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.

A Parameter Varying PD Control for Fuzzy Servo Mechanism

This paper presents the formulation of novel implementation method based on parameter varying PD controller for fuzzy servo controllers. This formulation uses the approximation of fuzzy nonlinear function including error and error derivation in operation point. Obtained fuzzy control law has been employed to control angular position of servo using digital control technique applied to a typical microcontroller like AVR. The performance and robustness of modified fuzzy controller in comparison with PID controller evaluated in no load, applied external disturbance with different magnitude conditions has been studied. The simulation results showed that the proposed fuzzy controller has a considerable advantage in rise time, settling time and overshoot respect to PID controller when the servo system encounters with nonlinear features like saturation and friction.

Novel ARC-Fuzzy Coordinated Automatic Tracking Control of Four-Wheeled Mobile Robot

Four-wheeled,individual-driven,nonholonomic structured mobile robots are widely used in industries for automated work,inspection and explora-tion purposes.The trajectory tracking control of the four-wheel individual-driven mobile robot is one of the most blooming research topics due to its nonholonomic structure.The wheel velocities are separately adjusted to follow the trajectory in the old-fashioned kinematic control of skid-steered mobile robots.However,there is no consideration for robot dynamics when using a kinematic controller that solely addresses the robot chassis’s motion.As a result,the mobile robot has lim-ited performance,such as chattering during curved movement.In this research work,a three-tiered adaptive robust control with fuzzy parameter estimation,including dynamic modeling,direct torque control and wheel slip control is pro-posed.Fuzzy logic-based parameter estimation is a valuable tool for adjusting adaptive robust controller(ARC)parameters and tracking the trajectories with less tracking error as well as high tracking accuracy.This research considers the O type and 8 type trajectories for performance analysis of the proposed novel control technique.Our suggested approach outperforms the existing control methods such as Fuzzy,proportional–integral–derivative(PID)and adaptive robust controller with discrete projection(ARC–DP).The experimental results show that the scheduled performance index decreases by 2.77%and 4.76%.All the experimen-tal simulations obviously proved that the proposed ARC-Fuzzy performed well in smooth groud surfaces compared to other approaches.

Design of an Intelligent Usage Parameter Control in ATM Networks

This paper presents the principle of the fuzzy associate memory controlled leaky bucket (FAMLB) and several concepts for Usage Parameter Control (UPC) in ATM networks. The multiplex weight and random fuzzy rules adjustment method in the system architecture are introdued. The conclusions show that the FAMLB is a better dynamic method of UPC than the traditional ones.

Fuzzy Shape Control Based on El man Dynamic Recursion Network Prediction Model

In the strip rolling process, shape control system possesses the characteristics of nonlinearity, strong coupling, time delay and time variation. Based on self adapting Elman dynamic recursion network prediction model, the fuzzy control method was used to control the shape on four-high cold mill. The simulation results showed that the system can be applied to real time on line control of the shape.

Stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks with mixed delays and the Wiener process based on sampled-data control

We investigate the stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks （MJFCNNs） with discrete, unbounded distributed delays, and the Wiener process based on sampled-data control using the linear matrix inequality （LMI） approach. The Lyapunov–Krasovskii functional combined with the input delay approach as well as the free-weighting matrix approach is employed to derive several sufficient criteria in terms of LMIs to ensure that the delayed MJFCNNs with the Wiener process is stochastic asymptotical synchronous. Restrictions （e.g., time derivative is smaller than one） are removed to obtain a proposed sampled-data controller. Finally, a numerical example is provided to demonstrate the reliability of the derived results.

Parameter Identification and Application of Slippage Kinematics for Tracked Mobile Robots

A new parameter identification method is proposed to solve the slippage problem when tracked mobile robots execute turning motions.Such motion is divided into two states in this paper:pivot turning and coupled turning between angular velocity and linear velocity.In the processing of pivot turning,the slippage parameters could be obtained by measuring the end point in a square path.In the process of coupled turning,the slippage parameters could be calculated by measuring the perimeter of a circular path and the linear distance between the start and end points.The identification results showed that slippage parameters were affected by velocity.Therefore,a fuzzy rule base was established with the basis on the identification data,and a fuzzy controller was applied to motion control and dead reckoning.This method effectively compensated for errors resulting in unequal tension between the left and right tracks,structural dimensions and slippage.The results demonstrated that the accuracy of robot positioning and control could be substantially improved on a rigid floor.

SERVICE-AWARE BASED FUZZY ADMISSION CONTROL SCHEME IN MULTI-SERVICE NETWORKS

Multi-service aggregated transmission is the direction of IP network. Providing different Quality of Service (QoS) assurance for different services has become a crucial problem in future network. Admission control is a vital function for multi-service IP network. This paper proposes a novel fuzzy admission control scheme based on coarse granularity service-aware technique. Different service has discriminative sensitivity to the same QoS characteristic parameter in general. The traffic class can be perceived by the service request parameter and the proposed QoS function. And requirements of dif- ferent applications can be met by maintaining the life parameter. From simulation results, the proposed scheme shows a better QoS provisioning than those traditional fuzzy logic based methods under the same admission probability.

Fuzzy controller based on chaos optimal design and its application

In order to overcome difficulty of tuning parameters of fuzzy controller, a chaos optimal design method based on annealing strategy is proposed. First, apply the chaotic variables to search for parameters of fuzzy contro-(ller,) and transform the optimal variables into chaotic variables by carrier-wave method. Making use of the intrinsic stochastic property and ergodicity of chaos movement to escape from the local minimum and direct optimization searching within global range, an approximate global optimal solution is obtained. Then, the chaos local searching and optimization based on annealing strategy are cited, the parameters are optimized again within the limits of the approximate global optimal solution, the optimization is realized by means of combination of global and partial chaos searching, which can converge quickly to global optimal value. Finally, the third order system and discrete nonlinear system are simulated and compared with traditional method of fuzzy control. The results show that the new chaos optimal design method is superior to fuzzy control method, and that the control results are of high precision, with no overshoot and fast response.

四旋翼无人机改进自抗扰姿态控制

针对四旋翼无人机姿态控制过程中模型不确定及外部扰动问题,设计了一种自抗扰控制的姿态控制器。将模糊控制方法与传统的自抗扰控制理论相结合,增加了一个集模糊化、模糊推理和清晰化为一体的模糊控制单元,采用改进天牛须算法整定自抗扰控制器参数,提高了参数整定效率。为解决fal函数拐点处不平滑,易产生小幅抖振以及误差较大时系统增益较大等问题,引入了改进的yfal函数替代扩张状态观测器中的fal函数,并对改进后的状态观测器进行了收敛性判断。仿真结果表明,改进后的自抗扰控制器具有更快的响应速度和抗干扰能力。

基于参数估计-滑模阻抗控制的SEA性能分析

重载四足机器人的足部与地面接触过程和步态转换过程中会受到不确定的冲击载荷作用,易导致足部机构载荷过大从而造成结构的冲击损坏。因此,针对使用液压串联弹性执行器(series elastic actuators,SEA)作为足部末端在非结构环境下动态性能差的问题,提出了基于环境参数估计的滑模阻抗控制方法(environmental parameter estimation sliding mode,EPESM)。以阀控液压缸的活塞位移传递函数为基础建立了基于位置内环的SEA阻抗控制模型,并以PID作为基础控制器;为改善SEA阻抗控制的动态性能,根据Lyapunov第二法构建稳定的自适应环境参数估计方法对SEA期望位置进行前馈补偿;为提升自适应环境参数估计方法在SEA工作过程中不同阶段的动态性能和环境变化适应性,使用模糊控制方法对自适应环境参数估计方法中的自适应参数进行寻优;以SEA状态方程为基础构建滑模控制器与PID控制器进行动态性能对比分析。仿真结果表明:在变SEA弹簧刚度工况和变环境刚度下,EPESM阻抗控制的响应速度明显更快,可将调节时间从平均5 s缩短到1 s内,能更快地达到预期位移和预期接触力,且能略微降低稳态误差,使接触力误差保持在±6 N内。在动态跟踪工况下,EPESM阻抗控制的动态性能更好,在快速进入跟踪状态后,可以长时间保持0.2 s以内的相位滞后和5.2%的幅值误差。

FUZZY自整定PID参数控制器在水轮机调速器中的应用

针对常规PID控制器在水轮机调速器不能在线进行参数自整定的问题 ,结合模糊控制技术 ,提出了PID参数模糊 (FUZZY)自整定。仿真和分析表明该控制器具有良好的自适应调节能力 。

储能调频控制参数自适应优化与退出机制设计

为解决储能控制参数设置不当引起的频率稳定性问题以及储能调频效果和循环使用寿命难以平衡的问题,提出一种储能调频控制参数自适应优化与退出机制设计方法。首先分析储能下垂、虚拟正(负)惯量系数对系统频率动态性能及稳定性的影响,得到参数稳定变化的范围;然后,根据控制参数稳定变化区间,设计含虚拟负惯量的模糊自适应控制方法,实现控制方式之间的平滑切换和优势互补,加快频率恢复;再则,利用Logistic函数设计考虑储能荷电状态(SOC)的自适应反馈控制策略,同时引入储能退出机制,反馈调节优化储能出力,以期解决储能调频效果和循环使用寿命难以平衡的问题。最后,在Matlab/Simulink仿真平台上进行仿真验证,验证所提策略的有效性及对频率的改善作用。

一种新型Fuzzy-PID控制器的研究

设计了一种新型的Fuzzy-PID控制器,在模糊控制器部分加入了一个积分参数.对带有非线性环节的二阶系统进行了仿真控制实验,并与PID控制器进行了比较,实验结果表明其较好的控制性能和较强的自适应性.

Fuzzy-PID算法在气体流量控制中的应用

在气体流量控制中,由于被控参数具有时变、非线性、不确定性等因素,常规PID控制算法难以满足控制要求。本文采用模糊PID算法实现对气体流量的控制,运用模糊推理在线整定PID控制器的三个参数Kp、Ki、Kd。实际运行结果表明,该控制器取得了较好的快速性和稳定性。

基于Matlab的2种Fuzzy-PID控制器的设计与仿真

针对电温控制,提出2种Fuzzy-PID控制方式Fuzzy-PID阈值控制和Fuzzy-PID参数自整定控制,运用仿真软件Matlab实现这2种Fuzzy-PID温度控制器的设计与仿真.Fuzzy-PID阈值控制器选用给定温度偏差值作为阈值,阈值以上的大偏差范围内采用模糊控制,温差小于阈值时采用PID控制;Fuzzy-PID参数自整定控制器先由模糊控制策略得到自适应的PID参数KP,KI,KD,再采用这个改进的PID控制器来控制全局系统.仿真结果表明2种Fuzzy-PID控制器都具有良好的动态和稳态性能.

红外干燥箱变论域模糊PID复合温控设计

烘干失重法粮食水分测定过程中,干燥箱温度的精准控制是检测结果准确性的重要保障。针对干燥箱温度变化过程非线性、时滞性和温控精度高的特点,提出一种基于粒子群模型参数辨识的变论域模糊PID复合温度控制策略。基于红外辐射干燥机理及能量守恒方程建立温控系统非线性模型,并利用粒子群优化算法完成参数辨识,通过设定二级偏差阈值,将变论域模糊PID算法结合Bang-Bang控制方法构成自适应分级复合控制策略。系统仿真实验表明:相较于常规PID控制和传统模糊PID控制方法,改进后的复合控制策略在超调量、调节时间和控制进度方面明显改善,在不同工作点温度下,控制误差均小于0.4℃,且具有较好的抗干扰能力。

参数自整定Fuzzy-PID在液奶杀菌系统的应用研究

介绍了液奶杀菌的工艺流程,针对在液奶杀菌系统中传统的温度PID控制所存在的控制精度低,PID参数无法根据工况在线调整等问题,提出了将模糊控制与经典PID控制结合起来的参数自整定模糊PID控制算法,对传统PID温度控制系统进行了优化处理,并以西门子PLC为控制器通过查表法实现了此模糊控制算法。为了检验系统的性能指标,通过MATLAB软件对系统进行了仿真实验,仿真结果表明在液奶杀菌温度控制系统中,参数自整定模糊PID控制相比于经典的PID控制系统控制精度更高,响应速度更快,鲁棒性更好。

基于Fuzzy-PID控制在重介质洗选煤控制系统中的应用

重介质洗选煤工艺各控制参数如密度、液位、压力,都具有非线性和时变性,并很难建立精确的数学模型。常规PID控制在工业生产中应用广泛,但PID参数需要人工多次整定得到,耗时长、能耗高、效率低;整定过程中影响产品的质量。为了提高洗选煤的整体效益,本文设计了一种基于Fuzzy-PID控制的高精度控制器,它根据PID参数的优化规律,利用模糊控制规则对PID参数进行自动整定。自2011年在云南某洗选煤厂投运结果表明,本控制系统相比原PID控制,简化了控制过程,提高了生产效率,提高了控制精度,提高了产品质量。