非线性系统的鲁棒采样最优控制

采样系统控制作为一种数字控制的直接设计方法,近年来引起了广泛的重视,另一方面系统的时域约束在工业控制中是不可避免的。利用实用稳定性理论,研究了具有输出约束的一类非线性系统的鲁棒采样最优控制问题,结果表示为一些矩阵不等式,最后给了出了一个迭代算法。     

Takagi-Sugeno模糊连续系统的模糊采样控制

研究了T-S模糊连续系统的模糊采样控制问题．利用广义系统的描述方法、Lyapunov-Krasovikii泛函以及线性矩阵不等式(LMI)方法,建立了LMIs形式的依赖于采样时间间隔的模糊采样镇定条件,同时给出了模糊采样控制律的设计方法．所设计的模糊采样控制律可以镇定T-S模糊系统．而且,当连续时间模糊控制律可以镇定T-S模糊系统时,对于足够小的采样时间间隔,带有同样增益矩阵的模糊采样控制律也可以镇定T-S模糊系统．最后,通过两个仿真实例说明了所给方法的有效性．     

分段线性函数应用于线性时变系统的最优控制

本文给出了分段线性函数的一些运算性质,利用这些性质求解线性时变系统基于二次型性能指标的最优反馈控制律,推导出了形式简明的求解算法,该算法较之于方块脉冲函数算法具有更高的计算精度。     

非线性系统的模糊混合$H_{2}$/H∞采样控制

针对一类非线性系统,提出模糊混合$H_{2     

非线性时延网络控制系统的模糊建模与控制

针对时变网络诱导时延小于一个采样周期的非线性时延网络控制系统,讨论系统的稳定性及控制器的设计方法.利用基于“IF-THEN”规则的模糊模型近似系统中的非线性,将时延的不确定性转化为系统参数的不确定性,从而将此类非线性网络控制系统建模为一类具有参数不确定性的离散Takagi-Sugeno（T-S）模糊模型.基于建立的模型,利用Lyapunov方法和线性矩阵不等式方法,分析了系统的稳定性及模糊状态反馈控制器的设计方法,最后通过仿真实例验证了所提出方法的有效性.     

不确定时变时滞模糊大系统的采样分散可靠H∞双曲控制

研究了时变时滞不确定连续模糊大系统的采样可靠双曲控制. 首先对一类复杂大系统进行模糊双曲建模, 然后根据李亚普诺夫直接方法和大系统的分散控制理论, 得出了基于线性矩阵不等式的条件, 该条件不仅在所有控制元件都有效工作时, 而且在执行器可能存在故障的情况下都能保证系统的性能. 且不需要执行器的精确故障参数, 只需要故障参数的上下界. 该条件只依赖于时滞的上界, 不依赖于时变时滞的导数. 因此得到的条件有更小的保守性. 最后应用两个例子验证了设计过程及其有效性.     

状态时滞系统的输入多采样最优控制

本文针对离散状态时滞系统，首先将其变形为无时滞形式，设计出最优控制器；然后运用离散提升技术对输入进行多采样，得到扩展的离散系统模型，再运用最优控制技术对扩展系统进行最优设计。最后对系统进行仿真，结果表明，该算法具有较好的控制效果。具有较好的稳定性。     

具有时变时延的网络化LPV系统的容错控制

针对一类具有时变时延以及Lipschitz非线性项的网络化线性参数变化系统,研究了系统中存在外部扰动、执行器和传感器同时发生随机故障时的容错控制问题。用Bernoulli分布序列描述执行器和传感器发生的随机故障,利用自由权矩阵方法处理时变时延。根据Lyapunov-Krasovskii稳定性定理和线性矩阵不等式（LMI）方法求出◢H◣▼∞▽容错控制器存在的充分条件,然后通过利用近似基函数和网格化技术将无限维的LMI求解问题转换为有限维的LMI问题,得到了相应的容错控制器增益。最后,通过数值仿真验证了所设计方法的有效性。     

T-S模糊时变时滞系统的稳定性分析

考虑具有时变时滞的Takagi-Sugeno模糊系统稳定性分析的问题.基于Lyapunov 稳定性理论,定义一个新的Lyapunov-Krasovskii泛函,使用恰当的界定技术处理Lyapunov-Krasovskii泛函求导过程中产生的积分项,以线性矩阵不等式的形式给出一个决策变量少、运算效率高、保守性小的时滞相关稳定性准则;此外,在理论推导过程中避免了一些现有文献理论推导过程中出现的问题.最后通过给出的数例验证了该稳定性准则的有效性.     

一类区间时变时滞T-S模糊系统的鲁棒控制

针对一类区同时变时滞T-S模糊系统,研究了其时滞相关渐近稳定性以及控制器设计问题.基于Lyapunov稳定性理论和线性矩阵不等式(LMI)工具,并结合自由权矩阵方法,设计一个包含时滞区间均值在内的新Lyapunov-Krasovskii泛函,给出了改进的时滞T-S模糊系统渐近稳定的时滞相关准则.同时,根据并行分布补偿算法,给出了带有记忆的状态反馈模糊控制器的设计方法.最后,实例仿真表明了方法的有效性.     

Approximate and sampled-data predictors for control of nonlinear delay systems

This tutorial research article presents all recent advances on the use of approximate and sampled-data predictors for time-delay systems. The implementation of predictor feedback for delay systems requires the so-called predictor mapping, i.e., the prediction of the future value of the state vector. However, predictor mappings are rarely available for nonlinear systems. The approximate predictors allow us to overcome this challenge and implement predictor feedback safely for nonlinear time-delay systems even in the case of sampled measurements. The present article reviews the implementation schemes for predictor feedback and provides illustrative examples, which can help the reader to understand the importance of the topic.     

Fuzzy guaranteed cost control for nonlinear systems with time-varying delay

This paper focuses on the problem of guaranteed cost control for Takagi-Sugeno (T-S) fuzzy systems with time-varying delayed state. A linear quadratic cost function is considered as a performance index of the closed-loop fuzzy system. Then, the guaranteed cost control of the closed-loop fuzzy system is discussed, and the sufficient conditions are provided for the construction of a guaranteed cost controller via state feedback and observer-based output feedback. When these conditions, which are given in terms of the feasibility of linear matrix inequalities (LMIs), are satisfied, the designed state feedback controller and observer-based controller gain matrices can be obtained via a convex optimization problem. Two illustrative examples are provided to demonstrate the effectiveness of the approaches proposed in this paper.     

Fuzzy neural-based control for nonlinear time-varying delay systems.

In this paper, a partially known nonlinear dynamic system with time-varying delays of the input and state is approximated by N fuzzy-based linear subsystems described by a state-space model with average delay. To shape the response of the closed-loop system, a set of fuzzy reference models is established. Similarly, the same fuzzy sets of the system rule are employed to design a fuzzy neural-based control. The proposed control contains a radial-basis function neural network to learn the uncertainties caused by the approximation error of the fuzzy model (e.g., time-varying delays and parameter variations) and the interactions resulting from the other subsystems. As the norm of the switching surface is inside of a defined set, the learning law starts; in this situation, the proposed method is an adaptive control possessing an extra compensation of uncertainties. As it is outside of the other set, which is smaller than the aforementioned set, the learning law stops; under this circumstance, the proposed method becomes a robust control without the compensation of uncertainties. A transition between robust control and adaptive control is also assigned to smooth the possible discontinuity of the control input. No assumption about the upper bound of the time-varying delays for the state and the input is required. However, two time-average delays are needed to simplify the controller design: 1) the stabilized conditions for every transformed delay-free subsystem must be satisfied; and 2) the learning uncertainties must be relatively bounded. The stability of the overall system is verified by Lyapunov stability theory. Simulations as compared with a linear transformed state feedback with integration control are also arranged to consolidate the usefulness of the proposed control.     

Decentralised sampled-data control for large-scale systems with nonlinear interconnections

This paper presents a decentralised sampled-data control technique for a class of large-scale systems, which are considered to consist of linear subsystems and nonlinear interconnections. The decentralised sampled-data controller design problem is established using a closed-loop subsystem. Based on the controller design problem, the stability condition is derived for a closed-loop large-scale system, and the maximum interconnection bound is guaranteed to satisfy the stability condition. Also, its sufficient condition is formulated in terms of linear matrix inequalities. Finally, the effectiveness of the proposed technique is verified by using an example of the multi-machine power system.     

Reinforcement learning-based event-triggered optimal control for unknown nonlinear systems with input delay

The optimal control issue of discrete-time nonlinear unknown systems with time-delay control input is the focus of this work. In order to reduce communication costs, a reinforcement learning-based event-triggered controller is proposed. By applying the proposed control method, closed-loop system's asymptotic stability is demonstrated, and a maximum upper bound for the infinite-horizon performance index can be calculated beforehand. The event-triggered condition requires the next time state information. In an effort to forecast the next state and achieve optimal control, three neural networks (NNs) are introduced and used to approximate system state, value function, and optimal control. Additionally, a M NN is utilized to cope with the time-delay term of control input. Moreover, taking the estimation errors of NNs into account, the uniformly ultimately boundedness of state and NNs weight estimation errors can be guaranteed. Ultimately, the validity of proposed approach is illustrated by simulations.     

非线性采样控制系统的Lagrange稳定性

针对由具有外部慢时变输入信号的非线性被控对象和线性数字控制器构成的非线性采样系统,采用非线性跳跃系统方法及其线性化策略,得到了这类系统在理想离散化方式和无限字长数字控制器作用下(即不考虑量化因素)的Lagrange稳定(最终有界)条件.并指出,当考虑数字控制器和接口器件的非线性量化因素影响时,只要非线性量化环节及其相应偏导数有界,则所得稳定性结论仍然成立.