基于区间二型T-S模糊模型的网络控制系统的输出反馈预测控制

针对干扰作用下的非线性网络控制系统,给出了带一个自由控制作用的输出反馈预测控制方法.首先,利用区间二型T-S模糊模型描述具有参数不确定性的非线性对象,采用马尔科夫链描述系统中的随机丢包过程,由此建立了丢包网络环境下的非线性网络控制系统的数学模型.然后,通过引入二次有界技术得到了干扰作用下网络控制系统的稳定性描述方法,并在此基础上给出了状态观测器的线性矩阵不等式条件.最后,基于估计状态,通过将无穷时域控制作用参数化为一个自由控制作用加一个线性反馈律得到了输出反馈预测控制方法.论文的特色在于构建了在线更新误差椭圆集合的基本方法,满足了约束条件下输出反馈预测控制保证稳定性的要求.仿真例子验证了所提方法的有效性.     

一类非线性MIMO系统的模糊自适应输出反馈控制

针对一类MIMO非线性状态不可测系统,提出一种基于观测器的模糊自适应输出反馈控制方法,通过应用“主导输入”的概念,并将自适应控制、H∞控制与模糊逻辑系统相结合,导出了输出反馈控制律以及参数的自适应律.基于李亚普诺夫函数证明了该控制方法可保证闭环系统的全局稳定,并获得了H∞跟踪性能指标.     

一类非线性系统的间接自适应输出反馈模糊控制

针对一类未知非线性系统,提出一种输出反馈控制方法。首先在假设系统状态已知的情况下设计状态反馈控制器,实现跟踪性能。然后在系统状态不完全可测的情况下,通过设计高增益观测器对系统的状态进行估计,实现输出反馈控制器设计。最后证明所设计的输出反馈控制器可获得状态反馈控制器所取得的最大最小问题的跟踪性能。     

非线性系统的模糊自适应输出反馈控制

针对一类未知非线性系统,考虑系统状态不完全可测的情况,利用Lyapunov综合方法设计了一种基于高增益观测器的模糊鲁棒自适应输出反馈控制器,并证明在一定条件下,所设计的输出反馈控制器能获得状态反馈控制器的性能。     

一类模糊非线性系统的直接鲁棒自适应输出反馈控制

针对一类未知非线性系统,利用模糊逻辑系统、H^∞控制和高增益观测器,提出一种模糊直接鲁棒自适应输出反馈控制方法。证明了所设计的输出反馈控制方法不但能保证闭环控制系统稳定,而且可获得在状态反馈控制器下的性能。仿真结果进一步验证了所提出方法的有效性。     

非线性系统的间接自适应模糊输出反馈监督控制

In this paper, an indirect adaptive fuzzy output feedback controller with supervisory mode for a class of unknown nonlinear systems is developed. The proposed approach does not need the availability of the state variables, moreover, a supervisory controller is appended to the adaptive fuzzy controller to force the state to be within the constraint set. Therefore, if the adaptive fuzzy controller cannot maintain the stability, the supervisory controller starts to work to guarantee stability. On the other hand, if the adaptive fuzzy controller works well, the supervisory controller will be deactivated. The overall adaptive fuzzy control scheme guarantees the stability of the whole closed-loop systems. The simulation results confirm the effectiveness of the proposed method.     

Output Tracking Control for Fuzzy Systems Via Output Feedback Design

Fuzzy observer-based control design is proposed to deal with the output tracking problem for nonlinear systems. For the purpose of tracking design, the new concept of virtual desired variables and, in turn the so-called generalized kinematics are introduced to simplify the design procedure. In light of this concept, the design procedure is split into two steps: i) Determine the virtual desired variables from the generalized kinematics; and ii) Determine the control gains just like solving linear matrix inequalities for stabilization problem. For immeasurable state variables, output feedback design is proposed. Here, we focus on a common feature held by many physical systems where their membership functions of fuzzy sets satisfy a Lipschitz-like property. Based on this setting, control gains and observer gains can be designed separately. Moreover, zero tracking error and estimation error are concluded. Three different types of systems, including nonlinear mass-spring systems, dc–dc converters, and induction motors are considered to demonstrate the design procedure. Their satisfactory simulation results verify the proposed approach.     

Indirect Adaptive Fuzzy Output Feedback Control with Supervisory Controller for Uncertain Nonlinear Systems

In this paper, an indirect adaptive fuzzy output feedback controller with supervisory mode for a class of unknown nonlinear systems is developed. The proposed approach does not need the availability of the state variables, moreover, a supervisory controller is appended to the adaptive fuzzy controller to force the state to be within the constraint set. Therefore, if the adaptive fuzzy controller cannot maintain the stability, the supervisory controller starts to work to guarantee stability. On the other hand, if the adaptive fuzzy controller works well, the supervisory controller will be de-activated. The overall adaptive fuzzy control scheme guarantees the stability of the whole closed-loop systems. The simulation results confirm the effectiveness of the proposed method.     

Cooperative Adaptive Fuzzy Output Feedback Control for Synchronization of Nonlinear Multi‐Agent Systems in the Presence of Input Saturation

This paper considers the leader‐following synchronization problem of nonlinear multi‐agent systems with unmeasurable states in the presence of input saturation. Each follower is governed by a class of strict‐feedback systems with unknown nonlinearities and the information of the leader can be accessed by only a small fraction of followers. An auxiliary system is introduced and its states are used to design the cooperative controllers for counteracting the effect of input saturation. By using fuzzy logic systems to approximate the unknown nonlinearities, local adaptive fuzzy observers are designed to estimate the unmeasurable states. Dynamic surface control (DSC) is employed to design distributed adaptive fuzzy output feedback controllers. The developed controllers guarantee that the outputs of all followers synchronize to that of the leader under directed communication graphs. Based on Lyapunov stability theory, it is proved that all signals in the closed‐loop systems are semiglobally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small neighborhood of the origin. An example is provided to show the effectiveness of the proposed control approach.     

具有输入饱和的非线性系统的分散输出控制

考虑了一类具有输入饱和的非线性组合大系统的镇定问题。利用李雅普诺夫稳定理论和矩阵理论研究了这类非线性组合大系统的分散输出镇定问题,并给出了一种分散输出镇定控制器的设计。考虑了另一类具有输入饱和的非线性相似组合大系统,由于相似组合系统的结构特点,给出了简洁的分散输出镇定的条件。     

基于LS-SVM的非线性系统自适应输出反馈控制

针对状态不可测的单输入单输出非线性不确定系统,提出一种基于最小二乘支持向量机(LS-SVM)的直接自适应输出反馈控制方法.该方法首先设计一种误差观测器,间接地估计出系统的状态,然后采用最小二乘支持向量机构造自适应控制器,控制器参数的在线调整规律由李亚普诺夫稳定性理论导出.文中严格证明了闭环系统的渐近稳定性,仿真研究表明了此控制方法的可行性和有效性.     

Adaptive Fixed-time Output Feedback Tracking Control for Uncertain Nonlinear Systems

International Journal of Control, Automation and Systems - This article studies the fixed-time output feedback tracking control based on the command filtered backstepping method for nonlinear...     

模糊奇异摄动系统动态输出反馈H∞控制

针对一类非线性奇异摄动系统,建立了基于T-S 模糊模型的模糊奇异摄动系统模型．通过李亚普诺夫 方法和Schur 补定理,研究其动态输出反馈H∞控制．将系统动态输出反馈H∞控制器设计归结为求解一组与摄动参 数e 无关的线性矩阵不等式,避免了由e 引起的数值求解的病态问题．所获得的控制器使闭环系统渐近稳定,并达 到了给定的H∞性能指标．该方法适用于标准和非标准非线性奇异摄动系统．仿真实例说明了该方法的有效性     

Adaptive Neural Tracking Control for Unknown Output Feedback Nonlinear Time-delay Systems

An adaptive output feedback neural network tracking controller is designed for a class of unknown output feedback nonlinear time-delay systems by using backstepping technique.Neural networks are used to approximate unknown time-delay functions.Delay-dependent filters are intro- duced for state estimation.The domination method is used to deal with the smooth time-delay basis functions.The adaptive bounding technique is employed to estimate the upper bound of the neural network reconstruction error.Based on Lyapunov-Krasoviskii functional,the semi-global uniform ultimate boundedness(SGUUB)of all the signals in the closed-loop system is proved.The arbitrary output tracking accuracy is achieved by tuning the design parameters and the neural node number. The feasibility is investigated by an illustrative simulation example.     

未知输出反馈非线性时滞系统自适应神经网络跟踪控制

An adaptive output feedback neural network tracking controller is designed for a class of unknown output feedback nonlinear time-delay systems by using backstepping technique. Neural networks are used to approximate unknown time-delay functions. Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the neural network reconstruction error. Based on Lyapunov-Krasoviskii functional, the semi-global uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters and the neural node number. The feasibility is investigated by an illustrative simulation example.     

Adaptive Learning Control of Nonlinear Systems by Output Error Feedback

This paper addresses the problem of designing an output error feedback control for single-input, single-output nonlinear systems with uncertain, smooth, output-dependent nonlinearities whose local Lipschitz constants are known. The considered systems are required to be observable, minimum phase with known relative degree and known high frequency gain sign: linear systems are included. The reference output signal is assumed to be smooth and periodic with known period. By developing in Fourier series expansion a suitable periodic input reference signal, an output error feedback adaptive learning control is designed which ldquolearnsrdquo the input reference signal by identifying its Fourier coefficients: bounded closed loop signals and exponential tracking of both input and output reference signals are obtained when the Fourier series expansion is finite, while arbitrary small tracking errors are exponentially achieved otherwise. The resulting control is not model based, is independent of the system order and depends only on the relative degree, the reference signal period and the high frequency gain sign.     

Output Constrained Adaptive Controller Design for Nonlinear Saturation Systems

This paper considers the adaptive neuro-fuzzy control scheme to solve the output tracking problem for a class of strict-feedback nonlinear systems.Both asymmetric output constraints and input saturation are considered.An asymmetric barrier Lyapunov function with time-varying prescribed performance is presented to tackle the output-tracking error constraints.A high-gain observer is employed to relax the requirement of the Lipschitz continuity about the nonlinear dynamics.To avoid the"explosion of complexity",the dynamic surface control(DSC)technique is employed to filter the virtual control signal of each subsystem.To deal with the actuator saturation,an additional auxiliary dynamical system is designed.It is theoretically investigated that the parameter estimation and output tracking error are semi-global uniformly ultimately bounded.Two simulation examples are conducted to verify the presented adaptive fuzzy controller design.     

Adaptive Control of Piecewise Linear Systems with State Feedback for Output Tracking

A piecewise linear system consists of a set of linear time‐invariant (LTI) subsystems, with a switching sequence specifying an active subsystem at each time instant. This paper studies the adaptive control problem of single‐input, single‐output (SISO) piecewise linear systems. By employing the knowledge of the time instant indicator functions of system parameter switches, a new controller structure parametrization is proposed for the development of a stable adaptive control scheme with reduced modeling error in the estimation error signal used for parameter adaptive laws. This key feature is achieved by the new control scheme's ability to avoid a major parameter swapping term in the error model, with the help of indicator functions whose knowledge is available in many applications. A direct state feedback model reference adaptive control (MRAC) scheme is presented for such systems to achieve closed‐loop signal boundedness and small output tracking error in the mean square sense, under the usual slow system parameter switching condition. Simulation results on linearized NASA GTM models are presented to demonstrate the effectiveness of the proposed scheme.     

一类不确定非线性系统的模糊动态输出反馈控制

利用模糊T－S模型对一类不确定非线性系统进行模糊建模,在此基础上研究基于观测器的模糊动态输出反馈控制,给出了模糊闭环系统二次稳定的充分条件及其反馈控制增益和观测器的求法,以及输出反馈控制器的设计方法,仿真结果证明所提出的控制方法是有效的。     

压电陶瓷驱动平台自适应输出反馈控制

压电陶瓷驱动平台的精度和动态特性主要取决于所设计的控制器是否可以有效地补偿压电陶瓷固有的迟滞特性. 针对这一问题, 提出了一种基于神经网络 (Neural network, NN)的自适应输出反馈控制策略. 为了避免压电陶瓷速度测量噪声的影响, 采用高增益观测器对压电陶瓷平台的速度状态进行估计; 为了克服压电陶瓷的迟滞非线性特征, 采用神经网络动态补偿策略; 针对神经网络逼近误差和观测器估计误差, 控制器设计中增加了鲁棒控制项. 最后应用Lyapunov 稳定性理论证明了所提出的控制器的收敛性问题. 仿真实验表明了所提控制方法的有效性.