混合攻击下时变信息物理系统的有限时域H∞控制

本文讨论了一类线性时变信息物理系统(CPS)在有限时域内受到混合攻击的H∞控制问题.上述提到的混合攻击,包括对传感器和控制器之间通信通道发起的拒绝服务(DoS)攻击和对传感器和执行器发起的数据注入(FDI)攻击,其目的在于破坏测量数据和控制数据,从而危及闭环系统的功能.本文的目的在于研究攻击注入信号和被控输出的关系,来设计控制器增益以使闭环系统在有限时域内具有H∞性能;与此同时,减少最坏情况下攻击输入信号对线性二次性能的影响.为了解决以上问题,本文用了随机分析方法和配方法来建立所需的控制器存在的充分条件,并且通过求解一些设定条件下的两个耦合的倒向递推黎卡提差分方程(RDEs),提出了一个有限时域控制器设计算法.最后,本文给出了数值仿真和实验结果,来证明该方法的有效性.     

欺骗攻击下弹性自触发模型预测控制

针对在欺骗攻击下自触发模型预测控制系统的安全控制问题, 本文提出一种基于关键数据保护的弹性自 触发模型预测控制(MPC)策略. 对比现有的自触发MPC, 该方法仅需对少量关键控制样本进行保护, 则可保证闭环 系统稳定运行, 从而有效节省系统资源. 首先, 基于自触发MPC和欺骗攻击的特征推导标称系统与被攻击系统状态之间的误差上界, 从而定量分析出欺骗攻击对系统的损害. 然后, 通过所获得误差上界和李雅普诺夫定理建立关键数据的选取条件并对其实施保护. 最后, 严格证明了在仅对关键控制样本实施保护后, 被控系统仍可在欺骗攻击下保持稳定. 此外, 基于移动机器人和弹簧小车系统对所提算法进行了仿真实验, 结果表明所提算法能够显著节省保护资源, 验证了算法的有效性     

Comments on: ‘Implementation of self-tuning regulators with variable forgetting factors’

The similarities are pointed out between the variable forgetting factor given in the paper by Fortescue, Kershenbaum and Ydstie and that given in Wellstead and Sanoff. Moreover, it is shown that the method of Fortescue, Kershenbaum and Ydstie can be made significantly more efficient, computationally, than is indicated.     

Adaptive input matching control of structurally varying plants

Adaptive control using input matching can avoid the identifiability and/or exact model-matching constraints of the bulk of existing adaptive control methods. The focus on input matchability allows the use of general controller structures capable of controlling a plant whose structure may vary arbitrarily within an allowable class. The applicability of this technique is demonstrated for two simulated plants: a MIMO linear network increasing its complexity due to component failure and an unstable SISO plant controlled by an actuator which gradually develops a nonlinearity. The sole need for pre-specification of an allowable plant structure re-focuses the thrust of system identification from parameter estimation to structure determination.     

基于弹性控制器的网络控制系统稳定性分析

研究了一类不确定网络控制系统的弹性控制器设计问题。针对一类具有不确定参数的被控对象模型,考虑网络诱导时延的作用,建立基于弹性反馈控制器的网络控制系统闭环模型。采用Lyapunov方法,给出保证该闭环系统稳定的允许时延上界。仿真结果证明了本文方法的正确性和有效性。     

一种新的时变系统极点配置自适应控制方法*

本文针对连续快时变系统,提出不要求系统参数未知部分上界的先验信息的自适应算法,得到间接自适应控制方案并且证明闭环系统稳定,仿真结果说明本文的方法有效．     

机器人系统有限时间自适应迭代学习控制

研究任意初态下;机器人系统的有限时间自适应迭代学习控制方法。引入初始修正吸引子的概念;构造一个含有初始修正项的误差变量。针对定常机器人系统和时变机器人系统;采用Lyapunov-like方法;分别设计迭代学习控制器处理系统中不确定性。并且;采用未含/含限幅学习机制;保证闭环系统各变量的一致有界性和误差变量在整个作业区间一致收敛性。藉以实现跟踪误差在预先指定区间的完全跟踪。仿真结果验证所设计控制方法的有效性。     

Orthonormal function parametrisation of model-predictive control for linear time-varying systems

It is well known that some practical difficulties are involved in the implementation of stabilising model predictive control for time-varying systems. In order to address the difficulty of computational load, this paper extends the orthonormal function method for model predictive control to linear time-varying systems. We provide sufficient conditions for a sub-optimal model predictive controller to be stabilising for a time-varying system. It is also shown that the orthonormal parametrisation method enables us to reduce the number of decision variables significantly and with a satisfactory performance. In addition, it is shown that orthonormality and, the called for, long prediction horizons are not necessary for stability. Examples are provided, illustrating the effectiveness of the method for linear time-varying systems.     

A proportional integral extremum-seeking control approach for discrete-time nonlinear systems

This paper proposes a proportional-integral extremum-seeking control technique for a class of discrete-time nonlinear dynamical systems with unknown dynamics. The technique is a generalisation of existing time-varying extremum-seeking control (ESC) techniques that provides fast transient performance of the closed-loop system to the optimum equilibrium of a measured objective function. The main contribution of the proposed technique is the addition of a proportional action that can be used to minimise the impact of a time-scale separation on the transient performance of the ESC system. The integral action fulfils the role of standard ESC techniques to identify optimal equilibrium conditions. The effectiveness of the proposed approach is demonstrated using a simulation example.     

时变机器人系统的重复学习控制：一种混合学习方案(英文)

Repetitive learning control is presented for finite- time-trajectory tracking of uncertain time-varying robotic sys- tems.A hybrid learning scheme is given to cope with the con- stant and time-varying unknowns in system dynamics,where the time functions are learned in an iterative learning way,without the aid of Taylor expression,while the conventional differential learning method is suggested for estimating the constant ones. It is distinct that the presented repetitive learning control avoids the requirement for initial repositioning at the beginning of each cycle,and the time-varying unknowns are not necessary to be periodic.It is shown that with the adoption of hybrid learning, the boundedness of state variables of the closed-loop system is guaranteed and the tracking error is ensured to converge to zero as iteration increases.The effectiveness of the proposed scheme is demonstrated through numerical simulation.     

Necessary and sufficient condition for convergence of iterative learning algorithm

This correspondence is concerned with an iterative learning algorithm for MIMO linear time-varying systems. We provide a necessary and sufficient condition for the existence of a convergent algorithm. The result extends the main result in Saab (IEEE Trans. Automat. Control 40(6) (1995) 1138).