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 introduced. The conclusions show that the FAMLB is a better dynamic method of UPC than the traditional ones.

ADAPTIVE GENETIC ALGORITHM BASED ON SIX FUZZY LOGIC CONTROLLERS

The performance of genetic algorithm(GA) is determined by the capability of search and optimization for satisfactory solutions. The new adaptive genetic algorithm(AGA) is built for inducing suitable search and optimization relationship. The use of six fuzzy logic controllers(6FLCs) is proposed for dynamic control genetic operating parameters of a symbolic-coded GA. This paper uses AGA based on 6FLCs to deal with the travelling salesman problem (TSP). Experimental results show that AGA based on 6FLCs is more efficient than a standard GA in solving combinatorial optimization problems similar to TSP.

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.

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.

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.

Fuzzy-PID controlled lift feedback fin stabilizer

Conventional PID controllers are widely used in fin stabilizer control systems, but they have time-variations, nonlinearity, and uncertainty influencing their control effects. A lift feedback fuzzy-PID control method was developed to better deal with these problems, and this lift feedback fin stabilizer system was simulated under different sea condition. Test results showed the system has better anti-rolling performance than traditional fin-angle PID control systems.

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.

低渗灰岩油藏气水均衡驱替主控参数合理界限及开发效果评价

为制定中东低渗生屑灰岩油藏注气注水均衡驱替合理开发技术政策,设计开发因素正交试验模拟,运用组分模型数模技术,并通过二元次模型创建开发主控参数正交图版,明确气水协同注入均衡驱替的主控参数界限;在此基础上,构建基于数模预测的开发动态模糊综合评价体系,定量表征油井生产差异性,评价开发效果,指导矿场先导试验区优选。结果表明:注气注水开发主控参数对采出程度的影响由大到小排序为注气速度、注入时机、采油速度、井距、注水速度;实现油气油水前缘均衡驱替的合理采油速度为2.0%,注气速度为(34~45)×10^(4)m^(3)/d,注水速度为240~320 m^(3)/d,注采井距700 m,地层压力保持原始地层压力水平85%;指导优选开发等级区,实施注水注气矿场先导试验,取得较好试验效果。

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。

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。

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 。

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。

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.

多参数优化MPC的自动驾驶轨迹跟踪控制

针对自动驾驶车辆横向控制在大曲率路径处跟踪误差较大的问题,提出一种多参数优化模型预测控制(model predictive control,MPC)的轨迹跟踪控制策略。根据车辆动力学模型和目标函数搭建轨迹跟踪MPC控制器,将车速、横向位置误差和横摆角误差作为模糊输入,输出前轮转角作用于车辆。通过模糊控制对MPC控制器的预测时域、控制时域和权重矩阵等多个参数进行实时优化,在双移线轨迹不同速度和不同路面附着系数下完成Carsim/Simulink联合仿真,验证控制策略的有效性。仿真结果表明,MPC多参数优化算法优于MPC传统算法和MPC单参数优化算法,在高附着路面轨迹跟踪精度平均可提高27.4%;在低附着路面能够降低最大横摆角误差27.3%,并且能够更好地平衡跟踪精度和操纵稳定性。