一类模糊非线性系统自适应输出反馈控制

针对一类未知非线性系统,利用模糊逻辑系统、H∞控制和高增益观测器,提出了一种模糊自适应输出反馈控制方法.证明了所设计的输出反馈控制器可以获得状态反馈控制器的性能.仿真结果证明了所提出方法是有效的.     

一类不确定非仿射非线性系统自适应模糊控制

针对一类非仿射非线性系统提出了自适应模糊控制方法,该方法把不确定非线性系统表示为定常线性子系统加非线性项的形式,然后采用模糊逻辑系统设计补偿器来消除非线性项的影响。引入时变死区函数对模糊逻辑系统中的未知参数进行自适应调节,并对时变死区设计了自适应律。证明了该方法可使闭环系统的所有信号均有界,且使跟踪误差收敛到原点的小邻域内。仿真结果表明了该方法的有效性。     

控制方向未知的非线性系统的自适应输出跟踪控制

针对一类含有未知控制方向和时变不确定性的本质非线性系统,应用Nussbaum-type增益技术和Adding a power integrator递推设计方法,设计了一种鲁棒自适应状态反馈拉制器．所设计的控制器能保证闭环系统所有信号全局一致有界,特别是通过适当调整控制器设计参数,可使输出跟踪误差在有限时间后变得适当小．最后通过仿真实例对算法进行验证.     

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.     

一类非线性系统的间接自适应输出反馈模糊控制

针对一类未知非线性系统,提出一种输出反馈控制方法。首先在假设系统状态已知的情况下设计状态反馈控制器,实现跟踪性能。然后在系统状态不完全可测的情况下,通过设计高增益观测器对系统的状态进行估计,实现输出反馈控制器设计。最后证明所设计的输出反馈控制器可获得状态反馈控制器所取得的最大最小问题的跟踪性能。     

具有高阶时延的离散非仿射非线性系统的自适应跟踪控制

本文研究了一类具有高阶输入–输出时延的非仿射非线性离散不确定系统的自适应输出跟踪控制问题,提出了一种基于隐函数的自适应输出反馈输出跟踪控制方案.该方案主要解决了两个技术问题:一是构造了基于未知参数估计和未来时刻信号估计的隐函数方程解的自适应控制律,解决了因系统高阶时延导致的控制律因果矛盾问题并实现了闭环稳定和渐近输出跟踪;二是针对非仿射非线性控制律难求解问题,提出了基于迭代解的解析自适应控制律,实现了闭环稳定和实用输出跟踪.最后仿真研究证实了所提出控制方案的有效性.     

一类模糊非线性系统的直接鲁棒自适应输出反馈控制

针对一类未知非线性系统,利用模糊逻辑系统、H^∞控制和高增益观测器,提出一种模糊直接鲁棒自适应输出反馈控制方法。证明了所设计的输出反馈控制方法不但能保证闭环控制系统稳定,而且可获得在状态反馈控制器下的性能。仿真结果进一步验证了所提出方法的有效性。     

一类非线性系统的间接自适应模糊滑模控制

基于启发式知识的模糊控制是一种解决非线性系统控制问题的有效方法。然而其设计缺乏系统性,并且系统的稳定性和鲁棒性难以保证。本文利用滑模控制的概念和Lyapunov综合方法提出一种针对一类非线性系统的间接自适应模糊滑模控制（IAFSMC）方法。仿真研究表明即使在缺少系统先验知识和不确定性干扰的情况下,系统性能也十分理想。     

严格反馈型非仿射非线性系统的自适应模糊控制

针对一类具有严格反馈形式的非仿射非线性受扰系统,提出了基于backstepping方法的自适应模糊控制.该算法仅要求模糊逻辑系统逼近误差范数有界,引入监督控制补偿系统逼近误差和外界干扰,保证闭环系统所有信号一致有界,跟踪误差一致渐近稳定.将R(o)ssle混沌系统作为仿真对象,仿真结果表明了该方法的有效性.     

一类非线性系统的间接自适应模糊控制器的研究

研究一类不确定非线性系统的间适应模糊控制问题。基于Wang提出的监督控制方案，利用Ⅰ型模糊系统的逼近能力，提出一种自适应模糊控制器设计的新方案，该方案通过引入最优逼近误差的自适应补偿项来消除建模误差的影响，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控制系统是全局稳定的，跟踪误差收敛到零，仿真结果表明了该方法的有效性。     

Linear adaptive control of a class of SISO nonaffine nonlinear systems

This paper addresses the problem of linear adaptive control for a class of uncertain continuous-time single-input single-output (SISO) nonaffine nonlinear dynamic systems. Using the implicit function theory, the existence of an ideal controller which can achieve control objectives is firstly demonstrated. However, this ideal controller cannot be known and computed even if the system model is well known. The aim of our work is to construct this unknown ideal controller using a simple linear controller with the free parameters updated online by a stable adaptation mechanism designed to minimise the error between the unknown ideal controller and the used linear controller. Since the mathematical model of the system is assumed unknown in this work, the proposed control scheme can be regarded as a simple model free controller for the studied class of nonaffine systems. We prove that the closed-loop system is stable and all the signals are bounded. An application of the proposed linear adaptive controller for a nonaffine system is illustrated through the simulation results to demonstrate the effectiveness of the proposed control scheme.     

非仿射非线性系统的自主构架模糊控制器

针对SISO非仿射非线性系统,提出一种新型自主构架模糊控制器.此控制器由鲁棒控制器与自主构架模糊系统构成.模糊系统初始只含有一条规则,根据系统误差和ε完备性2条准则自主增加规则及隶属函数,从而完善模糊系统结构,逼近非线性系统不确定量.模糊系统利用"伪模糊输出"法对新增规则后件初始化,考虑到实际计算能力,采用替换隶属函数机制限制规则数目.通过理论推导证明了系统的稳定性,理论和半实物仿真实验验证了所提出方法的有效性.     

Decentralised direct adaptive output feedback fuzzy controller for a class of large-scale nonaffine nonlinear systems and its applications

A novel cooperation-based decentralised direct adaptive fuzzy control via output feedback is developed for a class of large-scale nonaffine uncertain nonlinear systems using a direct adaptive fuzzy approach in this article. Under assumption that all the controllers share their prior information about the subsystem reference models, the interconnections between subsystems are relaxed to arbitrarily strong nonlinearities without matching conditions. The assumption on input gains is extended from typical positive constants to highly nonlinear functions. The feedback and adaptation mechanisms require neither the typical observation error filtering nor the famous strictly positive-real condition. Based on Lyapunov's direct method, the tracking errors of the closed-loop large-scale system are guaranteed to converge to tunable neighbourhoods of the origin. The proposed algorithm is applied to controlling two mechanical large-scale systems and simulation results substantiate its effectiveness.     

A direct NN‐approximation control for uncertain nonaffine systems with unknown control direction

In this paper, we present an adaptive neural network (NN) controller for uncertain nonaffine systems with unknown control direction. Most of the previous NN‐based controllers included a damping term in the adaptive law of NN weights to ensure the closed‐loop stability. The estimated error of the NN weights as well as the tracking error were therefore increased, relying not only on the size of the NN approximation error but also on the ideal NN weights. Compared with those, the proposed controller evades using the damping term through combining a novel adaptive algorithm and a switching mechanism to update the weights. The NN thus can directly approach a target controller with satisfactory accuracy even if the control direction is unknown. Stability analysis shows that the tracking error and the estimated error of NN weights both converge to small neighbors of 0 which solely depend on the NN approximation error. At last, simulations on a Duffing‐Holmes chaotic model show the effectiveness of the proposed controller in comparison to another NN‐based method.     

Decentralised direct adaptive fuzzy control for a class of large-scale nonaffine nonlinear systems and application to AHS

A novel decentralised direct adaptive fuzzy controller design is presented for a class of large-scale nonaffine uncertain nonlinear systems in this article. By integrating a fuzzy logic system and H _∞ tracking technique, the designed controller is able to adaptively compensate for interconnections and disturbances with unknown bounds, but none of the control and adaptation laws contains a sign function so that control chattering can be shunned. The closed-loop large-scale system is guaranteed to be asymptotically stable and obtain good H _∞ tracking performance. The control approach developed is applied to the following control problem of a string of vehicles within an automated highway system (AHS) and simulation results verify its validity.     

含高阶干扰的非仿射非线性系统自适应跟踪控制

本文考虑了一类带有高阶干扰和未知参数的非仿射非线性系统的自适应跟踪控制问题.为了提高系统的抗干扰性能,首先设计了扩张状态滤波器估计系统受到的高阶干扰,并把干扰估计值引入到控制器中.其次,在每一步递推设计中,为了避免backstepping方法固有的"微分爆炸"问题,引入滑模微分器估计虚拟控制律的微分,进而提出了一种新的自适应控制策略.借助Lyapunov函数理论方法分析了闭环系统的稳定性,即在所提控制策略作用下,可保证闭环系统所有信号是一致最终有界的.最后,利用MATLAB仿真验证了方法的有效性.     

基于逼近误差调节的直接自适应模糊控制

对含未知参数的一类非线性系统给出一种新的直接自适应模糊控制方案．利用在线自适应调节估计逼近误差,用此估计值设计补偿器减小逼近误差对跟踪精度的影响．该方法不仅能够保证闭环系统的稳定性,而且可以使跟踪误差收敛到0或0的小邻域．     

Robust adaptive self-structuring fuzzy control design for nonaffine,nonlinear systems

In this article, a robust adaptive self-structuring fuzzy control (RASFC) scheme for the uncertain or ill-defined nonlinear, nonaffine systems is proposed. The RASFC scheme is composed of a robust adaptive controller and a self-structuring fuzzy controller. In the self-structuring fuzzy controller design, a novel self-structuring fuzzy system (SFS) is used to approximate the unknown plant nonlinearity, and the SFS can automatically grow and prune fuzzy rules to realise a compact fuzzy rule base. The robust adaptive controller is designed to achieve an L ₂ tracking performance to stabilise the closed-loop system. This L ₂ tracking performance can provide a clear expression of tracking error in terms of the sum of lumped uncertainty and external disturbance, which has not been shown in previous works. Finally, five examples are presented to show that the proposed RASFC scheme can achieve favourable tracking performance, yet heavy computational burden is relieved.     

基于自适应模糊神经网络控制器的网络控制系统

网络控制系统中存在着时延、丢包、网络干扰等问题。针对网络控制系统中存在恶化系统的控制性能,甚至导致系统不稳定的因素,提出了一种基于自适应模糊神经网络控制器的网络控制系统,它能根据系统的实际输出与期望输出误差,利用自适应模糊控制和神经网络自学习的原理进行控制参数的自行调整,以符合控制系统的实际要求,同时,分析了网络延时,丢包率及网络干扰因素对系统性能的影响。利用TrueTime工具箱建立了包含自适应模糊神经网络控制器的网络控制系统的仿真模型,并将其分别与基于常规PID控制器的网络控制系统和基于模糊参数PID控制器的网络控制系统进行了比较。实验结果表明,在相同的网络环境下,基于自适应模糊神经网络控制器的网络控制系统的控制效果比基于常规的PID控制器和基于模糊参数PID控制器的要好,且具有较好的抗干扰能力和鲁棒性能。     

Robust decentralized adaptive fuzzy control for a class of large‐scale MIMO nonlinear systems

In this paper, a novel decentralized robust adaptive fuzzy control scheme is proposed for a class of large‐scale multiple‐input multiple‐output uncertain nonlinear systems. By virtue of fuzzy logic systems and the regularized inverse matrix, the decentralized robust indirect adaptive fuzzy controller is developed such that the controller singularity problem is addressed under a united design framework; no a priori knowledge of the bounds on lumped uncertainties are being required. The closed‐loop large‐scale system is proved to be asymptotically stable. Simulation results confirmed the validity of the approach presented. Copyright © 2011 John Wiley & Sons, Ltd.