基于多面体方法的时变约束系统控制算法

基于多面体方法的约束系统控制算法能有效地处理存在状态量和控制量凸约束的定常系统的控制问题,采用多面体方法对时变约束系统的稳定控制方法进行了研究．针对时变约束系统的模型参数是变化的特点,提出了一种约束系统的动态控制算法．仿真结果表明,该算法能保证约束时变系统的稳定性．     

非线性时变系统的自耦PID控制方法

针对一类非线性时变系统的控制问题,使用了一种基于自耦PID的控制理论方法.该方法首先将时变不确定、模型不确定定义为一个扩张状态,并将非线性时变系统映射为未知线性系统;然后使用自耦PID控制方法构造了一个闭环系统;最后在复频域分析了闭环系统的鲁棒稳定性和抗扰动鲁棒性.理论分析与仿真结果都表明了本文控制方法具有良好的动态品质和稳态性能,在未知复杂系统控制领域具有广泛的应用前景.     

基于观测器的飞行器时变控制效益估计算法

研究飞行器时变控制效益的在线估计方法,证明了系统控制效益可估计的充分条件. 针对效益矩阵分段连续时变的情形,给出了基于观测器,方程求解和最小二乘法的在线估计方法,分析和证明了算法的收敛性。 数值仿真结果表明,该算法能快速估计时变控制效益,具有良好的稳定性和鲁棒性。

     

T-S模糊神经网络应用于变参数系统的控制研究

针对基于模型的传统控制策略在线性时变系统中的应用受到系统的时变性和不确定性限制,通常难以获得理想的控制性能这一问题,提出了线性时变系统的一种变参数系统模型。该模型具有有界性和不确定性特点,利用模糊神经网络具有的自学习能力强、模型依赖性小以及鲁棒性强的优点,提出一种基于遗传算法的T-S模糊神经网络控制器对其进行控制研究,并通过仿真实验证明了该模糊神经网络控制器对变参数系统控制的可行性与有效性,为线性时变系统的控制问题提供了一种新思路。     

基于观测器的飞行器时变控制效益估计算法

研究飞行器时变控制效益的在线估计方法,证明了系统控制效益可估计的充分条件.针对效益矩阵分段连续时变的情形,给出了基于观测器、方程求解和最小二乘法的在线估计方法,分析和证明了算法的收敛性.数值仿真结果表明,该算法能快速估计时变控制效益,具有良好的稳定性和鲁棒性.     

控制方向未知的全状态约束非线性系统的鲁棒自适应跟踪控制

针对一类控制方向未知的含有时变不确定参数和未知时变有界扰动的全状态约束非线性系统,本文提出了一种基于障碍Lyapunov函数的反步自适应控制方法.障碍Lyapunov函数保证了系统状态在运行过程中始终保持在约束区间内;Nussbaum型函数的引入解决了系统控制方向未知的问题;光滑投影算法确保了不确定时变参数的有界性.障碍Lyapunov函数、Nussbaum型函数及光滑投影算法与反步自适应方法的有效结合首次解决了控制方向未知的全状态约束非线性系统的跟踪控制问题.所设计的自适应鲁棒控制器能在满足状态约束的前提下确保闭环系统的所有信号有界.通过恰当地选取设计参数,系统的跟踪误差将收敛于0的任意小的邻域内.仿真结果表明了控制方案的可行性.     

一种基于过程神经网络的动态系统控制信号求解模型和算法

针对非线性动态系统控制问题,提出了一种基于过程神经网络的控制信号求解模型和算法。利用过程神经网络对动态系统时变输入/输出信号的非线性映射机制和对系统过程模态特征的自适应提取能力,建立基于过程神经网络的辨识模型;然后根据所建立的辨识模型、系统控制结构和状态参数之间的关系,构建可满足系统信息传递约束关系的控制信号求解模型。分析了过程神经网络控制模型的信息处理机制,给出了基于GA与LMS相结合的优化求解算法,实验结果验证了模型和算法的有效性。     

一般广义周期时变系统的H∞控制

针对一般广义周期时变系统的H∞控制问题,采用Lyapunov函数理论和线性矩阵不等式(LMI)相结合的分析方法,通过分析正常周期时变系统和一般广义周期时变系统允许的充分必要条件,得到了一般广义周期时变系统允许且满足H∞控制性能指标的充分必要条件.所得结论将广义系统H∞控制问题研究成果推广到一般广义周期时变系统的控制性能,奠定了广义时变系统H∞控制问题的研究基础.     

时变采样周期网络控制系统的鲁棒容错控制器设计

研究具有时变采样周期网络控制系统的执行器失效的完整性问题．假设系统任意两个连续采样间隔具有上界,利用输入时延法,将时变采样周期网络控制系统等价转化为连续时变时延网络控制系统．在此基础上,基于时延条件,应用Ｌｙａｐｕｎｏｖ稳定性理论和线性矩阵不等式（ＬＭＩｓ）方法证明了鲁棒容错控制律的存在条件,设计了鲁棒容错控制器,并给出了系统完整性条件下的最大允许时延的估计方法．仿真结果验证了所提方法的可行性和有效性．

     

关于网络控制系统中的延迟对系统性能影响的研究

对于有关网络控制系统中存在的延迟对系统的稳定性及其它性能的消极影响及补偿方法，做了较系统的分析．把系统按其延迟大于或小于一个采样周期，定常或时变，确定或随机的不同性质进行分类．并对相继时刻的随机延迟相互独立或者条件相关的不同特点进行了讨论．总结了保证系统均方稳定性的随机控制方法，提出研究网络控制系统的模型方法具有多样性，但其模型框架有值得探讨的一致性，并指出了网络控制系统研究工作创新性之中的鲜明的继承性．     

Robustness of a discrete-time predictor-based controller for time-varying measurement delay

A predictor-based controller for time-varying delay systems is presented in this paper and its robustness properties for different uncertainties are analyzed. First, a time-varying delay dependent stability condition is expressed in terms of LMIs. Then, uncertainties in the knowledge of all plant-model parameters are considered and the resulting closed-loop system is shown to be robust with respect to these uncertainties. A significant improvement with respect to the same control strategy without predictor is achieved. The scheme is applicable to open-loop unstable plants and it has been tested in a real-time application to control the roll angle of a quad-rotor helicopter prototype. The experimental results show good performance and robustness of the proposed scheme even in the presence of long delay uncertainties.     

非线性量化小脑模型关节控制器神经网络控制器

常规小脑模型关节控制器(CMAC)神经网络采用线性均匀量化,稳态控制精度与量化级数相关,增加量化级数可提高稳态精度但会导致内存空间和计算量的增加.本文提出一种可采用幂函数、高斯、分段3种非线性量化方法的非线性量CMAC神经网络,并分析了非线性量化CMAC的收敛性,解释了非线性量化提高稳态精度的本质.面向一阶惯性环节、二阶系统、一阶时变系统及二阶时变系统,分别跟踪方波、斜坡、正弦波、三角波和加速度等输入信号,仿真验证了非线性量化CMAC神经网络控制器的有效性,给出了不同非线性量化方法的适用性.结果表明,非线性量化CMAC参数容易设定,物理意义清晰,与常规CMAC对比,其快速性和控制精度显著提高,可以有效解决实际复杂非线性时变系统的控制.     

Simplified adaptive control via improved robust positive real conditions

A new method is derived for embedding plants in a robust state-feedback scheme, to achieve strictly positive realness of the resulting augmented plants. A state-feedback gain is derived that guarantees the strictly positive realness of the closed-loop in presence of polytopic type, possibly time-varying, parameter uncertainties in the model that describes the plant. This is achieved by assigning different Lyapunov functions to each of the vertices of the uncertainty polytope. The obtained feedback gain is used to apply existing methods for robust simplified adaptive control on systems with possibly time-varying polytopic uncertainties.     

基于联合滤波算法的复杂时变系统在线自适应逆控制

针对一类结构和参数均具备时变特性的复杂时变系统,提出一种新的基于联合滤波算法的在线自适应逆控制方法.该方法在处理参数时变问题的同时可兼顾系统的结构时变特性,实现复杂动态系统的在线跟踪控制.同时提出新的联合Volterra核函数滤波算法,该算法克服了原Volterra滤波器计算复杂运算速度慢的缺点,实现了动态非线性系统的在线跟踪控制.通过仿真分析可以得出,对于此类线性、非线性复杂时变系统,基于新的联合滤波器的自适应逆控制方法可以快速有效的实现动态对象在线建模与控制.     

Robust state-derivative feedback LMI-based designs for multivariable linear systems

In some practical problems, for instance in the control systems for the suppression of vibration in mechanical systems, the state-derivative signals are easier to obtain than the state signals. New necessary and sufficient linear matrix inequalities (LMI) conditions for the design of state-derivative feedback for multi-input (MI) linear systems are proposed. For multi-input/multi-output (MIMO) linear time-invariant or time-varying plants, with or without uncertainties in their parameters, the proposed methods can include in the LMI-based control designs the specifications of the decay rate, bounds on the output peak, and bounds on the state-derivative feedback matrix K. These design procedures allow new specifications and also, they consider a broader class of plants than the related results available in the literature. The LMIs, when feasible, can be efficiently solved using convex programming techniques. Practical applications illustrate the efficiency of the proposed methods.     

Modeling and boundary control of a flexible marine riser coupled with internal fluid dynamics

In this paper, vibration reduction of a flexible marine riser with time-varying internal fluid is studied by using boundary control method and Lyapunov’s direct method. To achieve more accurate and practical riser’s dynamic behavior, the model of marine riser with time-varying internal fluid is modeled by a distributed parameter system (DPS) with partial differential equations (PDEs) and ordinary differential equations (ODEs) involving functions of space and time. The dynamic responses of riser are completely different if the time-varying internal fluid is considered. Boundary control is designed at the top boundary of the riser based on original infinite dimensionality PDEs model and Lyapunov’s direct method to reduce the riser’s vibrations. The uniform boundedness and closed-loop stability are proved based on the proposed boundary control. Simulation results verify the effectiveness of the proposed boundary control.     

Two-loop reinforcement learning algorithm for finite-horizon optimal control of continuous-time affine nonlinear systems

This article proposes three novel time-varying policy iteration algorithms for finite-horizon optimal control problem of continuous-time affine nonlinear systems. We first propose a model-based time-varying policy iteration algorithm. The method considers time-varying solutions to the Hamiltonian–Jacobi–Bellman equation for finite-horizon optimal control. Based on this algorithm, value function approximation is applied to the Bellman equation by establishing neural networks with time-varying weights. A novel update law for time-varying weights is put forward based on the idea of iterative learning control, which obtains optimal solutions more efficiently compared to previous works. Considering that system models may be unknown in real applications, we propose a partially model-free time-varying policy iteration algorithm that applies integral reinforcement learning to acquiring the time-varying value function. Moreover, analysis of convergence, stability, and optimality is provided for every algorithm. Finally, simulations for different cases are given to verify the convenience and effectiveness of the proposed algorithms.     

Design of new adaptive fuzzy logic controller for nonlinear plantswith unknown or time-varying dead zones

An adaptive fuzzy logic controller (FLC) is designed for plants with unknown and/or time-varying dead zones. The steady-state control resolutions with perturbing action, which are different from the ones in the transient states, are used to cancel out the unknown and/or time-varying dead-zone effects. Automatically adjusted control resolutions play a key role as a fuzzy dead-zone inverse. The control resolutions of the control input variables are dependent on the scaling gains of the variables. Therefore, we can develop the fuzzy dead-zone inverse by reperturbing and adjusting the scaling gains adequately in the steady-states. The developed fuzzy logic controllers that are applied to the plants with unknown dead zones ensure their effectiveness even though the dead-zone characteristics are time varying     

一类自同步时变耗散耦合网络内耦合矩阵选择方法

研究使一类时变耗散耦合复杂网络自同步的内耦合矩阵选择方法.在主稳定函数法的基础上,通过对节点变分方程系统阵的约当标准型进行分析,给出了几种适用于不同情况的内耦合矩阵选择方法,以同步速度为依据提出了较优内耦合矩阵的选择命题,并给出了相应的选择方法.最后以3个典型算例验证了所提出方法的有效性.