Actuator fault estimation based on generalized learning observer for quasi-linear parameter varying systems

This article presents a generalized learning observer (GLO) design for the simultaneous estimation of states and actuator faults for polytopic quasi-linear parameter varying systems. The proposed approach is based on the use of a GLO, which generalized the existing results on the proportional-integral observers. Conditions of existence and stability of the observer are given through the stability analysis in the sense of Lyapunov. Its design is obtained in terms of a set of linear matrix inequalities. The performance of the proposed method is evaluated by simulation in a one-link-flexible joint robot system.     

Adaptive neural prescribed performance output feedback control of pure feedback nonlinear systems using disturbance observer

In this study, an adaptive output feedback control with prescribed performance is proposed for unknown pure feedback nonlinear systems with external disturbances and unmeasured states. A novel prescribed performance function is developed and incorporated into an output error transformation to achieve tracking control with prescribed performance. To handle the unknown non-affine nonlinearities and avoid the algebraic loop problem, the radial basis function neural network (RBFNN) is adopted to approximate the unknown non-affine nonlinearities with the help of Butterworth low-pass filter. Based on the output of the RBFNN, the coupled design between sate observer and disturbance observer is presented to estimate the unmeasured states and compounded disturbances. Then, the adaptive output feedback control scheme is proposed for unknown pure feedback nonlinear systems, where a first-order filter is introduced to tackle with the issue of “explosion of complexity” in the traditional back-stepping approach. The boundedness and convergence of the closed-loop system are proved rigorously by utilizing the Lyapunov stability theorem. Finally, simulation studies are worked out to demonstrate the effectiveness of the proposed scheme.     

Adaptive prescribed performance control of nonlinear systems with unknown dead zone

An alternative adaptive control with prescribed performance is proposed to address the output tracking of nonlinear systems with a nonlinear dead zone input. An appropriate function that characterizes the convergence rate, maximum overshoot, and steady‐state error is adopted and incorporated into an output error transformation, and thus the stabilization of the transformed system is sufficient to achieve original tracking control with prescribed performance. The nonlinear dead zone is represented as a time‐varying system and Nussbaum‐type functions are utilized to deal with the unknown control gain dynamics. A novel high‐order neural network with a scalar adaptive weight is developed to approximate unknown nonlinearities, thus the computational costs can be diminished dramatically. Some restrictive assumptions on the system dynamics and the dead‐zone are circumvented. Simulations are included to validate the effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

Sliding mode control for a class of uncertain nonlinear system based on disturbance observer

In this paper, a sliding mode control (SMC) scheme is proposed for a class of nonlinear systems based on disturbance observers. For a nonlinear system, the disturbance that cannot be directly measured is estimated using a nonlinear disturbance observer. By choosing an appropriate nonlinear gain function, the disturbance observer can well approximate the unknown disturbance. Based on the output of the disturbance observer, an SMC scheme is presented for the nonlinear system, and the stability of the closed‐loop system is established using Lyapunov method. Finally, two simulation examples are presented to illustrate the features and the effectiveness of the proposed disturbance‐observer‐based SMC scheme. Copyright © 2009 John Wiley & Sons, Ltd.     

Adaptive fault tolerant control for a class of multi‐input multi‐output nonlinear systems with both sensor and actuator faults

Compared with the fault diagnosis, detection, and isolation literature, very few results are available to discuss control algorithms directly for multi‐input multi‐output nonlinear systems with both sensor and actuator faults in the fault tolerant control literature. In this work, we present a fault tolerant control algorithm to address the system output stabilization problem for a class of multi‐input multi‐output nonlinear systems with both parametric and nonparametric uncertainties, subject to sensor and actuator faults that can be both multiplicative and additive. All elements of the sensor measurements and actuator components can be faulty. Besides, the control input gain function is not fully known. Backstepping method is used in the analysis and control design. We show that under the proposed control scheme, uniformly ultimate boundedness of the system output is guaranteed, while all closed‐loop system signals stay bounded. In the cases where the sensor faults are only multiplicative, exponential convergence of the system state variables into small neighbourhoods around zero is guaranteed. An illustrative example on a robot manipulator model is presented in the end to further demonstrate the effectiveness of the proposed control scheme.     

考虑死区的水轮机调节系统非线性自抗扰控制

针对含死区环节和机械时滞的水轮机调节系统,提出了一种改进状态误差反馈控制律的自抗扰控制方法。首先,考虑电液随动系统传动过程中存在的间隙特性,建立含非线性死区环节和机械时滞的水轮机调节系统数学模型。其次,通过坐标变换将具有死区和时延特性的状态空间方程转化为可控标准化数学模型。然后,引入PID积分环节,设计新型的自抗扰控制器,消除状态误差反馈控制律处理误差信号过程中出现的抖振现象。最终,基于Lyapunov稳定性定理,分析系统误差方程,证明了扩张状态观测器的收敛性。仿真结果表明,所提控制器在不同运行工况下的控制效果均优于PID控制和传统自抗扰控制,验证了所设计控制器的有效性和优越性,具有良好的参考价值。     

Observer-based single-network incremental adaptive dynamic programming for fault-tolerant control of nonlinear systems with actuator faults

A model-free incremental adaptive fault-tolerant control (FTC) scheme is proposed for a class of nonlinear systems with actuator faults. To deal with actuator faults and guarantee the approximate optimal performance of the nominal nonlinear system without any prior knowledge of system dynamics, a single-network incremental adaptive dynamic programming (SIADP) algorithm based on incremental neural network observer is developed to design an active fault-tolerant control (AFTC) policy. An approximate linear time-varying system is obtained by incremental nonlinear technique, in which the relevant matrix parameters are identified by recursive least square estimation. Then, a SIADP algorithm-based fault-tolerant controller is developed. Based on the redundancy characteristic and function of actuators, a grouping scheme of actuators is introduced. An incremental neural network observer is designed to approximate the actuator faults. The novel SIADP scheme is constructed with a simplified single critic neural network to shorten the learning time and decrease the computational burden in the control process, in which the norm of the weight estimations of critic neural network is updated. Moreover, based on the Lyapunov theorem, the uniformly ultimately bounded stability of the closed-loop incremental system is proved. Finally, simulations are given to verify the effectiveness of the proposed FTC scheme.     

采样系统慢时变扰动比例积分观测器鲁棒控制

针对控制系统存在扰动或噪声使传统观测器存在较大观测误差问题,依据鲁棒控制理论,采用线性矩阵不等式(LMI)方法,设计了鲁棒H2/H∞全维状态比例积分观测器。研究了系统存在慢时变扰动情况下,采样系统基于比例积分观测器的状态观测效果及鲁棒稳定性,得到了一类充分条件,给出了观测器比例增益、积分增益、控制器增益以及鲁棒H2性能指标的求解方法。仿真结果表明,所设计的观测器计算量小,鲁棒性强,提高了状态观测精确度。     

基于终端滑模负载观测器的永磁同步电机位置系统反步控制

针对永磁同步电机位置控制系统存在负载扰动情况下的控制精度低,响应速度慢的问题,提出了一种基于终端滑模负载观测器的反步控制方法.设计了基于非奇异终端滑模的负载观测器,使观测误差在有限时间内收敛,并将观测值动态补偿到控制器中,提高了系统的控制精度;基于非奇异终端滑模和反步法设计位置控制器,提高了系统状态的收敛速度,增强了系统的鲁棒性,通过Lyapunov稳定性判定法证明了系统的稳定性.仿真结果表明,设计的终端滑模负载观测器能够快速、准确地估计出负载转矩,位置控制器能有效实现系统位置的渐近跟踪.     

Simultaneous time‐varying actuator and sensor fault reconstruction based on PI observer for LPV systems

We consider problems of actuator and sensor fault reconstruction simultaneously for linear parameter varying systems expressed in polytopic forms. By extending the sensor fault as an auxiliary state, a polytopic unknown input proportional‐integral observer in which the actuator fault signals are assumed to be time varying is developed to estimate the system states and the actuator and sensor fault at the same time. The existence conditions of the observer are derived in terms of linear matrix inequalities that can be readily handled via some efficient tools. An example is given to demonstrate the advantages of the proposed method in comparison to the existing results. Copyright © 2014 John Wiley & Sons, Ltd.     

Observer‐based adaptive neural control of uncertain MIMO nonlinear systems with unknown control direction

This paper investigates adaptive neural network output feedback control for a class of uncertain multi‐input multi‐output (MIMO) nonlinear systems with an unknown sign of control gain matrix. Because the system states are not required to be available for measurement, an observer is designed to estimate the system states. In order to deal with the unknown sign of control gain matrix, the Nussbaum‐type function is utilized. By using neural network, we approximated the unknown nonlinear functions and perfectly avoided the controller singularity problem. The stability of the closed‐loop system is analyzed by using Lyapunov method. Theoretical results are illustrated through a simulation example. Copyright © 2012 John Wiley & Sons, Ltd.     

Sliding mode disturbance observer control based on adaptive synchronization in a class of fractional‐order chaotic systems

In this paper, a fractional‐order Dadras‐Momeni chaotic system in a class of three‐dimensional autonomous differential equations has been considered. Later, a design technique of adaptive sliding mode disturbance‐observer for synchronization of a fractional‐order Dadras‐Momeni chaotic system with time‐varying disturbances is presented. Applying the Lyapunov stability theory, the suggested control technique fulfils that the states of the fractional‐order master and slave chaotic systems are synchronized hastily. While the upper bounds of disturbances are unknown, an adaptive regulation scheme is advised to estimate them. The recommended disturbance‐observer realizes the convergence of the disturbance approximation error to the origin. Finally, simulation results are presented in one example to demonstrate the efficiency of the offered scheme on the fractional‐order Dadras‐Momeni chaotic system in the existence of external disturbances.     

Realization of anti-slip/skid re-adhesion control for electric commuter train based on disturbance observer

Recently, disturbance observer has been used in many system and industry applications. This paper focus on the fine motion control technology based on disturbance observer for electric commuter train. The improvement of adhesion characteristics is important in electric commuter train. We propose the anti-slip/skid re-adhesion control system based on disturbance observer and sensor-less vector control. The effectiveness of the proposed method is confirmed by the experiment based on the actual electric commuter train, which is Series 205-5000 of East Japan Railway Company. Moreover, in order to extend the anti-slip/skid re-adhesion control considering the bogie vibration phenomenon, we propose a new anti-slip re-adhesion control based on the high order disturbance observer considering the resonant frequency of bogie system. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Robust fault tolerant control for a class of networked control systems with state delay and stochastic actuator failures

This paper is concerned with the robust fault tolerant controller design of networked control systems (NCSs) with state delay and stochastic actuator failures. By utilizing the input delay approach, an equivalent continuous‐time generalized time delay system in both state and input is obtained. By applying a delay decomposition approach, the information of the delayed plant states can be taken into full consideration, and new delay‐dependent sufficient conditions that ensure the asymptotic mean‐square stability of NCSs with stochastic actuator failures are derived in terms of linear matrix inequalities (LMIs). It is realized by employing a new Lyapunov–Krasovskii function in the decomposed integral intervals and directly handle the inversely weighted convex combination of quadratic terms of integral quantities with reciprocally convex combination technique. Moreover, the proposed approach involves neither slack variable nor any model transformation. A numerical example is provided to demonstrate the effectiveness and less conservatism of the proposed method.Copyright © 2012 John Wiley & Sons, Ltd.     

基于LMI的时滞电力系统双层广域阻尼控制

针对时滞电力系统提出一种新型建模方法,并且对其进行了广域附加区间阻尼控制的双层控制设计。直接采用时滞系统控制理论克服广域信号的时滞对闭环电力系统稳定性的不良影响。第一层控制,对无时滞电力系统施加计及时滞的输出反馈控制,形成时滞闭环系统。第二层控制,利用时滞系统的控制理论,采用线性矩阵不等式方法,求解状态反馈矩阵和观测器增益矩阵,进行反馈控制。10机39节点算例测试系统上的仿真结果表明,基于该方法设计的附加区间阻尼控制器,具有一定的时滞不敏感性,鲁棒性强,能够较好地抑制区间振荡。该方法为关于时滞系统的控制理论在广域附加区间阻尼控制中的直接应用搭建了平台,提供了可行性。     

基于扰动观测器的孤立交直流混合微电网双向AC/DC换流器电压波动控制策略研究

针对孤立交直流混合微电网中双向AC/DC换流器在外界扰动下出现电压波动的问题,设计了一种应用于双向AC/DC换流器的母线电压扰动观测器,以实现在分布式电源出力和负荷功率变化等外界扰动情况下对系统扰动量的快速跟踪,且无需增加额外的电压或电流传感器,保证了交直流混合微电网内分布式电源和负荷的即插即用功能。进一步地提出了基于扰动观测器的孤立交直流混合微电网双向AC/DC换流器电压波动控制策略,以有效抑制暂态电压波动和冲击,提高了孤立混合微电网在不同扰动下的动态响应性能和鲁棒稳定性。在PSCAD/EMTDC平台上搭建了孤立交直流混合微电网仿真模型,通过在不同暂态过程下的仿真测试验证了所提方法的有效性和正确性。     

Finite time adaptive fault-tolerant controller based on neural networks for switched stochastic nonlinear systems with actuator failures and disturbance

This article investigates the novel finite time adaptive neural fault-tolerant controller (FTC) for strict-feedback switched stochastic systems under arbitrary switching signals and takes into actuator failures including loss of effectiveness faults and bias faults consideration concurrently. Neural networks are utilized to approximate the unknown external disturbance and internal dynamics. On the basis of Itô differential equation and backstepping technique, an adaptive neural finite time FTC method is put forward. It is attested that the closed-loop systems are semiglobal practical finite time stable in probability and the tracking effects are great. Finally, to further demonstrate the high efficiency of proposed control method, two simulation examples are given.     

网络控制系统基于LMI的时延优化方法研究

针对网络控制系统中存在的传输延迟,介绍了一种网络控制系统时延补偿方法,将线性矩阵不等式优化方法引入到控制器的设计中,使闭环系统具有较好的性能。仿真结果表明了所提出方法的有效性。     

Adaptive neural network command filtered backstepping control of pure‐feedback systems in presence of full state constraints

An adaptive neural network (NN) command filtered backstepping control is proposed for the pure‐feedback system subjected to time‐varying output/stated constraints. By introducing a one‐to‐one nonlinear mapping, the obstacle caused by full stated constraints is conquered. The adaptive control law is constructed by command filtered backstepping technology and radial basis function NNs, where only one learning parameter needs to be updated online. The stability analysis via nonlinear small‐gain theorem shows that all the signals in closed‐loop system are semiglobal uniformly ultimately bounded. The simulation examples demonstrate the effectiveness of the proposed control scheme.     

基于模糊神经网络逆系统的五自由度无轴承永磁同步电机自抗扰控制

为了实现五自由度无轴承永磁同步电机的高性能控制,提出一种基于Takagi-Sugeno(T-S)型模糊神经网络逆系统的自抗扰控制方法.首先,基于五自由度无轴承永磁同步电机(5-DOF BPMSM)的结构及运行原理,建立五自由度无轴承永磁同步电机的数学模型,并对数学模型进行了可逆性分析.其次,利用T-S型模糊神经网络的非...