一种自适应模糊控制器

对文 [1]提出的模糊自适应控制算法提出改进方案 ,根据改进方案 ,控制算法可以去掉监督项 ,同时可加入一辅助控制项以提高动态特性 .经证明 ,算法仍然满足全局稳定 .为保证闭环渐近稳定性条件 ,文中提出一种模糊控制器结构自组织学习方法 .仿真结果显示 ,与文 [1]算法相比 ,本文算法更能保证闭环渐近稳定性 ,具有更好的动态性能 .     

一种自适应控制模糊器

对文（１）提出的模糊自适应控制算法提出改进方案,根据改进方案,控制算法可以去掉监督项,同时可加入一辅助控制项以提高动态特性。经证明,算法仍然满足全局稳定。为保证闭环渐近稳定性条件,文中提出一种模糊控制器结构自组织学习方法,仿真结果显示,与文（１）算法相比,本文算法更能保证闭环渐近稳定性,具有更好的动态性能。     

一类非线性系统的自组织模糊CMAC神经网络自适应重构跟踪控制

提出了一种非线性系统的自组织模糊CMAC(SOFCMAC)神经网络自适应重构跟踪控制方法,首先通过构造增广系统,设计出线性渐近跟踪控制器,然后采用SOFCMAC神经网络在线重构系统的非线性特性,以消除非线性特性引起的系统误差,可保证非线性系统闭环稳定并使系统输出跟踪期望输出.仿真算例证明了SOFCMAC神经网络自适应重构跟踪控制系统的稳定性.     

基于一种修改的李亚普诺夫函数的自适应模糊滑模控制

针对一类不确定非线性系统,基于一种修改的李亚普诺夫函数并利用Ⅱ型模糊系统的 逼近能力,提出了一种稳定自适应模糊控制器设计的新方案.该方案能够避免现有的一些自适 应模糊/神经网络控制器设计中对控制增益一阶导数上界的要求.通过理论分析,证明了闭环模 糊控制系统是全局稳定的,跟踪误差收敛到零.     

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。     

自适应模糊变结构控制器的一种新设计

针对一类非线性系统,基于一种修改的李亚普诺夫函数并利用Ⅰ型模糊系统的逼近能力,提出一种稳定自适应模糊控制器设计的新方案。该方案能够避免现有的一些自适应模糊／神经网络控制器设计中对控制增益一阶际数上界的要求。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。     

分数阶Rucklidge混沌系统的同步研究

主要讨论了分数阶混沌系统的同步问题.采用线性以及自适应控制两种不同的方案实现了分数阶Rucklidge系统的混沌同步.这两种方案均具有结构简单、易于实现的特点.而且,基于分数阶微分方程稳定性理论,可以保证同步是全局渐近稳定的.最后,数值结果证明了两种方案的可行性.     

离散直接自适应模糊滑模控制一步设计

滑模控制设计方法一般需要分两步进行,即设计稳定的滑模面和能使滑模到达的滑模控制器。对于离散非线性系统的模糊滑模控制,两步设计变得较为复杂。针对离散非线性系统轨迹跟踪控制问题,提出了一种采取一步直接进行自适应模糊滑模控制的方法。首先给出问题描述及动态模糊逻辑系统(Dynamic Fuzzy Logical System,DFLS);然后针对线性滑模面,基于在线参数自调整的DFLS逼近控制器中的非线性动态函数,一步构造设计了滑模控制律。通过Lyapunov分析方法,证明了所采取的自适应律能够保证滑模可达的同时,也能保证闭环系统误差也是渐近稳定的,并具有一定的鲁棒稳定性、抖振削弱和自适应性等优点。最后,在一阶倒立摆系统中应用所提出的控制设计方法,进行了仿真研究。仿真结果验证了该方法的正确性和优越性能。     

基于Popov超稳定性理论的模糊自适应控制器设计方法

对一类常见的非线性系统,利用Popov超稳定性理论得到一种模糊自适应控制方 案,该方案在模型匹配的条件下能保证闭环系统的(渐近)稳定性.当模型匹配条件不满足时, 通过引入一个辅助控制量使系统仍保持稳定.因此,文中提出的方法普遍适用于一类非线性 离散或连续控制系统的设计.     

不确定系统的鲁棒稳定性

针对一类不确定非线性系统,基于王立新1994年提出的监督控制方案提出了一种SISO系统的模型参考自适应模糊控制器设计的新方案.该方案同时利用广义多线性模糊逻辑系统的逼近能力和I型模糊逻辑系统的逼近能力,不仅能保证闭环系统状态全局稳定,而且有效避免了控制增益可能出现的奇异性问题.理论分析证明了跟踪误差收敛到零,仿真结果表明了该方法的有效性.     

Stable model reference adaptive fuzzy control of a class ofnonlinear systems

In this paper, we propose a new adaptive fuzzy control scheme called model reference adaptive fuzzy control (MRAFC). The MRAFC scheme employs a reference model to provide closed-loop performance feedback for generating or modifying a fuzzy controller's knowledge base. The MRAFC scheme grew from ideas in conventional model reference adaptive control (MRAC). The MRAFC scheme is developed to perform adaptive feedback linearization to a class of nonlinear systems. A class of fuzzy controllers, which can be expressed in an explicit form, is used as the primary controller. Based on Lyapunov's second method, we have developed MRAFC schemes and derived fuzzy rule adaptive laws. Hence, not only the stability of the system can be assured but also the performance, such as the issues of robustness and parameter convergence, of the MRAFC system can be analyzed explicitly. We showed that in the case of no modeling error, the state error converges to zero asymptotically. In the case that persistent excitation is satisfied, we showed that the MRAFC system is asymptotically stable. By considering the periodic signal as reference input signal, we showed that the square wave can make the MRAFC system be persistently excited. The feasibility of applying these techniques has been demonstrated by considering the control of an inverted pendulum in following a reference model response     

A multirule-base controller using the robust property of a fuzzycontroller and its design method

This paper suggests a new fuzzy adaptive controller, which is able to solve the problems of classical adaptive controllers and conventional fuzzy adaptive controllers. It explains the architecture of a fuzzy adaptive controller using the robust property of a fuzzy controller. The basic idea of new adaptive control scheme is that an adaptive controller can be constructed with parallel combination of robust controllers. This new adaptive controller uses a multirule-base architecture which has several independent fuzzy controllers in parallel, each with different robust stability area. Out of several independent fuzzy controllers, the most suited one is selected by a system identifier which observes variations in the controlled system parameter. Here, we propose a design procedure which can be carried out mathematically and systematically from the model of a controlled system; related mathematical theorems and their proofs are also given. The performance of the proposed adaptive control algorithm is analyzed through a design example and a DC motor control simulation     

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．     

控制方向未知的SISO非仿射系统间接自适应模糊输出反馈控制

针对一类单输入单输出(SISO)非仿射非线性系统控制方向未知时出现的控制器奇异问题,提出了一种间接自适应模糊控制方案.利用中值定理将非仿射系统转化为仿射系统,通过模糊逻辑系统逼近该仿射系统中的未知函数,并构造模糊控制器,同时利用Lyapunov稳定性定理设计自适应律,最终克服了控制器的奇异问题;在此基础上,通过构造观测器估计跟踪误差,设计输出反馈自适应模糊控制器,解决了状态不可测时系统控制器设计难题,采用Lyapunov稳定性定理证明控制器能使得跟踪误差收敛同时闭环系统所有信号均有界.仿真结果验证了所设计控制方案的可行性与有效性.     

PHANTOM Omni机器人的自适应模糊滑模控制

针对PHANTOM Omni机器人的位置轨迹跟踪问题,采用了一种基于模糊逻辑的自适应模糊滑模控制方案。利用滑模控制中的切换函数作为输入,根据模糊系统的逼近能力设计控制器,并基于李雅谱诺夫方法设计自适应律对控制器所需参数进行实时调节。仿真中将其与传统的滑模控制进行了比较,仿真结果表明：自适应模糊滑模控制能使PHANTOM Omni机器人更好地实现期望的位置轨迹跟踪并有效地减轻抖振现象,从而证明了该方法在PHANTOM Omni机器人上实施的可行性。     

A novel adaptive control approach for nonlinear strict-feedback systems using nonlinearly parameterised fuzzy approximators

In this article, the problem of output tracking of perturbed nonlinear strict-feedback systems is addressed and a novel adaptive fuzzy control scheme is proposed. The considered systems are with unknown nonlinearities, so an adaptive fuzzy approximation approach is embedded into a backstepping procedure to get the proposed controller. However, unlike the exiting results, approximators used in this article are not linearly parameterised. Using nonlinearly parameterised adaptive fuzzy approximators, the controller can be obtained without the restriction that fuzzy basis functions of the approximators must be well defined. By managing to adapt the norm of on-line parameter vectors in the control design, the computation burden is largely reduced. The proposed controller can guarantee the stability and desired tracking performance of the closed-loop system. An example is included to demonstrate the effectiveness of the control scheme.     

基于模糊神经网络的温度控制系统研究

在硬件系统不变的情况下提出一种新型温度控制方案,结合自适应模糊控制和神经网络,用神经网络的学习能力计算出隶属度函数参数及相应的模糊规则,达到更高的控制精度。并运用Matlab中自适应神经网络模糊推理系统ANFIS对系统进行了仿真,研究表明系统具有极强的适应能力和稳定性。     

Adaptive fuzzy control with compressors and limiters for a class of uncertain nonlinear systems

This paper proposes a novel adaptive fuzzy control design for a class of nonlinear uncertain systems. The definition of compressor and limiter with adjustable parameters is introduced at beginning, and then updated laws of parameters of the compressor and estimate values of fuzzy approximation accuracies are utilized to synthesize stable adaptive controllers. The most advantage of designing adaptive fuzzy controller is neglectful of the logic structure of fuzzy logic systems, which make designer focus on parameters of the compressor, limiter and fuzzy approximation accuracies. This adaptive fuzzy control method can not only reduce the number of on-line updated parameters but also guarantee states of the closed-loop system to be uniformly ultimately bounded (UUB). Simulation results demonstrate the effectiveness of the control scheme in this paper.     

Design of Cascade Adaptive Fuzzy Sliding-Mode Control for Nonlinear Two-Axis Inverted-Pendulum Servomechanism

Because the dynamic characteristic of a two-axis inverted-pendulum servomechanism is a nonlinear underactuated system, it is difficult to design a suitable control scheme that realizes real-time stabilization and accurate tracking control simultaneously. In general, the techniques developed for fully actuated systems cannot be used directly in underactuated systems. In this study, a cascade adaptive fuzzy sliding-mode control (AFSMC) scheme including inner and outer control loops is investigated for the stabilizing and tracking control of a nonlinear two-axis inverted-pendulum servomechanism. The aim of the inner control loop is to design an AFSMC law with fuzzy estimators so that the stick-angle vector can fit the stick-angle command vector derived from the stick-angle reference model. In the outer loop, the reference signal vector is designed via a fuzzy path-planning scheme so that the cart position vector tracks the cart-position command vector and the stick-angle tracking-error vector converges to zero simultaneously. All adaptive algorithms in the cascade AFSMC system are derived in the sense of Lyapunov stability analysis, so that system stability can be guaranteed in the entire closed-loop system. The effectiveness of the proposed control strategy is verified by numerical simulations and experimental results, and the superiority of the cascade AFSMC system is indicated in comparison with a cascade sliding-mode control system.      

RCMAC-based adaptive control design for brushless DC motors

This paper proposes a recurrent cerebellar model articulation controller (RCMAC)-based adaptive control for brushless DC motors. This control system is composed of a RCMAC and a compensation controller. RCMAC is used to mimic an ideal controller, and the compensation controller is designed to compensate for the approximation error between the ideal controller and RCMAC. The Lyapunov stability theory is utilized to derive the parameter tuning algorithm, so that the uniformly ultimately bound stability of the closed-loop system can be achieved. For comparison, a fuzzy control, an adaptive fuzzy control and the developed RCMAC-based adaptive control are implemented on a field programmable gate array chip for controlling a brushless DC motor. Experimental results reveal that the proposed RCMAC-based adaptive control system can achieve the best tracking performance. Moreover, since the developed RCMAC-based adaptive control scheme uses a hyperbolic tangent function to compensate for the approximation error, there is no chattering phenomenon in the control effort. Thus, the proposed control method is more suitable for real-time practical control applications.