Prediction of the undrained shear strength of remolded soil with non-linear regression,fuzzy logic,and artificial neural network

This study aims to predict the undrained shear strength of remolded soil samples using non-linear regression analyses,fuzzy logic,and artificial neural network modeling.A total of 1306 undrained shear strength results from 230 different remolded soil test settings reported in 21 publications were collected,utilizing six different measurement devices.Although water content,plastic limit,and liquid limit were used as input parameters for fuzzy logic and artificial neural network modeling,liquidity index or water content ratio was considered as an input parameter for non-linear regression analyses.In non-linear regression analyses,12 different regression equations were derived for the prediction of undrained shear strength of remolded soil.Feed-Forward backpropagation and the TANSIG transfer function were used for artificial neural network modeling,while the Mamdani inference system was preferred with trapezoidal and triangular membership functions for fuzzy logic modeling.The experimental results of 914 tests were used for training of the artificial neural network models,196 for validation and 196 for testing.It was observed that the accuracy of the artificial neural network and fuzzy logic modeling was higher than that of the non-linear regression analyses.Furthermore,a simple and reliable regression equation was proposed for assessments of undrained shear strength values with higher coefficients of determination.

基于带Fuzzy壳Boole代数和修正的Gaines-Rescher蕴涵算子的险象识别逻辑

建立了一种基于带 Fuzzy壳 Boole代数和修正的 Gaines-Rescher蕴涵算子的险象识别逻辑 ,并对这种逻辑进行了语义方面的研究 ,获得若干结果。特别是 ,在这种逻辑中笔者发现 α-HS规则和α-MP规则对每个α都无条件地成立。

Adaptive Fuzzy Backstepping Output Feedback Control of Nonlinear Time-delay Systems with Unknown High-frequency Gain Sign

In this paper, an adaptive fuzzy robust feedback control approach is proposed for a class of single-input and singleoutput （SISO） strict-feedback nonlinear systems with unknown nonlinear functions, time delays, unknown high-frequency gain sign, and without the measurements of the states. In the backstepping recursive design, fuzzy logic systems are employed to approximate the unknown smooth nonlinear functions, K-filters is designed to estimate the unmeasured states, and Nussbaum gain functions are introduced to solve the problem of unknown sign of high-frequency gain. By combining adaptive fuzzy control theory and adaptive backstepping design, a stable adaptive fuzzy output feedback control scheme is developed. It has been proven that the proposed adaptive fuzzy robust control approach can guarantee that all the signals of the closed-loop system are uniformly ultimately bounded and the tracking error can converge to a small neighborhood of the origin by appropriately choosing design parameters. Simulation results have shown the effectiveness of the proposed method.

Fuzzy Adaptive Control of Stochastic Nonlinear Systems with Unknown Virtual Control Gain Function

The problem of track control is studied for a class of strict-feedback stochastic nonlinear systems in which unknown virtual control gain function is the main feature. First, the so-called stochastic LaSalle theory is extended to some extent, and accordingly, the results of global ultimate boundedness for stochastic nonlinear systems are developed. Next, a new design scheme of fuzzy adaptive control is proposed. The advantage of it is that it does not require priori knowledge of virtual control gain function sign, which is usually demanded in many designs. At the same time, the track performance of closed-loop systems is improved by adaptive modifying the estimated error upper bound. By theoretical analysis, the signals of closed-loop systems are globally ultimately bounded in probability and the track error converges to a small residual set around the origin in 4th-power expectation.

Load Frequency Control of Small Hydropower Plants Using One-Input Fuzzy PI Controller with Linear and Non-Linear Plant Model

This study presents an intelligent approach for load frequency control (LFC) of small hydropower plants (SHPs). The approach which is based on fuzzy logic (FL), takes into account the non-linearity of SHPs—something which is not possible using traditional controllers. Most intelligent methods use two- input fuzzy controllers, but because such controllers are expensive, there is economic interest in the relatively cheaper single-input controllers. A non- linear control model based on one-input fuzzy logic PI (FLPI) controller was developed and applied to control the non-linear SHP. Using MATLAB/Si- mulink SimScape, the SHP was simulated with linear and non-linear plant models. The performance of the FLPI controller was investigated and compared with that of the conventional PI/PID controller. Results show that the settling time for the FLPI controller is about 8 times shorter;while the overshoot is about 15 times smaller compared to the conventional PI/PID controller. Therefore, the FLPI controller performs better than the conventional PI/PID controller not only in meeting the LFC control objective but also in ensuring increased dynamic stability of SHPs.

Stable adaptive fuzzy control of nonlinear systems using small-gain theorem and LMI approach

A new design scheme of stable adaptive fuzzy control for a class of nonlinear systems is proposed in this paper.The T-S fuzzy model is employed to represent the systems.First,the concept of the so-called parallel distributed compensation （PDC） and linear matrix inequality （LMI） approach are employed to design the state feedback controller without considering the error caused by fuzzy modeling.Sufficient conditions with respect to decay rate α are derived in the sense of Lyapunov asymptotic stability.Finally,the error caused by fuzzy modeling is considered and the input-to-state stable （ISS） method is used to design the adaptive compensation term to reduce the effect of the modeling error.By the small-gain theorem,the resulting closed-loop system is proved to be input-to-state stable.Theoretical analysis verifies that the state converges to zero and all signals of the closed-loop systems are bounded.The effectiveness of the proposed controller design methodology is demonstrated through numerical simulation on the chaotic Henon system.

Takagi - Sugeno Fuzzy PID Gain - Scheduling Controllers

This paper aims at Takagi - Sugeno (TS) fuzzy controllers as gain scheduling (GS) schemes of PID controllers. A TS fuzzy controller employs arbitrary input fuzzy sets, product or Zadeh fuzzy logic AND, TS fuzzy rules with linear consequent, and the generalized defuzzifler containing the popular centrold defuzzifler as a special case. We first establish the relationship between the TS fuzzy controller and the linear PID controller. The TS ftizzy controller is accurately a nonlinear PID controller with gains continuously changing with Its process output. Then we point out that the TS fuzzy controller is closely related to the traditional gain scheduler. The gains of the TS ftizzy controller are determined by three two - Input - one - output fuzzy systems with singleton output fuzzy sets. Finally, as a demonstration, a simple TS fuzzy controller employing two linear input fuzzy sets, Zadeh fuzzy logic AND, and the popular centrold defuzzifler is designed to be the gain scheduler for the PID controller.

Analytical Structures of Typical Takagi-Sugeno Fuzzy PID Gain Scheduling Controllers

The analytical structure of a class of typical Takagi Sugeno (TS) fuzzy controllers is revealed in this paper.The TS fuzzy controllers consist of three or more trapezoidal input fuzzy sets, Zadeh fuzzy logic AND operator,fuzzy rules with linear consequent, and the centriod defuzzifier. The TS fuzzy controllers are proved to be accurately nonlinear PID controllers with gains continuously changing with process output. The analytical expressions of the variable gains of the TS fuzzy controllers are derived and their mathematical characteristics including the bounds and geometrical shape of the gain variation are analyzed. The resulting explicit structures show that the TS fuzzy controllers are inherently nonlinear PID gain scheduling controllers with variable gains in different regions of input space.

Fuzzy gain-scheduling output feedback controller design for saturated linear plants

In this paper, the control design problem for linear systems subject to actuator saturations is considered. A fuzzy gain-scheduling output feedback controller is proposed to guarantee the stability of the closed-loop system as well as providing disturbance/error attenuation measured in L2 norm. The synthesis condition is cast as a convex optimization problem in terms of linear matrix inequalities (LMIs) and can be solved efficiently. The ball-beam system is used to demonstrate the proposed saturation control approach.

Fault Diagnosis for Non-linear System Based On Adaptive Fuzzy System

Although lots of valuable results for fault diagnosis based on model have been achieved in linear system, it is difficult to apply these results to non-linear system due to the difficulty of modeling the non-linear system by analysis. Adaptive Fuzzy system provides a way for solving this problem because it can approximate any non-linear system at any accuracy. The key for adaptive Fuzzy system to solve problem is its learning ability, so the authors present a learning algorithm for Adaptive fuzzy system, which can build the system's model by learning from the measurement data as well as experience knowledge with high accuracy. Furthermore, the experiment using the learning algorithm to model a servo-mechanism and to construct the fault diagnosis system based on the model is carried out, the results is very good.

基于FPGA的Fuzzy-PID温度控制器的设计与应用

针对某能源分析仪器中高温炉控制的要求,设计了一个基于模糊参数自整定的PID控制器,利用模糊控制所得的PID参数结果来调节PID参数变化,从而实现了PID参数的自动调节。结果显示此种模糊参数自整定的PID控制器对于高温炉系统中存在的非线性、大滞后、物理模型不精确等问题都有很好的解决效果。

畜禽测定的多目标决策模型 I.后裔测定的Fuzzy多目标规划

本文首次将Fuzzy多目标规划的方法引入到动物育种规划的研究之中。通过隶属函数的建立,利用理想点法、线性加权和法和几何平均法,分别研究了兼顾测定总成本和遗传进展时的后裔测定的最佳决策问题,并给出了求Fuzzy解的数值计算方法。最后,讨论了不同方法在实际中的应用。这些方法可被有效地应用到其它类型的动物育种决策之中去。

Compensation for secondary uncertainty in electro-hydraulic servo system by gain adaptive sliding mode variable structure control

Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.

Improvement of Dynamic and Steady Characteristics of Pneumatic Position Servo with Fuzzy-PI Control

This paper provided a fuzzy-PI control. It makes use of the advantages of fuzzy controller for dynamic characteristics, and the advantages of PI control for steady characteristics of pneumatic position servo. Experimental results show that positioning accuracy meets the conventional industrial needs, and prove that the fuzzy-PI controller to be correct and more effective than the usual PID controller. The control method improve the dynamic and steady characteristics of the system.

Combined indirect and direct method for adaptive fuzzy output feedback control of nonlinear system

A novel control method for a general class of nonlinear systems using fuzzy logic systems （FLSs） is presertted. Indirect and direct methods are combined to design the adaptive fuzzy output feedback controller and a high-gain observer is used to estimate the derivatives of the system output. The closed-loop system is proven to be semiglobally uniformly ultimately bounded. In addition, it is shown that if the approximation accuracy of the fuzzy logic system is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussion.

Fuzzy optimization of space frame

Fuzzy concepts are introduced into structural optimization to solve fuzzyoptimization problems with a crisp objective function and fuzzy constraints, also a non-membershipfunction is used to convert fuzzy constrains into crisp constrains. Two models are discussed wherethe objective function considered is the volume of space frame and the fuzzy constrains are designlimits by the axial strength, slenderness, deflection, thickness and diameter of space frame member.

欠驱动航天器模糊自适应增强耦合姿态控制

在快速轨道机动期间,针对固体推进作用下推力偏心、安装误差等因素带来的姿态强干扰问题,提出了一种基于推力矢量控制技术的航天器姿态欠驱动智能控制方法。首先,建立了航天器姿态误差动力学模型,并分析推力矢量控制输入的欠驱动特性。然后,考虑强干扰不确定性和滚转通道耦合弱的问题,设计了基于增强耦合策略与自适应模糊观测器的欠驱动智能控制律,结合模糊逻辑函数逼近强干扰不确定项并引入控制律中,实现航天器的姿态欠驱动智能控制,通过Lyapunov理论证明了系统的稳定性。最后,通过与分层滑模控制方法进行对比仿真,验证了所设计的方法能够使得三轴姿态稳定时间缩短14%,滚转通道耦合弱产生的静差被有效消除,为快速轨道机动期间的强干扰抑制技术提供基础。

基于特征加权混合隶属度的模糊孪生支持向量机

模糊孪生支持向量机(FTSVM)忽略了不同特征间的差异,导致核函数或距离的计算无法准确反映样本间的相似性,使FTSVM在处理含有大量不相关或弱相关特征的高维数据分类时,难以达到良好分类效果;且隶属度的设计未有效区分离群点或噪声。针对以上问题,提出了一种基于特征加权混合隶属度的FM-FTSVM。首先计算每个特征的信息增益,并依据信息增益值的大小为特征赋予权重,降低不相关或弱相关特征的作用,使其能更好地应用于高维数据分类;然后,为每一类样本构造一个最小包围球计算基于紧密度的特征加权隶属度,并结合基于距离的特征加权隶属度得到特征加权混合隶属度,综合考虑样本点到类中心的特征加权欧式距离和样本间的紧密程度,可更好识别离群点或噪声数据;最后,融合特征加权核函数,降低不相关特征对核函数或距离计算产生的影响。与对比算法在人工数据集、高维数据集和UCI数据集上进行比较,发现本文提出的方法在区分离群点、噪声和有效样本上有明显优势,且在高维数据集上可获得更好分类效果。

基于模糊免疫-单神经元PID控制单开关高增益直流变换器

针对1种无变压器、无耦合电感的新型单开关高增益变换器,通过将电压升举单元引入Boost变换器来实现在较小占空比情况下提高电压增益,降低开关管、二极管电压应力,减小开关管的导通损耗,从而提高变换器的效率。为了进一步提高变换器的动态性能与抗扰动能力,在分析单神经元控制器的基础上引入免疫反馈机制,研究模糊免疫-单神经元PID控制策略,将模糊免疫控制与单神经元智能控制器结合,实现了单神经元比例系数的自整定。最后,对所提变换器和控制策略进行了仿真研究,并设计了1台输出200V/0.5A的实验样机进行实验验证,仿真和实验结果均表明:所提变换器可在较小占空比情况下获得较高电压增益;与传统的PID控制策略相比,所提模糊免疫-单神经元PID控制策略能更有效地抑制系统扰动,提高变换器的动态性能,具有更强的自适应能力和鲁棒性。