一类不确定非线性纯反馈系统的自适应鲁棒模糊控制

针对一类带有不确定性的非线性MIMO纯反馈系统,提出一种自适应鲁棒模糊控制方法,该方法放宽了已有文献对系统模型的限制条件,基于李雅普诺夫分析方法获得了控制输入和自适应律．在控制输入设计中,鲁棒控制项用于补偿逼近误差向量．通过选择适当的设计参数。提出的控制方法使得闭环系统的所有信号是一致有界的和跟踪误差向量的范数收敛到小的零邻域内．仿真结果表明了所提出方法的有效性．     

Merits and limitations of using fuzzy inference system for temporal integration of INS/GPS in vehicular navigation

Most of the present vehicular navigation systems rely on global positioning system (GPS) combined with inertial navigation system (INS) for reliable determination of the vehicle position and heading. Integrating both systems provide several advantages and eliminate their individual shortcomings. Kalman filter (KF) has been widely used to fuse data from both systems. However, KF-based integration techniques suffer from several limitations related to its immunity to noise, observability and the necessity of accurate stochastic models of sensor random errors. This article investigates the potential use of adaptive neuro-fuzzy inference system (ANFIS) for temporal integration of INS/GPS in vehicular navigation. An ANFIS-based module named “P–δP” is designed, developed, implemented and tested for fusing INS and GPS position information. The fusion process aims at providing continuous correction of INS position to prevent its long-term growth using GPS position updates. In addition, it provides reliable prediction of the vehicle position during GPS outages. The P–δP module was examined using real navigation system data compromising an Ashtech Z12 GPS receiver and a Honeywell LRF-III INS. The proposed module proved to be successful as a modeless and platform independent module that does not require a priori knowledge of the navigation equipment utilized. Limitations of the ANFIS module are also discussed.     

INS/GPS组合导航中实时模糊自适应滤波技术的研究

针对INS/GPS组合导航系统,现有的滤波算法实时性不强、结构复杂,文章利用衰减记忆的方法提出了一种结构简单,适于实时计算的模糊自适应滤波算法,提高了系统的稳定性。仿真实验表明,这种算法在组合导航中具有高效率,高精度等优点,是一种实时有效的滤波算法。     

Adaptive multiple-surface sliding control for non-autonomous systems with mismatched uncertainties

In this paper, we propose a sliding mode-based controller for a class of single-input single-output nonlinear systems with mismatched uncertainties whose variation bounds are not given. The concept of multiple-surface sliding control is used to cope with the uncertainty mismatch problem, and the function approximation technique is introduced to transform the uncertainties into a finite combination of orthonormal basis functions. An adaptive controller can thus be designed using the Lyapunov approach to achieve output error convergence and boundedness of all signals. Simulation results of a benchmark problem have verified the performance and feasibility of the proposed control strategy.     

基于模糊逻辑的自适应卡尔曼滤波在GPS/INS组合导航中的应用

提出了一种利用模糊逻辑控制器来在线调节卡尔曼滤波器的自适应数据融合方法,并着重研究了其在GPS/INS组合导航中的应用．根据位置误差系数和卡尔曼滤波器的新息的统计信息，采用模糊逻辑控制器对卡尔曼滤波器进行连续修正，将卡尔曼滤波器调整到最优状态，从而提高组合导航系统的精度．仿真结果证明这种方法比标准卡尔曼滤波具有更高的精度．     

模糊逻辑自适应卡尔曼滤波技术 在GPS/ INS 组合导航系统中的仿真研

提出利用模糊逻辑自适应控制器(FLAC)调整扩展卡尔曼滤波器(KALMAN)的方法，该方法应用于CPS／INS信号融合。FLAC通过监视残差是否为零均值白噪音，然后根据模糊规则调整滤波器的指数加权，从而使滤波器不断执行最优估计。通过仿真与常规扩展卡尔曼滤波器相比较，其性能优于常规扩展卡尔曼滤波器。     

An Asymptotic Scaling Analysis of LQ Performance for an Approximate Adaptive Control Design

We consider the adaptive tracking problem for a chain of integrators, where the uncertainty is static and functional. The uncertainty is specified by L ²/L ^∞ or weighted L ²/L ^∞ norm bounds. We analyse a standard Lyapunov-based adaptive design which utilises a function approximator to induce a parametric uncertainty, on which the adaptive design is completed. Performance is measured by a modified LQ cost functional, penalising both the tracking error transient and the control effort. With such a cost functional, it is shown that a standard control design has divergent performance when the resolution of a “mono-resolution” approximator is increased. The class of “mono-resolution” approximators includes models popular in applications. A general construction of a class of approximators and their associated controllers which have a uniformly bounded performance independent of the resolution of the approximator is given. Date received: 20 April 1999. Date revised: 29 October 2001.     

直觉模糊神经网络的函数逼近能力

运用直觉模糊集理论,建立了自适应神经-直觉模糊推理系统（ANIFIS）的控制模型,并证明了该模型具有全局逼近性质.首先将Zadeh模糊推理神经网络变为直觉模糊推理网络,建立一个多输入单输出的T-S型ANIFIS模型;然后设计了系统变量的属性函数和推理规则,确定了各层的输入输出计算关系,以及系统输出结果的合成计算表达式;最后通过证明所建模型的输出结果计算式满足Stone-Weirstrass定理的3个假设条件,完成了该模型的全局逼近性证明.     

Adaptive fuzzy control systems with dynamic structure

This article proposes a novel fuzzy system, referred to as a dynamic structure fuzzy system, to address tracking control problems for unknown nonlinear dynamical systems. The fuzzy system is employed to reconstruct the unknown nonlinearities of dynamic systems. In the dynamic structure fuzzy system, the number of fuzzy rules can be either increased or decreased over time based on the required approximation accuracy. The advantage of the dynamic structure fuzzy system is that a suitable-sized fuzzy system can be found to avoid overfitting or underfitting data sets. By using Gaussian radial basis function (GRBF) as a membership function, adaptation laws are presented for tuning all parameters of the parameterized fuzzy system, including the output weights, the widths and the centers of the GRBF's. Global boundedness of the overall control scheme is guaranteed in the sense of Lyapunov. The tracking error converges to the required precision through the adaptive control scheme derived by the Lyapunov synthesis approach. Simulations performed on an underwater vehicle system demonstrate the effectiveness of our scheme.     

Adaptive fuzzy control for a class of uncertain nonaffine nonlinear systems

An adaptive fuzzy control approach is proposed for a class of multiple-input-multiple-output (MIMO) nonlinear systems with completely unknown nonaffine functions. The MIMO systems are composed of n subsystems and each of subsystems is in the nested lower triangular form. It is difficult and complicated to control this class of systems due to the existence of unknown nonaffine functions and the couplings among the nested subsystems. This difficulty is overcome by introducing some special type Lyapunov functions and taking advantage of the mean-value theorem, the backstepping design method and the approximation property of the fuzzy systems. The proposed control approach can guarantee that all the signals in the closed-loop system are bounded. A simulation experiment is utilized to verify the feasibility of the proposed approach.     

Some approximation properties of adaptive fuzzy systems with variable universe of discourse

In the last 20 years, while most research on fuzzy approximation theory has focused on nonadaptive fuzzy systems, little work has been done on adaptive fuzzy systems. This paper introduces an algorithm for adaptive fuzzy systems with Variable Universe of Discourse (VUD). By means of contraction-expansion factors, universe of discourse can be modified online, and fuzzy rules can reproduce automatically to adapt to the modified universe of discourse. Thus, dependence on the size of initial rule base is greatly reduced. Using Stone-Weierstrass theorem, VUD adaptive fuzzy systems are proved to be universal approximators with two-order approximation accuracy. In addition, the convergence properties of approximation error are discussed, and a sufficient condition is presented to partition universe of discourse and to calculate the size of rule base. An example is also given to illustrate the approximation power of VUD adaptive fuzzy systems.     

Adaptive fuzzy tracking control of nonlinear time-delay systems with unknown virtual control coefficients

In this paper, a novel adaptive fuzzy control scheme is proposed for a class of uncertain single-input and single-output (SISO) nonlinear time-delay systems with the lower triangular form. Fuzzy logic systems are used to approximate unknown nonlinear functions, then the adaptive fuzzy tracking controller is constructed by combining Lyapunov-Krasovskii functionals and the backstepping approach. The proposed controller guarantees uniform ultimate boundedness of all the signals in the closed-loop system, while the tracking error converges to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters is not more than the order of the systems under consideration. Finally, simulation studies are given to demonstrate the effectiveness of the proposed design scheme.     

一种模糊QR滤波及在组合导航中的仿真研究

总结了常用的自适应滤波的方法，并提出了一种基于模糊逻辑的自适应卡尔曼滤波技术，用模糊逻辑自适应推理器来“在线”修正卡尔曼滤波系统噪声协方差Q和测量噪声协方差R，从而使滤波器不断执行最优估计。仿真结果表明该方法可以提高GPS／INS组合导航系统的精度和可靠性。     

Adaptive IIR/FIR fusion filter and its application to the INS/GPS integrated system

Motivated by the complementary features of the IIR-type filter and the FIR-type filter, this paper proposes a robust IIR/FIR fusion filter and an INS/GPS integrated system designed with the fusion filter. In the fusion filter, an IIR-type filter (SPKF) and a FIR-type filter (MRHKF filter) are processed independently, and then the two filters are merged using the mixing probability calculated using the residuals and residual covariance information of the two filters. The merits of the SPKF and the MRHKF filter are integrated and the demerits of the filters are diminished through the filter fusion. Consequently, the proposed fusion filter shows robustness against model uncertainty, temporary disturbing noise, large initial estimation error, etc. The stability of the fusion filter is verified by showing the closeness of two filters in the mixing/redistribution process and the upper bound of the error covariance matrices. This fusion filter is applied to an INS/GPS integrated system. The performance of the INS/GPS integrated system designed using the fusion filter is verified through a simulation under various error environments and is experimentally confirmed.     

Adaptive fuzzy decentralized control for interconnected MIMO nonlinear subsystems

This paper describes an adaptive fuzzy control strategy for decentralized control for a class of interconnected nonlinear systems with MIMO subsystems. An adaptive robust tracking control schemes based on fuzzy basis function approach is developed such that all the states and signals are bounded. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds. The resultant adaptive fuzzy decentralized control with multi-controller architecture guarantees stability and convergence of the output errors to zero asymptotically by local output-feedback. An extensive application example of a three-machine power system is discussed in detail to verify the effectiveness of the proposed algorithm.     

严格反馈非线性系统的自适应模糊控制

针对一类不确定非线性系统,基于backstepping方法提出了一种新的鲁棒自适应模糊控制器设计方案。该方案通过引入最优逼近误差的自适应补偿项和新的鲁棒项,削减建模误差和参数估计误差的影响,从而在稳定性分析中取消了要求逼近误差平方可积或逼近误差的上确界已知的条件。理论分析证明了闭环系统状态有界,跟踪误差收敛到零的较小邻域内。仿真结果表明了该方法的有效性。     

一类死区非线性系统的自适应模糊控制设计

为了实现对具有时变摄动死区非线性系统的跟踪控制,本文提出了一种基于自适应模糊逼近器的Backstepping控制方法。该方法通过将死区特性合理分解,并将自适应模糊逼近器嵌入到Backstepping设计步骤中,逐步递推得到控制律。所提出的控制方法适用于高阶非线性系统,并且不要求被控系统满足匹配条件;所采用的模糊逼近器是非线性参数化的,亦即不要求其模糊基函数是完全确定已知的,从而降低了对先验知识的依赖性。为了得到未知参数的自适应律,本文先应用Taylor级数展开式将具有非线性关系的未知参数相互分离,使其呈现线性关系,然后根据Lyapunov稳定性定理给出在线可调参数的自适应律。此外,所设计的自适应律是对与未知参数向量的范数相关的变量进行在线调节,这样可以有效减少需要在线调节的参数数量,从而降低了控制器的在线计算负担,提高了系统的响应速度和控制精度。本文给出的控制设计能够有效地克服死区特性对系统性能的影响,使得闭环系统所有信号均指数收敛到原点的指定邻域内,系统输出可以按给定的精度跟踪参考信号。最后,本文用一个仿真实例验证了所给控制方法的有效性。     

Adaptive robust fuzzy control for path tracking of a wheeled mobile robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Adaptive fuzzy controller design with observer for a class of uncertain nonlinear MIMO systems

In this paper, an adaptive fuzzy control approach is proposed to stabilize a class of uncertain nonlinear MIMO systems with the unmeasured states and the external disturbances. The fuzzy logic systems are used to approximate the unknown functions. Because it does not required to assume that the system states are measurable, it needs to design an observer to estimate the system unmeasured states. The considered MIMO systems are more general, i.e. they consist of N subsystems and each subsystem is in the non‐affine form. The stability of the closed‐loop system is verified by using Lyapunov analysis method. Two simulation examples are utilized to verify the effectiveness of the proposed approach. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Adaptive fuzzy backstepping control of three-phase active power filter

A backstepping controller (BC) and an adaptive fuzzy backstepping controller (AFBC) are proposed for three-phase active power filter (APF) in this paper. Firstly, the dynamic model for APF is build in which both the system parameter variations and external disturbance are considered. Then, the backstepping method is applied in the design of current control system to deal with the nonlinearity of APF. Moreover, the AFBC is developed by combining the backstepping approach with adaptive fuzzy strategy to attenuate the effect of parameter uncertainties and external disturbances. Fuzzy logic system is designed to estimate the unknown nonlinear function in the AFBC where the parameters are adjusted online by the adaptive law derived from the Lyapunov stability analysis to guarantee the tracking performance and stability of the closed-loop system. Simulation studies using the MATLAB/SimPower Systems Toolbox demonstrate that the proposed control strategies exhibit excellent performance in both steady state and transient operation.