镇定中的重要控制律.首先揭示该控制律本质上是一种变结构控制且闭环的切换面总可达,受此启发并为了相对容易地构造控制李雅普诺夫函数,运用零状态可检测概念定义弱控制李雅普诺夫函数,并证明了基于弱控制李雅普诺夫函数的Sontag-Type控制的优化镇定性.文中还证明,在温和条件下,基于弱控制李雅普诺夫函数的Sontag＿Type控制为仿射系统的输入到状态镇定控制.     

带有时变输出约束的机械臂自适应轨迹跟踪控制

针对带有输出约束和动力学模型参数未知的机械臂系统，提出一种基于时变tan型障碍李雅普诺夫函数的自适应控制方法.首先，通过设置时变约束边界，给出了一个时变tan型障碍李雅普诺夫函数，保证系统在初始误差较大情况下的瞬态性能和稳态性能，拓展了传统对数型障碍李雅普诺夫函数的适用范围.其次，为了处理机械臂动力学模型的不确定性，采用径向基神经网络(RBFNN)拟合未知的动力学模型，设计了基于RBFNN的自适应控制器，在满足约束的情况下提高了系统的鲁棒性.最后，通过二自由度机械臂轨迹跟踪的仿真，验证了所提方法的控制性能优于传统的PD控制器.     

航天器全状态约束输出反馈控制

针对无角速度测量的刚性航天器姿态跟踪问题,提出一种全状态约束输出反馈控制方法.建立修正罗德里格参数描述的系统模型,提出能够适用于约束与非约束情况的改进型障碍李雅普诺夫函数(MBLF),拓展传统对数型障碍李雅普诺夫函数的适用范围.构造二阶辅助系统,将控制输入和饱和输入之间的差作为构造系统的输入,进而产生信号以补偿饱和的影响.设计状态观测器估计未知状态量,并结合反步法设计输出反馈控制律,保证系统全状态约束性能和姿态跟踪精度.通过李雅普诺夫稳定性分析证明姿态观测误差和跟踪误差能够达到一致最终有界.仿真结果验证所提方法的有效性.     

带有输出约束条件的随机多智能体系统容错控制

在有向通讯拓扑图下,针对一类具有输出约束和执行器偏差增益故障的非严格反馈随机多智能体系统,提出一种自适应神经网络容错控制设计方案.采用神经网络逼近未知非线性函数,构造障碍李雅普诺夫函数处理系统的输出约束问题,以反步法和动态面技术为框架,结合Nussbaum函数设计自适应神经网络容错控制方法.基于李雅普诺夫稳定性理论,证明所有跟随者输出与领导者输出达到一致,闭环系统的所有信号依概率半全局一致最终有界且系统输出限制在给定紧集内.论文最后通过仿真实验验证所给出控制方案的有效性.     

Non-Markovian开放量子系统的特性分析与状态转移

针对时间无卷积的二能级Non-Markovian开放量子系统, 分别研究了环境截断频率、耦合系数和系统振荡频率对系统衰减系数、相干性和纯度的影响, 并根据不同数值对系统性能影响的分析结果来确定合适的系统仿真实验的参数; 基于李雅普诺夫稳定性定理设计了用于系统状态转移的控制场; 在Matlab环境下进行了系统数值仿真实验,研究了Non-Markovian系统自由演化轨迹的特性, 以及在所设计的控制器作用下纯态到纯态的状态转移,并通过性能对比实验, 验证了所提出的量子李雅普诺夫控制方法应用于Non-Markovian系统状态转移的有效性, 同时分析了控制参数、截断频率参数对控制系统性能的影响.     

基于积分反步法的轧机速度系统控制器设计

本文给出了基于积分反步法的控制器设计方法，该方法通过逐步修正算法设计镇定控制器，实现系统的全局调节或跟踪。每一步把状态坐标变换、一个给定的李雅普诺夫函数和虚拟控制联系起来，最终得到一个控制李雅普诺夫函数(clf)。基于上述方法，对直流电机驱动的轧机速度系统进行了反馈控制器设计。仿真研究结果表明，本文所设计的反馈控制器使闭环系统稳定，系统具有良好的跟踪性能。     

Markov跳跃非线性系统逆最优增益设计

证明了一类严格反馈Markov跳跃系统是依概率输入–状态可稳定的.其次,证明了逆最优增益设计问题可解的一个充分条件是存在一组满足小控制量的依概率输入–状态稳定控制李雅普诺夫函数.最后,利用积分反推方法,给出了严格反馈Markov跳跃系统逆最优增益设计问题的一个构造性解.其中,为了克服由于Markov跳跃引起的耦合项所带来的困难,所设计的李雅普诺夫函数以及控制器是与模态无关的.     

Entanglement classification via integer partitions

Quantum state engineering is a central task in Lyapunov-based quantum control. Given different initial states, better performance may be achieved if the control parameters, such as the Lyapunov function, are individually optimized for each initial state, however, at the expense of computing resources. To tackle this issue, we propose an initial-state-adaptive Lyapunov control strategy with machine learning. Specifically, artificial neural networks are used to learn the relationship between the optimal control parameters and initial states through supervised learning with samples. Two designs are presented where the feedforward neural network and the general regression neural network are used to select control schemes and design Lyapunov functions, respectively. We demonstrate the performance of the designs with a three-level quantum system for an eigenstate control problem. Since the sample generation and the training of neural networks are carried out in advance, the initial-state-adaptive Lyapunov control can be implemented for new initial states without much increase of computational resources.

     

Implicit Lyapunov control of finite dimensional Schrödinger equations

An implicit Lyapunov-based approach is proposed for generating trajectories of a finite dimensional controlled quantum system. The main difficulty comes from the fact that we consider the degenerate case where the linearized control system around the target state is not controllable. The controlled Lyapunov function is defined by an implicit equation and its existence is shown by a fix point theorem. The convergence analysis is done using LaSalle invariance principle. Closed-loop simulations illustrate the performance of such feedback laws for the open-loop control of a test case considered by chemists.     

Feedback stabilization of nonlinear systems by locally bounded controls

In this paper results are presented on output regulation and stabilization of nonlinear control systems using Lyapunov-based methods. Sufficient conditions for global output regulation and stabilization using locally bounded state feedback are obtained. The approach used here is motivated by the work of Artstein (1983), Sontag (1989) and Tsinias (1989) on the relationship between control Lyapunov functions and feedback stabilization.     

State transfer based on decoherence-free target state by Lyapunov-based control

We propose a Lyapunov-based control approach for state transfer based on the decoherence-free target state.The expected target state is constructed to be a decoherence-free state in a decoherence-free subspace(DFS) by an external laser fieldⅠ,so that the system state can be decoupled from the environment,and no more decoherence process will occur.With the decoherence-free target state,we design a Lyapunov-based control fieldⅡto steer the given initial state to the decoherence-free state of open quantum systems as completely as possible,and decouple the system state from the environment at the same time.In the end,it is verified that the state transfer control designed comes true on a∧-type four-level atomic system,and the system can stay on the decoherence-free target state without coupling to environment.     

Stabilization of uncertain chained form systems within finite settling time

This note considers finite time stabilization of uncertain chained form systems. The objective is to design a nonsmooth state feedback law such that the controlled chained form system is both Lyapunov stable and finite-time convergent within any given settling time. We propose a novel switching control strategy with help of homogeneity, time-rescaling, and Lyapunov-based method. Also, the simulation results show the effectiveness of the proposed control design approach.     

Feedback stabilization for a class of discontinuous systems driven by integrator

This paper investigates the feedback stabilization problem for a class of discontinuous systems which is characterized by Filippov differential inclusion. Lyapunov-based backstepping design method is generalized with nonsmooth Lyapunov functions to solve the control problem. A set-valued time derivative is introduced first for nonsmooth function along discontinuous vector fields, which enables us to perform Lyapunov-based design with nondifferentiable Lyapunov function. Conditions for designing a virtual control law which is shown nondifferentiable in general in the recursive design problem are proposed. Finally, as a special case, piecewise linear system is discussed to demonstrate the application of the presented design approach.     

Predictive control of switched nonlinear systems with scheduled mode transitions

In this work, a predictive control framework is proposed for the constrained stabilization of switched nonlinear systems that transit between their constituent modes at prescribed switching times. The main idea is to design a Lyapunov-based predictive controller for each constituent mode in which the switched system operates and incorporate constraints in the predictive controller design which upon satisfaction ensure that the prescribed transitions between the modes occur in a way that guarantees stability of the switched closed-loop system. This is achieved as follows: For each constituent mode, a Lyapunov-based model predictive controller (MPC) is designed, and an analytic bounded controller, using the same Lyapunov function, is used to explicitly characterize a set of initial conditions for which the MPC, irrespective of the controller parameters, is guaranteed to be feasible, and hence stabilizing. Then, constraints are incorporated in the MPC design which, upon satisfaction, ensure that: 1) the state of the closed-loop system, at the time of the transition, resides in the stability region of the mode that the system is switched into, and 2) the Lyapunov function for each mode is nonincreasing wherever the mode is reactivated, thereby guaranteeing stability. The proposed control method is demonstrated through application to a chemical process example.     

严格反馈系统约束迭代学习控制

针对一类含有状态约束和任意初态的严格反馈非线性系统,本文提出了基于二次分式型障碍李雅普诺夫函数的误差跟踪学习控制算法.二次分式型障碍李雅普诺夫函数保证了系统跟踪误差在迭代过程中限制于预设的界内,进而保持状态在约束区间内.引入一级数收敛序列用于处理扰动对系统跟踪性能的影响.构造期望误差轨迹解决了系统的初值问题.经迭代学习后,所设计的学习控制器能够实现系统输出在预指定作业区间上精确跟踪参考信号.最后的仿真结果验证了所提控制算法的有效性.