一类非线性系统的执行器偏差故障诊断

针对一类具有系统建模误差,以及含传感器模型不确定项的非线性控制系统,提出了一种用于执行器故障诊断的观测器方法。观测器中引入自适应补偿项,当出现偏差后将对故障及建模不确定项进行补偿。自适应算法中采用了死区算子,增强了算法的鲁棒性。同时利用自适应律进行偏差估计。理论分析证明观测器的观测误差收敛到零。仿真结果表明了方法的有效性。     

Backstepping-based active fault-tolerant control for a class of uncertain SISO nonlinear systems

Active fault-tolerant control is investigated for a class of uncertain SISO nonlinear flight control systems based on the adaptive observer, feedback linearization and backstepping theory. Firstly an adaptive observer is constructed to estimate the fault in the faulty system. A new fault updating law is presented to simplify the assumption conditions of the adaptive observer. The asymptotical stability of the observer and the uniform ultimate boundedness of the fault estimation error are guaranteed by Lyapunov theorem. Then a backstepping-based active fault-tolerant controller is designed for the faulty system. The asymptotical stability of the closed-loop system and uniform ultimate boundedness of the tracking error are proved based on Lyapunov theorem. The effectiveness of the proposed scheme is demonstrated through the numerical simulation of a flight control system.     

Robust fault detection and diagnosis for uncertain nonlinear systems

This paper considers robust fault detection and diagnosis for input uncertain nonlinear systems. It proposes a multi-objective fault detection criterion so that the fault residual is sensitive to the fault but insensitive to the uncertainty as much as possible. Then the paper solves the proposed criterion by maximizing the smallest singular value of the transformation from faults to fault detection residuals while minimizing the largest singular value of the transformation from input uncertainty to the fault detection residuals. This method is applied to an aircraft which has a fault in the left elevator or rudder. The simulation results show the proposed method can detect the control surface failures rapidly and efficiently.     

Output feedback based adaptive robust fault-tolerant control for a class of uncertain nonlinear systems

An adaptive robust approach for actuator fault-tolerant control of a class of uncertain nonlinear systems is proposed. The two chief ways in which the system performance can degrade following an actuator-fault are undesirable transients and unacceptably large steady-state tracking errors. Adaptive control based schemes can achieve good final tracking accuracy in spite of change in system parameters following an actuator fault, and robust control based designs can achieve guaranteed transient response. However, neither adaptive control nor robust control based fault-tolerant designs can address both the issues associated with actuator faults. In the present work, an adaptive robust fault-tolerant control scheme is claimed to solve both the problems, as it seamlessly integrates adaptive and robust control design techniques. Comparative simulation studies are performed using a nonlinear hypersonic aircraft model to show the effectiveness of the proposed scheme over a robust adaptive control based faulttolerant scheme.     

一类不确定非线性系统的自适应模糊控制

对一类不确定非线性系统提出自适应模糊控制方法。此方法用模糊逻辑系统设计自适应模糊监督控制器和自适应模糊控制器,且设计补偿器对逼近误差进行补偿,以此来减少逼近误差对跟踪精度的影响,同时对自适应模糊监督控制器和自适应模糊控制器中的未知参数设计了自适应学习律。证明了该方法不但能保证闭环系统稳定,而且可使跟踪误差收敛到原点的邻域内。仿真结果验证了此方法的有效性。     

不确定广义系统的镇定

针对输出反馈下不确定广义系统的可镇定问题,从状态反馈下闭环广义系统的稳定性问题入手,利用矩阵不等式,依次给出不确定广义系统可镇定、关联不确定广义大系统的分散可镇定、以及关联不确定广义大系统在执行器故障、传感器故障下分散可镇定的充分条件。由此推广到输出反馈,给出了输出反馈意义下的广义系统可镇定、关联不确定广义大系统的分散可镇定、以及关联不确定广义大系统在执行器故障、传感器故障下分散可镇定的充分条件(即完整性设计)。其中反馈控制器的设计方法简明而又统一,便于应用。最后用数值例子来验证所给结果的有效性。     

具有非线性输入的一类不确定混沌系统的控制

研究了一类具有非线性输入的不确定混沌系统的跟踪控制问题。首先在系统状态已知,系统的非线性项及干扰项的界已知的情况下设计了理想滑模控制器,然后在系统只有一个状态可测,非线性项及干扰项的界未知的情况下,利用扩张观测器观测出系统的未知信息再设计控制器,从而实现了混沌系统的跟踪控制。最后对Duffing系统进行数值仿真,结果表明该控制方案是有效的。     

一类不确定时滞非线性系统的H∞鲁棒镇定

利用混杂状态反馈策略研究一类不确定时滞非线性系统的鲁棒H_∞镇定问题.首先,当控制器增益矩阵均已知时,利用单Lyapunov函数法和凸组合技术,给出了闭环非线性切换系统具有鲁棒H_∞性能的充分条件并设计出相应切换律.然后,当控制器增益矩阵均未知时,利用多Lyapunov函数法,设计了控制器及相应的切换策略,使闭环非线性切换系统是鲁棒H_∞渐近稳定的.     

基于支持向量机的ESP系统传感器故障诊断方法

应用支持向量机(SVMs)回归估计方法建立ESP系统的传感器预测模型；将支持向量机模式分类方法应用于传感器的故障分离，用DAGSVM作为残差分类器获得故障结果。研究结果表明将支持向量机应用于ESP系统的传感器故障诊断是有效可行的。     

参数不确定混沌系统的反步变结构控制

针对一类存在不确定性或者输入扰动的严参数反馈形式非线性混沌系统,提出一种基于反步法的自适应变结构控制器。结合反步法和变结构控制,在第1步到第n -1步,用递推方法来选取Lyapunov函数,运用自适应反步法设计,在第n步(n阶系统) ,选择适当的滑动面来确定变结构控制器,以实现系统的全局调节或跟踪。仿真结果表明,所提出的控制器对存在输入扰动和参数不确定时具有强鲁棒性     

一类不确定执行器非线性系统的自适应控制

针对一类带不确定执行器非线性的控制系统,提出了一种自适应神经网络控制方法。建立了包括死区、齿隙和“类齿隙”磁滞特征的非线性执行器模型。通过结合所建立的模型和Nussbaum增益技术,解决了当执行器非线性不确定时的控制问题。所设计的方案不需知道非线性特征参数边界,并且当非线性特征为死区时,其坡度可以为时变的。引入了自适应补偿项消除建模误差和干扰的影响。仿真结果验证了该方法的有效性。     

一类不确定非线性切换系统的鲁棒H∞控制

研究一类不确定非线性切换系统的鲁棒H∞控制问题.利用共同Lyapunov函数和凸组合技术,给出了由线性矩阵不等式表示的使闭环系统渐近稳定且满足鲁棒H∞性能的充分条件,设计了切换策略;最后给出了一个数值仿真实例表明结论的有效性.该结果对一类不确定线性切换系统的鲁棒H∞控制也成立.     

一类非线性大系统的鲁棒变结构观测器

研究了一类具有结构和参数不确定性的非线性关联大系统状态观测器设计问题。在假定非线性系统的线性部分是可观测、不确定性和关联项满足Lipschitz条件的情况下,利用变结构控制的思想提出了一种非线性状态观测器设计方案,并证明了该观测器的指数收敛特性。所设计的观测器对结构和参数不确定性具有较强的鲁棒性。最后给出仿真研究说明了所提方法的有效性。     

一类不确定离散网络系统的全局滑模控制

针对动态传输控制协议(transmission control protocol, TCP)网络的拥塞问题,基于离散全局滑模控制理论设计了一种主动队列管理(active queue management, AQM)算法,该算法消除了滑模控制的到达阶段,保证网络系统在整个控制过程中的鲁棒性。为了减弱离散指数趋近律的抖振现象,给出了一种改进的滑模趋近律,使得复杂的网络系统具有良好的性能。仿真结果验证了所设计的控制器对活动的TCP连接数、链路带宽及往返时延的不确定性具有很强的稳定性和鲁棒性。     

Adaptive tracking controller using BP neural networks for a class of nonlinear systems

An BP neural-network-based adaptive control (NNAC) design method is described whose aim is to control a class of partially unknown nonlinear systems. Making use of the online identification of BP neural networks, the results of the identification could be used into the parameters of the controller. Not only the strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can asymptotically converge to zero by Lyapunov theory in the process of this design method.And a simulation example is also presented to evaluate the effectiveness of the design.     

Sliding mode H∞ control for a class of uncertain nonlinear state-delayed systems

A new proportional-integral (PI) sliding surface is designed for a class of uncertain nonlinear state-delayed systems. Based on this, an adaptive sliding mode controller (ASMC) is synthesized, which guarantees the occurrence of sliding mode even when the system is undergoing parameter uncertainties and external disturbance. The resulting sliding mode has the same order as the original system, so that it becomes easy to solve the H∞ control problem by designing a memoryless H∞ state feedback controller. A delay-dependent sufficient condition is proposed in terms of linear matrix inequalities (LMIs), which guarantees the sliding mode robust asymptotically stable and has a noise attenuation level γ in an H∞ sense. The admissible state feedback controller can be found by solving a sequential minimization problem subject to LMI constraints by applying the cone complementary linearization method. This design scheme combines the strong robustness of the sliding mode control with the H∞ norm performance. A numerical example is given to illustrate the effectiveness of the proposed scheme.     

Adaptive neural control for a class of uncertain stochastic nonlinear systems with dead-zone

The problem of adaptive stabilization is addressed for a class of uncertain stochastic nonlinear strict-feedback systems with both unknown dead-zone and unknown gain functions.By using the backstepping method and neural network(NN) parameterization,a novel adaptive neural control scheme which contains fewer learning parameters is developed to solve the stabilization problem of such systems.Meanwhile,stability analysis is presented to guarantee that all the error variables are semi-globally uniformly ultimately bounded with desired probability in a compact set.The effectiveness of the proposed design is illustrated by simulation results.     

Fast self-adapting high-order sliding mode control for a class of uncertain nonlinear systems

A fast self-adapting high-order sliding mode(FSH-OSM)controller is designed for a class of nonlinear systems with unknown uncertainties.As for uncertainty-free ...     

Iterative learning based fault detection and estimation in nonlinear systems

Aiming at a class of nonlinear systems that contains faults,a novel iterative learning scheme is applied to fault detection,and a novel algorithm of fault detection and estimation is proposed.This algorithm first constructs residual signals by the output of the practical system and the output of the designed fault tracking estimator,and then uses the residuals and the differencevalue signal of the adjacent two residuals to gradually revise the introduced virtual faults,which can cause the virtual faults to close to the practical faults in systems,thereby achieving the goal of fault detection for systems.This algorithm not only makes full use of the existing valid information of systems and has a faster tracking convergent speed than the proportional-type(P-type) algorithm,but also calculates more simply than the proportional-derivative-type(PD-type) algorithm and avoids the unstable effects of differential operations in the system.The final simulation results prove the validity of the proposed algorithm.     

Neural network based adaptive sliding mode control of uncertain nonlinear systems

The purpose of this paper is the design of neural network-based adaptive sliding mode controller for uncertain unknown nonlinear systems. A special architecture adaptive neural network, with hyperbolic tangent activation functions, is used to emulate the equivalent and switching control terms of the classic sliding mode control (SMC). Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. In addition to keeping the stability and robustness properties of the SMC, the neural network-based adaptive sliding mode controller exhibits perfect rejection of faults arising during the system operating. Simulation studies are used to illustrate and clarify the theoretical results.