Fuzzy self-tuning PID control of the operation temperatures in a two-staged membrane separation process

A two-staged membrane separation process for hydrogen recovery from refinery gases is introduced. The principle of the gas membrane separation process and the influence of the operation temperatures are analyzed. As the conventional PID controller is difficult to make the operation temperatures steady, a fuzzy self-tuning PID control algorithm is proposed. The application shows that the algorithm is effective, the operation temperatures of both stages can be controlled steadily, and the operation flexibility and adaptability of the hydrogen recovery unit are enhanced with safety. This study lays a foundation to optimize the control of the membrane separation process and thus ensure the membrane performance.

Stabilization of CSTR w ith Self-tuning Sliding Mode Controller Using T-S Fuzzy Linearization

A self-tuning reaching law based sliding mode control(SMC)theory is proposed to stabilize the nonlinear continuous stirred tank reactor(CSTR).T-S fuzzy logic is used to build a global fuzzy state-space linear model.Combing the traits of SMC and CSTR,three fuzzy rules can meet the requirements of controlled system.The self-tuning switch control law which can drive the state variables to the sliding surface as soon as possible is designed to ensure the robustness of uncertain fuzzy system.Lyapunov equation is applied to proving the stability of the sliding surface.The simulations show that the proposed approach can achieve desired performance with less chattering problem.

A Stable Fuzzy-Based Computational Model and Control for Inductions Motors

In this paper,a stable and adaptive sliding mode control(SMC)method for induction motors is introduced.Determining the parameters of this system has been one of the existing challenges.To solve this challenge,a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism.According to the dynamic changes of the system,in addition to the parameters of the SMC,the parameters of the type-2 fuzzy neural network are also updated online.The conditions for guaranteeing the convergence and stability of the control system are provided.In the simulation part,in order to test the proposed method,several uncertain models and load torque have been applied.Also,the results have been compared to the SMC based on the type-1 fuzzy system,the traditional SMC,and the PI controller.The average RMSE in different scenarios,for type-2 fuzzy SMC,is 0.0311,for type-1 fuzzy SMC is 0.0497,for traditional SMC is 0.0778,and finally for PI controller is 0.0997.

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.

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.

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.

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.

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。

跷跷板系统干扰补偿的分层滑模控制策略研究

为提高跷跷板系统在外部干扰下的平衡控制性能,提出一种基于指数收敛干扰观测器与切换增益模糊自适应分层滑模控制相结合的复合控制策略。首先,设计一种指数收敛干扰观测器,对系统受到的外部扰动等不确定因素进行估计补偿,以改善控制器鲁棒性。该观测器不需要状态变量二阶导的信息,可以应对实际工程中难以通过求导的方式获取加速度信号的问题。然后,针对跷跷板这类强耦合欠驱动系统提出一种分层滑模控制算法,并进一步针对滑模控制算法在未知大干扰下切换增益过大的问题,通过模糊规则的设计将切换增益模糊化,以降低系统抖振。最后,仿真和试验结果表明,相较于传统的滑模控制算法,该分层滑模控制算法抗干扰能力更强、切换增益更低。该策略可应用于类跷跷板平衡控制的实际系统。

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。

基于模糊免疫算法和PLC的永磁直驱风电系统变桨增益控制方法

为保证永磁直驱风电系统的可靠、稳定运行,提出基于模糊免疫算法和可编程逻辑控制器(PLC)的永磁直驱风电系统变桨增益控制方法。对永磁直驱风电系统运行气动特性进行了分析,确定风速对其气动特性的影响;以PLC控制器为核心,引入模糊比例-积分-微分(PID)控制算法,并在控制器中引入变增益因子形成增益控制参数;将发电机转速和功率作为该算法的输入量,实现叶尖速比和桨距角的自动调节。采用免疫算法对增益控制参数进行优化,以此提升模糊PID算法的动态性和静态性,保证了变桨增益控制效果。测试结果显示,该方法依据发电机转速和功率作为控制输入量较合理,有效控制风轮转速在1 r/min以内,功率变化范围在7.2~7.5 kW之间,风能利用能率在70%~80%之间,最大超调量为3.32%,控制效果良好。

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。

一种改进的SA-APF路径规划算法

针对基于模拟退火的人工势场算法存在局部最小值问题,引入一种随机逃逸力,提出一种改进的SA-APF(Simulated Annealing-Artificial Potential Field)算法,可使机器人快速逃离局部最优解。针对算法中仍存在目标不可达问题,根据模糊控制方法提出一种自适应斥力势场增益系数,在机器人遇到目标点附近有对称排列的障碍物时,使机器人能够到达目标点,并采用4次贝赛尔曲线拟合,使路径更光滑。仿真结果表明,在简单和复杂的障碍物的实验条件下,改进后的算法可以准确、高效地完成路径规划。

Motion Control of Omnidirectional Mobile Platform based on Fuzzy PID

The omnidirectional mobile platform has three degrees of freedom that make it widely applicable to all areas of industry, while the Mecanum wheel has the disadvantages wheel omnidirectional mobile platform is always difficult in of large vibration, the trajectory precision of Mecanum the omnidirectional mobile platform. To control the trajectory of the omnidirectional mobile platform better, this paper proposes a fuzzy self-tuning PID control model, through establishing the motion model of omnidirectional mobile platform in Adams software, then combined with Simulink simulation, analysis of fuzzy PID controller to improve the accuracy of the speed control of omnidirectional mobile platform, improve the control method of a precise trajectory of the omnidirectional mobile platform motion.

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.

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.