全状态约束的时滞系统神经网络输出反馈控制

针对一类严格反馈形式的单输入单输出时滞系统,研究在全状态约束下的输出反馈控制.首先,设计状态观测器估计不可测量的状态;其次,利用RBF神经网络逼近未知的非线性函数,利用障碍Lyapunov函数确保全状态约束及Lyapunov-Krasovskii方法消除时滞对系统的影响;最后,设计输出反馈控制器,并且有更少的更新参数减少了计算负荷.所设计的控制器可以保证闭环系统中所有信号半全局一致最终有界,信号误差收敛到小的领域内.仿真例子进一步验证了所提出方法的有效性.     

随机时滞系统的神经网络输出反馈动态面控制

针对一类具有未知控制方向的随机时滞系统设计自适应神经输出反馈控制器.首先,利用状态观测器估计不可测量的系统状态;其次,选择合适的Lyapunov-Krasovskii函数消除未知延迟项对系统的影响,利用Nussbaum-type函数处理系统的未知控制方向问题,通过神经网络逼近未知的非线性函数,以及用动态表面控制(DSC)解决控制器设计中出现的复杂性问题;最后,通过Lyapunov稳定性理论,构造一个鲁棒自适应神经网络输出反馈控制器,可以保证闭环系统中所有信号在二阶或四阶矩意义下一致最终有界,跟踪误差能收敛到零值小的领域内.仿真实例验证了所提出方法的有效性.     

输出非对称死区的非严格反馈非线性系统控制

本文研究了具有输出非对称死区和状态含未知控制方向的非严格反馈非线性系统, 设计了稳定的自适应 神经网络控制器. 首先, 针对输出非对称死区的问题, 本文采用死区逆的方法, 构造光滑模型逼近原死区模型. 其 次, 在控制器设计过程中, 基于障碍Lyapunov函数的构造, 动态面控制和反步法, 设计出自适应控制信号, 虚拟控制 信号和实际控制信号. 通过稳定性分析, 证明所设计的神经网络控制器可以保证闭环系统内所有信号是半全局一致 最终有界. 最后, 通过MATLAB数值仿真, 说明所设计控制器的有效性.     

一种简化的自适应神经网络输出反馈镇定

针对一类输出反馈非线性时滞系统,提出一种简化的自适应神经网络镇定算法.所设计的状态观测器和控制器不依赖于时滞.不同于现有的结果,系统的时滞项假定完全未知,仅采用一个神经网络补偿所有未知非线性函数,因此控制器设计更加简单,而且最终的闭环系统被证明是半全局渐近稳定的.仿真结果进一步验证了该控制方案的有效性.     

基于观测器的受扰多项式系统H∞输出跟踪控制

考虑一类受到外部扰动影响的多项式系统在状态不完全可测情况下的H_∞输出跟踪控制问题.首先,综合前馈-反馈复合控制思想,设计基于观测器的输出跟踪控制器,其中反馈镇定控制器用于保证闭环系统稳定,前馈补偿控制器用以实现对参考模型输出信号的跟踪;然后,提出具有输出反馈结构的跟踪控制方法,其优势在于实现了分离原则,可单独设计观测器和控制器,降低计算复杂度;接着,利用依赖全状态的齐次多项式Lyapunov函数导出使得闭环系统渐近稳定且满足H_∞跟踪性能的充分条件,借助多项式平方和凸优化技术可直接求得相应的观测器和控制器;最后,通过数值仿真实例验证所提出设计方法的有效性和优越性.     

一类具有未知控制方向非线性系统的输出反馈自适应控制

研究了一类控制方向未知非线性系统的输出反馈自适应镇定问题. 首先, 通过一线性状态变换, 将未知控制系数集中起来, 从而将原系统变换为适于控制设计的新系统. 然后, 分别引入状态观测器和参数估计器, 并应用积分反推和调节函数方法, 给出了输出反馈稳定控制律的构造性设计过程. 可以证明,所设计的控制器确保原系统状态渐近收敛到原点, 而其它闭环系统状态有界. 仿真结论验证了所提出方法的有效性.     

随机非线性严格反馈系统的自适应神经网络输出反馈镇定

针对具有严格反馈形式的随机非线性系统, 首次引入神经网络控制技术, 设计了适当形式的随机控制 Lyapunov函数, 并运用反推(Backstepping)技术和非线性观测器设计技术, 构造出一类自适应神经网络输出反馈控制器. 在一定条件下, 证明了闭环系统平衡点依概率稳定. 仿真算例验证了所给控制方案的有效性.     

具有非匹配扰动的非线性变参数系统模型参考跟踪控制

本文研究了一类含有非匹配扰动的非线性变参数系统的跟踪控制问题.首先,设计非线性扰动观测器用于估计系统所受到的未知扰动.其次,在前馈–反馈跟踪控制器中引入扰动补偿控制项,提出一种基于扰动观测器的跟踪控制策略.利用依赖于状态和时变参数的线性矩阵不等式,导出保证闭环系统输入–状态稳定的充分条件,进而运用平方和凸优化技术解析地构造出扰动观测器和跟踪控制器.通过理论证明,所设计的控制策略能够实现非线性变参数系统输出对参考模型输出的跟踪,消除输出通道中非匹配扰动的影响.最后,由数值仿真例子验证了所提方法的有效性.     

具有预设性能的切换系统输出反馈自适应动态面控制

本文针对一类具有未建模动态和预设性能的输出反馈非线性切换系统,提出基于公共Lyapunov函数法的自适应输出反馈动态面控制方案.通过设计K滤波器和观测器估计不可测量的状态.引入动态信号处理动态不确定性.利用Nussbaum函数解决增益符号未知的问题.神经网络用于逼近由设计过程和理论分析所产生的未知连续函数.引入性能函数和误差转换器将预设性能控制问题转换为稳定性问题.通过适当选取切换子系统的初值,并利用动态面控制系统证明的特点,证明了闭环切换系统所有信号半全局一致终结有界.仿真例子验证了所提方案的有效性.     

状态观测的未知死区非线性系统的自适应神经网络跟踪控制

研究一类包含不确定项和未知死区特性的严格反馈系统跟踪控制问题.首先,设计状态观测器估计不可测量的系统状态;然后,利用RBF神经网络逼近未知的系统动态;最后,基于Backstepping技术构造自适应神经网络输出反馈控制器,并减少更新参数以减轻运算负荷.所提出的控制器可以保证闭环系统中所有信号半全局最终一致有界,跟踪误差能收敛到零值小的领域内.两个仿真例子进一步验证了所提出方法的有效性.     

Adaptive neural output feedback tracking control for a class of nonlinear systems

In this paper, an adaptive neural output feedback control scheme based on backstepping technique and dynamic surface control (DSC) approach is developed to solve the tracking control problem for a class of nonlinear systems with unmeasurable states. Firstly, a nonlinear state observer is designed to estimate the unmeasurable states. Secondly, in the controller design process, radial basis function neural networks (RBFNNs) are utilised to approximate the unknown nonlinear functions, and then a novel adaptive neural output feedback tracking control scheme is developed via backstepping technique and DSC approach. It is shown that the proposed controller ensures that all signals of the closed-loop system remain bounded and the tracking error converges to a small neighbourhood around the origin. Finally, two numerical examples and one realistic example are given to illustrate the effectiveness of the proposed design approach.     

Discrete-time neural network output feedback control of nonlinear discrete-time systems in non-strict form

An adaptive neural network (NN)-based output feedback controller is proposed to deliver a desired tracking performance for a class of discrete-time nonlinear systems, which are represented in non-strict feedback form. The NN backstepping approach is utilized to design the adaptive output feedback controller consisting of: (1) an NN observer to estimate the system states and (2) two NNs to generate the virtual and actual control inputs, respectively. The non-causal problem encountered during the control design is overcome by using a dynamic NN which is constructed through a feedforward NN with a novel weight tuning law. The separation principle is relaxed, persistency of excitation condition (PE) is not needed and certainty equivalence principle is not used. The uniformly ultimate boundedness (UUB) of the closed-loop tracking error, the state estimation errors and the NN weight estimates is demonstrated. Though the proposed work is applicable for second order nonlinear discrete-time systems expressed in non-strict feedback form, the proposed controller design can be easily extendable to an nth order nonlinear discrete-time system.     

Output feedback stabilization for time-delay nonlinear interconnected systems using neural networks.

In this paper, dynamic output feedback control problem is investigated for a class of nonlinear interconnected systems with time delays. Decentralized observer independent of the time delays is first designed. Then, we employ the bounds information of uncertain interconnections to construct the decentralized output feedback controller via backstepping design method. Based on Lyapunov stability theory, we show that the designed controller can render the closed-loop system asymptotically stable with the help of the changing supplying function idea. Furthermore, the corresponding decentralized control problem is considered under the case that the bounds of uncertain interconnections are not precisely known. By employing the neural network approximation theory, we construct the neural network output feedback controller with corresponding adaptive law. The resulting closed-loop system is stable in the sense of semiglobal boundedness. The observers and controllers constructed in this paper are independent of the time delays. Finally, simulations are done to verify the effectiveness of the theoretic results obtained.     

Output feedback control of nonlinear systems subject to sensor data losses

In this work, we focus on output feedback control of nonlinear systems subject to sensor data losses. We initially construct an output feedback controller based on a combination of a Lyapunov-based controller with a high-gain observer. We then study the stability and robustness properties of the closed-loop system in the presence of sensor data losses for both the continuous and sampled-data systems. We state a set of sufficient conditions under which the closed-loop system is guaranteed to be practically stable. The theoretical results are demonstrated using a chemical process example.     

含不匹配扰动非严格反馈系统的输出反馈补偿控制

针对一类非严格反馈非线性系统,系统中包含不确定函数和未知外部扰动,提出一种带不匹配扰动补偿的输出反馈模糊控制器.采用模糊逻辑系统逼近未知的非线性函数,同时构造模糊状态观测器观测系统未知状态.考虑观测器和控制器会受到外部扰动和模糊逼近误差构成的不匹配总扰动信号影响,采用改进的扰动观测器对不匹配扰动进行估计和补偿,使扰动观...     

Adaptive neural network control of nonlinear systems by state andoutput feedback

This paper presents a novel control method for a general class of nonlinear systems using neural networks (NNs). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the output is measurable, by using a high-gain observer to estimate the derivatives of the system output, an adaptive output feedback NN controller is proposed. The closed-loop system is proven to be semi-globally uniformly ultimately bounded (SGUUB). In addition, if the approximation accuracy of the neural networks is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussions.     

Disturbance-observer-based sampled-data adaptive output feedback control for a class of uncertain nonlinear systems

In this paper, a sampled-data adaptive output feedback controller is proposed for a class of uncertain nonlinear systems with unmeasured states, unknown dynamics and unknown time-varying external disturbances. To approximate uncertain nonlinear functions, radial basis function neural networks (RBFNNs) are employed. The state observer and the disturbance observer (DO) are constructed to estimate the unmeasured state and the external disturbance, respectively. Then, the sampled-data adaptive output feedback controller and adaptive laws are designed by using the backstepping design technique. The allowable sampling period T is derived to guarantee that all states of the resulting closed-loop system are semi-globally uniformly ultimately bounded. Finally, two simulation examples are presented to illustrate the effectiveness of the proposed approach.     

Global output feedback stabilization of upper-triangular nonlinear time-delay systems

In this paper,the problem of global output feedback stabilization for a class of upper-triangular nonlinear systems with time-varying time-delay in the state is considered.The uncertain nonlinearities are assumed to be higher-order in the unmeasurable states.Based on the extended homogeneous domination approach,using a low gain observer in combination with controller,the delay-independent output feedback controller makes closed-loop system globally asymptotically stable under a homogeneous growth condition.     

Adaptive output feedback control for nonlinear time-delay systems using neural network

This paper extends the adaptive neural network （NN） control approaches to a class of unknown output feedback nonlinear time-delay systems. An adaptive output feedback NN tracking controller is designed by backstepping technique. NNs are used to approximate unknown functions dependent on time delay, 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 NN approximation errors. Based on Lyapunov- Krasovskii functional, the semi-global uniform ultimate boundedness of all the signals in the closed-loop system is proved, The feasibility is investigated by two illustrative simulation examples.