GA-based learning for rule identification in fuzzy neural networks

Employing an effective learning process is a critical topic in designing a fuzzy neural network, especially when expert knowledge is not available. This paper presents a genetic algorithm (GA) based learning approach for a specific type of fuzzy neural network. The proposed learning approach consists of three stages. In the first stage the membership functions of both input and output variables are initialized by determining their centers and widths using a self-organizing algorithm. The second stage employs the proposed GA based learning algorithm to identify the fuzzy rules while the final stage tunes the derived structure and parameters using a back-propagation learning algorithm. The capabilities of the proposed GA-based learning approach are evaluated using a well-examined benchmark example and its effectiveness is analyzed by means of a comparative study with other approaches. The usefulness of the proposed GA-based learning approach is also illustrated in a practical case study where it is used to predict the performance of road traffic control actions. Results from the benchmarking exercise and case study effectively demonstrate the ability of the proposed three stages learning approach to identify relevant fuzzy rules from a training data set with a higher prediction accuracy than alternative approaches.     

Event-triggered controller design of nonlinear discrete-time networked control systems in T-S fuzzy model

This article is concerned with event-triggered fuzzy control design for a class of discrete-time nonlinear networked control systems (NCSs) with time-varying communication delays. Firstly, a more general mixed event-triggering scheme (ETS) is proposed. Secondly, considering the effects of the ETS and communication delays, based on the T-S fuzzy model scheme and time delay system approach, the original nonlinear NCSs is reformulated as a new event-triggered networked T-S fuzzy systems with interval time-varying delays. Sufficient conditions for uniform ultimately bound (UUB) stability are established in terms of linear matrix inequalities (LMIs). In particular, the quantitative relation between the boundness of the stability region and the triggering parameters are studied in detail. Thirdly, a relative ETS is also provided, which can be seen as a special case of the above proposed mixed ETS. As a difference from the preceding results, sufficient conditions on the existence of desired fuzzy controller are derived to ensure the asymptotic stability of the closed-loop system with reduced communication frequency between sensors and controllers. Moreover, a co-design algorithm for simultaneously determining the gain matrices of the fuzzy controller and the triggering parameters is developed. Finally, two illustrative examples are presented to demonstrate the advantage of the proposed ETS and the effectiveness of the controller design method.     

基于LabVIEW的软管脉冲实验模糊PID控制系统研究(英文)

软管脉冲实验台一般使用电液伺服系统,占用空间小,原理结构简单,信号跟踪能力强,在工业控制领域应用广泛。然而,由于液压反应速度慢、干扰因素多,因而控制精度低,系统不稳定;而传统的液压脉冲实验台单纯PID的控制方式原理简单,但参数调节困难。本文针对控制系统的特殊要求,提出了一种模糊PID控制方法,既保留了原控制系统的优点,又改善了原系统存在的参数时变、不稳定、滞后等缺点,提高了系统的适应能力和精度。经反复调试和试验,结果表明,该系统有效地解决了系统不稳定、滞后严重和参数时变等问题。     

基于多自由度气动机械手位置伺服系统稳定性控制研究

基于传统易碎薄板机械手位置伺服控制系统稳定性低、自动化分拣效率低等不足,设计了一种基于笛卡尔坐标式的气动码垛机器手和位置伺服稳定性控制系统。首先,设计并介绍笛卡尔坐标式码垛机械手的基本组成结构,对机械手末端吸盘气动回路控制系统进行设计和分析;然后分别对机械手X、Y、Z三个方向的伺服电机控制原理进行分析并设计了一种位置伺服系统的前馈自适应控制算法;最后,将传统位置闭环PID算法和前馈自适应控制算法进行位置跟踪稳定性对比试验。实验结果表明该笛卡尔坐标式的气动码垛机器手和位置伺服稳定性控制系统设计合理,满足实际生产要求。     

Integrated PLC-fuzzy PID Simulink implemented AVR system

Improving the transient response of power generation systems using automation control in a precise manner is the key issue. We design a fuzzy proportional integral derivative (PID) controller using Matlab and programmable logic controllers (PLCs) for a set point voltage control problem in the automatic voltage regulator (AVR) system. The controller objective is to maintain the terminal voltage all the time under any loads and operational conditions by attaining to the desired range via the regulation of the generator exciter voltage. The main voltage control system uses PLCs to implement the AVR action. The proposed fuzzy controller combines the genetic algorithm (GA), radial-basis function network (RBF-NN) identification and fuzzy logic control to determine the optimal PID controller parameters in AVR system. The RBF tuning for various operating conditions is further employed to develop the rule base of the Sugeno fuzzy system. The fuzzy PID controller (GNFPID) is further designed to transfer in PLCs (STEP 75.5) for implementing the AVR system with improved system response. An inherent interaction between two generator terminal voltage control and excitation current is revealed. The GNFPID controller configures the control signal based on interaction and there by reduces the voltage error and the oscillation in the terminal voltage control process. We achieve an excellent voltage control performance by testing the proposed fuzzy PID controller on a practical AVR system in synchronous generator for improve the transient response.     

PID Control of Planar Nonlinear Uncertain Systems in the Presence of Actuator Saturation

This paper investigates PID control design for a class of planar nonlinear uncertain systems in the presence of actuator saturation. Based on the bounds on the growth rates of the nonlinear uncertain function in the system model, the system is placed in a linear differential inclusion. Each vertex system of the linear differential inclusion is a linear system subject to actuator saturation. By placing the saturated PID control into a convex hull formed by the PID controller and an auxiliary linear feedback law, we establish conditions under which an ellipsoid is contractively invariant and hence is an estimate of the domain of attraction of the equilibrium point of the closed-loop system. The equilibrium point corresponds to the desired set point for the system output. Thus, the location of the equilibrium point and the size of the domain of attraction determine, respectively, the set point that the output can achieve and the range of initial conditions from which this set point can be reached. Based on these conditions, the feasible set points can be determined and the design of the PID control law that stabilizes the nonlinear uncertain system at a feasible set point with a large domain of attraction can then be formulated and solved as a constrained optimization problem with constraints in the form of linear matrix inequalities (LMIs). Application of the proposed design to a magnetic suspension system illustrates the design process and the performance of the resulting PID control law.      

真空热处理炉布料矩阵优化仿真分析

为了获得更高的加热效率和更好的温度均匀性,采用有限元软件建立了真空热处理炉加热过程仿真模型,并耦合PID算法用于温度控制。通过与实测温度对比,验证了仿真模型的准确性。借助该模型,模拟研究了布料矩阵对两种典型形状零件在真空热处理炉内加热特性的影响。模拟装炉时基于零件几何形状特征,对圆棒形工件采用顺排、叉排和环形排列3种形式,对圆盘形工件采用横排式和竖排式。研究结果表明:尽管零件形状和数量相同,但是随着布料矩阵的变化,加热效率和温度均匀性都会改变。对于圆棒形工件,采用环形排列不仅可以提高内部工件的加热速率,而且相较于叉排式可以将最大温差减小36 ℃;对于圆盘形工件,由水平式改为竖直式布料可以将最大温差由248 ℃减小至171 ℃。     

Finite-time adaptive fuzzy control for a class of output constrained nonlinear systems with dead-zone

The article investigates the finite-time adaptive fuzzy control for a class of nonlinear systems with output constraint and input dead-zone. First, by skillfully combining the barrier Lyapunov function, backstepping design method, and finite-time control theory, a novel adaptive state-feedback tracking controller is constructed, and the output constraint of the nonlinear system is not violated. Second, the fuzzy logic system is used to approximate unknown function in the nonlinear system. Third, the finite-time command filter is introduced to avoid the problem of “complexity explosion” caused by repeated differentiations of the virtual control signal in conventional backstepping control schemes. Meanwhile, a new saturation function is added in the compensating signal for filter error to improve control accuracy. Finally, based on Lyapunov stability analysis, all the signals of the closed-loop are proved to be semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood region of the origin in a finite time. A simulation example is presented to demonstrate the effectiveness for the proposed control scheme.     

基于优化粒子数和分段权值的粒子群直线电机PID整定

以工程中直线电机伺服系统为研究对象,提出一种优化粒子数量加分段式惯性权重递减的粒子群PID控制器参数优化算法。优化粒子数量的方法可降低函数调用次数,通过对近两代的全局最优值进行比较,得到的误差值如果大于设定值,认为是在初始寻优阶段,保持粒子数量,否则在最终优化阶段,减少粒子数量,所减少的粒子特征是最接近最佳粒子的粒子,以保证在欧氏距离内实现粒子的分散性。最后再结合指数衰减曲线加线性递减曲线构成的分段式惯性权重递减策略提升算法的全局寻优和局部寻优能力。经数值验证分析,该优化算法在保证遍历性的同时,在一定程度上提高了算法的运行速度和寻优精度。实验仿真结果表明,该算法对PID控制器进行参数优化,直线电机系统响应速度快,超调量小,调节时间短。