不确定时滞关联大系统的全局稳定模糊容错控制

研究了一类带有时变时滞的不确定非线性关联大系统的自适应模糊容错控制问题.用有界的参考信号代换模糊逼近器输入中的未知时滞信号,使得控制器的设计与应用不再依赖于时滞假设条件,使得控制器的设计和控制方法的应用更为方便.容错反推控制技术和自适应技术相结合来处理代换误差和逼近误差.所提出的方案能有效补偿所有4种类型的执行器故障,同时还可保证闭环系统的全局稳定性.仿真结果进一步验证了本文方法的有效性.     

互联双倒立摆自适应容错模糊控制

针对互联双倒立摆系统,提出了一种基于时滞代换的自适应容错控制方案。该方案用有界的参考信号代换模糊逼近器输入中的未知时滞信号,这使得控制器的设计不再依赖于时滞假设条件。容错反推控制技术和自适应技术相结合来处理代换误差和逼近误差。所提出的方案能有效补偿所有四种类型的执行器故障,同时还可保证闭环系统的全局稳定性。仿真结果进一步验证了该方法的有效性。     

非线性时滞大系统自适应神经网络分散控制

针对一类未知非线性时滞关联大系统，提出一种自适应神经网络分散跟踪控制方案．采用神经网络逼近各子系统内部的非线性函数和关联项中的时滞非线性函数；利用占有方法处理时滞项，采用Backstepping技术设计分散控制律和参数自适应律．基于Lyapunov-Krasoviskii泛函证明了闭环大系统所有信号半全局一致最终有界．通过调节设计参数和增加神经元个数，可以实现任意输出跟踪精度．实例仿真说明了该方案的可行性。     

多输入多输出非线性多时滞系统的直接自适应模糊跟踪控制

针对多输入多输出非线性多时滞系统,提出了一种直接自适应模糊跟踪控制方案．该方案有机综合了自适应控制和H∞ 控制,构建了一种自适应时滞模糊逻辑系统用来逼近有多重时滞的未知函数;设计了H∞ 补偿器来抵消模糊逼近误差和外部扰动．根据跟踪误差给出了参数调节规律,构造了包含时滞的李亚普诺夫函数,从而证明了误差闭环系统满足期望的H∞ 跟踪性能．仿真结果表明了该方案的可行性．     

一类非线性时滞系统的鲁棒间接自适应控制

针对一类不确定非线性时滞系统,基于变结构控制原理,利用多层神经网络逼近的能力,提出具有投影算法的间接自适应控制方案。该方案通过监督控制器保证闭环系统所有信号有界,并引入综合误差的自适应补偿项来消除建模误差的影响。理论分析证明跟踪误差收敛到零,仿真结果表明该方法有的效性。     

一类约束非线性系统的自适应模糊容错控制

针对一类输出受限的非线性系统,提出了一种自适应模糊容错控制方案。在考虑可能发生的执行器故障的情况下,采用非线性映射的方法处理系统受约束的输出变量,从而将约束量的初值选取区间扩大为整个约束空间;用有界的参考信号代换未知函数的自变量,再用模糊逼近器处理这些未知函数,并结合自适应技术处理代换误差和逼近误差,从而使得闭环系统为全局一致最终有界,系统输出可以有效跟踪参考信号。仿真结果进一步验证了本文方法的有效性。     

一种简化的自适应模糊动态面控制

针对一类不确定非线性时变时滞系统,提出了一种简化的自适应模糊动态面控制方法.该方法取消了对系统时滞常做的假设.仅采用一个模糊逼近器便使所有的未知函数得到补偿,简化了控制器的结构.通过构造合适的Lyapunov-Krasovskii泛函,闭环系统的所有信号被证明为半全局一致最终有界.仿真实例进一步验证了控制方案的有效性.     

时变时滞非线性系统自适应神经网络控制

对于一类具有未知时变时滞和虚拟控制系数的不确定严格反馈非线性系统,基于后推设计提出一种自适应神经网络控制方案.选取适当的Lyapunov-Krasovskii泛函补偿未知时变时滞不确定项.通过构造连续的待逼近函数来解决利用神经网络对未知非线性函数进行逼近时出现的奇异问题.通过引入一个新的中间变量,保证了虚拟控制求导的正确性.仿真算例表明,所设计的控制器能保证闭环系统所有信号是半全局一致终结有界的,且跟踪误差收敛到零的一个邻域内.     

多输入多输出非线性多时滞系统的直接自适应模糊跟踪控制

针对多输入多输出非线性多时滞系统,提出了一种直接自适应模糊跟踪控制方案．该方案有机综合了自适应控制和H∞ 控制,构建了一种自适应时滞模糊逻辑系统用来逼近有多重时滞的未知函数;设计了H∞ 补偿器来抵消模糊逼近误差和外部扰动．根据跟踪误差给出了参数调节规律,构造了包含时滞的李亚普诺夫函数,从而证明了误差闭环系统满足期望的H∞ 跟踪性能．仿真结果表明了该方案的可行性．

     

周期时变时滞非线性参数化系统的自适应学习控制

针对一阶未知非线性参数化周期时变时滞系统, 设计了一种自适应学习控制方案. 假设未知时变参数, 时变时滞和参考信号的共同周期是已知的, 通过重构系统方程, 将包含时变时滞在内的所有未知时变项合并成为一个周期时变向量, 采用周期自适应律估计该向量. 通过构造一个Lyapunov-Krasovskii型复合能量函数证明了所有信号有界并且跟踪误差收敛. 结果被推广到一类含有混合参数的高阶非线性系统. 通过两个仿真例子说明本文所提出的控制算法的有效性.     

Adaptive iterative learning control for nonlinearly parameterized systems with unknown time-varying delay and unknown control direction

This paper proposes a new adaptive iterative learning control approach for a class of nonlinearly parameterized systems with unknown time-varying delay and unknown control direction.By employing the parameter separation technique and signal replacement mechanism,the approach can overcome unknown time-varying parameters and unknown time-varying delay of the nonlinear systems.By incorporating a Nussbaum-type function,the proposed approach can deal with the unknown control direction of the nonlinear systems.Based on a Lyapunov-Krasovskii-like composite energy function,the convergence of tracking error sequence is achieved in the iteration domain.Finally,two simulation examples are provided to illustrate the feasibility of the proposed control method.     

Adaptive backstepping‐based control design for uncertain nonlinear active suspension system with input delay

This paper addresses the control problem of adaptive backstepping control for a class of nonlinear active suspension systems considering the model uncertainties and actuator input delays and presents a novel adaptive backstepping‐based controller design method. Based on the established nonlinear active suspension model, a projector operator–based adaptive control law is first developed to estimate the uncertain sprung‐mass online, and then the desirable controller design and stability analysis are conducted by combining backstepping technique and Lyapunov stability theory, which can not only deal with the actuator input delay but also achieve better dynamics performances and safety constraints requirements of the closed‐loop control system. Furthermore, the relationship between the input delay and the state variables of this vehicle suspension system is derived to present a simple and effective method of calculating the critical input delay. Finally, a numerical simulation investigation is provided to illustrate the effectiveness of the proposed controller.     

未知时变时滞非线性参数化系统自适应迭代学习控制

针对含有未知时变参数和时变时滞的非线性参数化系统,提出了一种新的自适应迭代学习控制方法.该方法将参数分离技术与信号置换思想相结合,可以处理含有时变参数和时滞相关不确定性的非线性系统.设计了一种自适应控制策略,使跟踪误差的平方在一个有限区间上的积分渐近收敛于零.通过构造Lyapunov-Krasovskii型复合能量函数,给出了闭环系统收敛的一个充分条件.给出两个仿真例子验证了控制方法的有效性.     

Adaptive fuzzy control for unknown nonlinear time-delay systems with virtual control functions

In this paper, adaptive fuzzy control is presented for a class of unknown nonlinear timedelay systems with virtual control functions. By employing fuzzy logic systems and the technique of delay replacement, dynamic surface control (DSC) design approach can be carried out with both unknown delay signals and nonlinearities. This is different from the existing results, which are used to make limitations on the time-delays. It is proved that the proposed design method is able to guarantee semiglobal uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system, with arbitrary small tracking error by appropriately choosing design constants.     

基于神经网络的一类非线性系统的自适应控制

基于递归神经网络给出了仅含一个非线性环节的一类非线性系统的自适应控制方案。该方案采用递归神经网络辨识非线性系统中的未知非线性环节。沿用广义最小方差自校正控制方法,可以解决非线性环节未知和工作点变化时传统方法无法控制的自适应控制问题。理论分析和仿真结果表明,该方法具有很好的控制效果。     

Decentralised adaptive control of switched interconnected high-order nonlinear systems with unknown control direction and time-delay

This paper studies the dynamic state feedback control problem for a class of interconnected large-scale switched high-order nonlinear systems with unknown control direction and time-varying time-delay. The adaptive laws are designed to estimate the bounds of switched parameters under arbitrary switching for subsystems. The Nussbaum function is used to deal with the unknown control direction problem. By combining the backstepping and homogeneous domination technique, the decentralised adaptive control strategies are developed and the resulting closed-loop system is asymptotically stable. Finally, a simulation example is given and the results show the effectiveness of the proposed control design method.     

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.