一种模糊-PI双模控制系统的仿真与设计

为了提高系统的控制性能,综合了模糊控制和PID控制的优点,提出一种基于模糊-PI双模控制器设计方法。典型的二维模糊控制器因缺少积分环节,难以消除稳态误差,控制的精度常常不能满足系统要求;而PI控制器具有良好的消除稳态误差的作用,所以将其与模糊控制器结合构成复合控制器。通过Matlab/Simulink仿真,结果表明,与经典的PID控制方式相比较,该控制方式在快速性、稳态性及准确性方面都有较大提高。     

Vibration Suppression Using Single Neuron-Based PI Fuzzy Controller and Fractional-Order Disturbance Observer

An approach is proposed for vibration suppression in a two-inertia system using an integration of a fractional-order disturbance observer and a single neuron-based PI fuzzy controller. The former is used to obtain disturbance estimate and generate compensation signal, and the latter is utilized to realize outer loop control. Fractional-order disturbance observer has a wider range to select a suitable tradeoff between robustness and vibration suppression, because introduction of fractional calculus makes universe of relative degree of Q-filter is expanded from integer domain to real-number domain. For the single neuron-based PI fuzzy controller, a single neuron makes up a PI controller and such a controller is embedded in each cell of the fuzzy control table. Thus, the fuzzy control table is changed into a controller matrix and it constructs a nonlinear adaptive controller with parameter self-tuning property. Experimental results illustrate that the integration of fractional-order disturbance observer and single neuron-based PI fuzzy controller can improve the performance of disturbance attenuation and system robustness     

Hybrid fuzzy proportional-integral plus conventional derivativecontrol of linear and nonlinear systems

This paper presents a new approach toward the optimal design of a hybrid proportional-integral-derivative (PID) controller applicable for controlling linear as well as nonlinear systems using genetic algorithms (GAs). The proposed hybrid PID controller is derived by replacing the conventional PI controller by a two-input normalized linear fuzzy logic controller (FLC) and executing the conventional D controller in an incremental form. The salient features of the proposed controller are as follows: (1) the linearly defined FLC can generate nonlinear output so that high nonlinearities of complex systems can be handled; (2) only one well-defined linear fuzzy control space is required for both linear and nonlinear systems; (3) optimal tuning of the controller gains is carried out by using a GA; and (4) it is simple and easy to implement. Simulation results on a temperature control system (linear system) and a missile model (nonlinear system) demonstrate the effectiveness and robustness of the proposed controller     

模糊PID型电流控制器在异步电机调速系统中的应用

本文设计了一种二维模糊PID控制器作为SVPWM异步电机矢量控制系统的电流调节器，以改善这类系统电流的动稳态性能。基于对矢量控制中d、q轴电流响应性能的不同要求，论文给出了两种控制方案。仿真实验结果说明，电流环采用模糊PID控制器比采用常规的PI控制器具备更好的动稳态性能。     

Experimental design of a fuzzy controller for improving power factor of boost rectifier

This article presents the design and the implementation of dSPACE DS1104 controller board-based PI and fuzzy logic peak current-mode controllers in the voltage loop and two controllers in the current loop based first on a standard fixed hysteresis band control, followed by a variable hysteresis band control to achieve constant switching frequency for a single-phase active power factor corrector in the continuous conduction mode. All these controllers have been verified via simulation in Simulink and a real-time implementation is performed on an experimental test bench utilising a rapid prototyping tool. The controllers are experimentally compared for steady-state performance and transient response. It is shown that the PI and fuzzy logic controllers give a superior steady-state performance, whereas the fuzzy logic inference based controller can achieve better dynamic response than its PI counterpart under large load disturbance and plant uncertainties. Furthermore, the variable hysteresis band control in the current loop gives a low total harmonic distortion of the input current compared to a standard fixed hysteresis band control.     

基于模糊控制的感应电机直接转矩控制系统

根据直接转矩控制理论,在Matlab 6.5/Simulink下构造了一个感应电机直接转矩控制系统的仿真模型。为改善感应电机系统的动、静态品质,设计了模糊自适应PI速度调节器,根据速度偏差与偏差变化率,通过模糊推理在线调整PI参数,提高系统的调速性能。仿真结果表明,这种模糊控制器具有比常规PID控制器更好的控制效果。     

Using fuzzy controller for improvement of power decoupling performance in PV systems

ABSTRACT

In photovoltaic systems in order to ensure the stable operation of the module, ripple value of the input voltage should be mitigated to a small value; for this purpose, the power decoupling circuit can be used. In this work, considering the four-switch-based power decoupling circuit developed, the conventional Proportional-Integral (PI) controller has been replaced with well-known fuzzy controller, which enjoys a wide range of control gain variation, both linear and nonlinear rules' capability, robustness and more flexibility. The operating modes of the circuit are addressed and accordingly the space state model is derived. Then, the proposed controller has been replaced and the performance of the system will be analysed. The power decoupling circuit along with the proposed controller is implemented by MATLAB/Simulink software. The simulation results show the ability of the proposed fuzzy controller to improve the performance of the system in comparison to the conventional PI controller. A deeper comparison of two controllers confirms that the system would experience more accuracy, faster response and lower total harmonic distortion (THD) in presence of the proposed fuzzy controller.     

A Recurrent Fuzzy-Network-Based Inverse Modeling Method for a Temperature System Control

Temperature control by a Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (TRFN) designed by modeling plant inverse is proposed in this paper. TRFN is a recurrent fuzzy network developed from a series of TSK-type fuzzy if--then rules, and is characterized by structure and parameter learning. In parameter learning, two types of learning algorithms, the Kalman filter and the gradient descent learning algorithms, are applied to consequent parameters depending on the learning situation. The TRFN has the following advantages when applied to temperature control problems: 1) high learning ability, which considerably reduces the controller training time; 2) no a priori knowledge of the plant order is required, which eases the design process; 3) good and robust control performance; 4) online learning ability, i.e., the TRFN can adapt itself to unpredictable plant changes. The TRFN-based direct inverse control configuration is applied to a real water bath temperature control plant, where various control conditions are experimented. The same experiments are also performed by proportional-integral (PI), fuzzy, and neural network controllers. From comparisons, the aforementioned advantages of a TRFN have been verified     

New self-tuning fuzzy PI control of a novel doubly salient permanent-magnet motor drive

In a doubly salient permanent-magnet (DSPM) motor drive, it is difficult to get satisfied control characteristics by using a normal linear proportional plus integral (PI) controller due to the high nonlinearity between speed and current or torque. Hence, a new self-tuning fuzzy PI controller with conditional integral, which is performed by a single-chip N87C196KD, is proposed. The initial parameters of the controller are optimized by using genetic arithmetic. Simulation and experiments on the newly proposed 8/6-pole DSPM machine have shown that the proposed new self-tuning fuzzy PI controller offers better adaptability than the normal linear PI control and that the developed motor drive offers better steady-state and dynamic performances.     

自调整模糊控制器在永磁同步电机矢量控制中的应用

模糊控制器对系统模型依赖程度低,性能优异,已经在许多场合得到了应用。传统的永磁同步电机矢量控制系统采用PI控制,在电机参数变化或负载波动较大时性能不佳。针对这一问题,本文设计了一种基于量化因子和比例因子自调整的自适应模糊控制器。仿真结果表明,与传统PI和基本模糊控制器相比,这种参数自调整模糊控制器能使系统具有更好的抗扰性和更优良的稳态性能。     

模糊控制在异步电机直接转矩控制系统中的应用

为提高异步电机直接转矩控制系统的低速性能,本文中提出了一种模糊控制和PI闭环校正磁链观测器相结合的控制方法。该方法采用PI闭环校正磁链观测器代替传统U-I模型,给磁链和转矩误差分级,用模糊控制代替传统滞环控制选择合适的电压矢量．为减少模糊控制规则和加快模糊推理,定义了定子磁链角映射。仿真结果表明,此方法能够提高磁链的观...     

Research on fuzzy self-adaptive PI-Smith control in long time-delay system

The long time-delay often exists in industrial process.In order to overcome the big overshoot and long regulating time of the long time-delay system control,a new fuzzy self-adaptive PI-Smith control method is proposed.This method combines the Smith predictive control with fuzzy self-adaptive proportional-integral(PI)control.The traditional proportional-integral-derivative(PID)controller in Smith predictive control is replaced by fuzzy PI controller which utilizes the principle of fuzzy control to tune parameters of PI controller on-line.The results of simulation for electric furnace show that the method has the advantages of shortening regulating time,no overshoot,no steady-state error,excellent control accuracy,and good adaptive ability to the change of system model.     

电压模式Buck-Boost变换器变论域模糊PI控制

电压模式Buck-Boost变换器是一个典型的非最小相位系统。根据DC-DC Buck-Boost变换器的工作特性,运用状态空间平均法建立其小信号模型,据此设计PI控制器参数。Buck-Boost变换器是一个时变的非线性系统,传统PI控制难以达到最优的控制效果,由此采用模糊PI控制来规避PI控制的弊端。针对模糊PI控制在被控量变化较大时,控制精确度变差的问题,设计了模糊论域自适应伸缩变化的变论域模糊PI控制器,对Buck-Boost电路进行控制。通过Matlab/Simulink环境仿真,实验结果表明,变论域模糊PI控制具有更好的动态控制性能。     

Robust loop shaping-fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach

In this paper, a new technique called robust loop shaping-fuzzy gain scheduled control (RLS-FGS) is proposed to design an effective nonlinear controller for a long stroke pneumatic servo system. In our technique, a nonlinear dynamic model of a long stroke pneumatic servo plant is identified by the fuzzy identification method and is used as the plant for our design. The structure of local controllers is selected as PID control which is proven by many research works that this type of control has many advantages such as simple structure, well understanding, and high performance. The proposed technique uses particle swarm optimization (PSO) to find the optimal local controllers which maximize the average stability margin. In addition, performance weighting function which is normally difficult to obtain is automatically determined by PSO. By the proposed technique, the RLS-FGS controller can be designed, and the structure of local controllers is still not complicated. As seen in the simulation and experimental results, our proposed technique is better than the classical gain scheduled PID controller tuned by pole placement and the conventional fuzzy PID controller tuned by ISE method in terms of robust performance.     

Fuzzy PI-type controller design for boost converter

We consider the fuzzy controller design problem for a boost DC-DC converter. We design a fuzzy PI-type controller based on the common control engineering knowledge that the transient control performance can be improved if we increase the P and I gains as the error grows. Using Kharitonov’s theorem, we derive a closed-loop control system stability condition which can be used to tune the fuzzy PI control parameters. Finally, we give simulation and experimental results to show the effectiveness and practicality of the proposed method.     

Mechatronic design and injection speed control of an ultra high-speed plastic injection molding machine

This paper presents pragmatic techniques for mechatronic design and injection speed control of an ultra high-speed plastic injection molding machine. Practical rules are proposed to select specifications of key mechatronic components in the hydraulic servo system, in order to efficiently construct an industry-level machine. With reasonable assumptions, a mathematical model of the injection speed control system is established and open-loop experimental data are then employed to validate the system model. By the model, a gain-scheduling PI controller and a fuzzy PI controller are presented, compared and then implemented into a digital signal processor (DSP) using standard C programming techniques. Experimental results are conducted to show that the two proposed controllers are capable of achieving satisfactory speed tracking performance. These developed techniques may provide useful references for engineers and practitioners attempting to design pragmatic, low-cost but high-performance ultra high-injection speed controllers.     

A method for improving the robustness of PID control

In this paper, an effective method is proposed for robust proportional-integral-derivative (PID) control that is easily implementable on commonly used equipment such as programmable logic controller (PLC) and programmable automation controller (PAC). The method is based on a two-loop model following control (MFC) system containing a nominal model of the controlled plant and two PID controllers. Basic features exhibited by the MFC structure are presented, and a technique to tune both component controllers is given. The proposed structures have been implemented in a programmable logic controller and tested on control plants with perturbed parameters. Also, the proposed control system has been checked for its performance in cases when the operation of PID controllers is based on fuzzy logic. Tuning rules for the fuzzy controllers in the presented MFC system have been proposed. Results of tests lend support to the view that the proposed control structures may find wide application to robust control of plants with time-varying parameters.     

A fuzzy sliding controller for nonlinear systems

It is well known that sliding-mode control can give good transient performance and system robustness. However, the presence of chattering may introduce problems to the actuators. Many chattering elimination methods use a finite DC gain controller which leads to a finite steady-state error. One method to ensure zero steady-state error is using a proportional plus integral (PI) controller. This paper proposes a fuzzy logic controller which combines a sliding-mode controller (SMC) and a PI controller. The advantages of the SMC and the PI controller can be combined and their disadvantages can be removed. The system stability is proved, although there is one more state variable to be considered in the PI subsystem. An illustrative example shows that good transient and steady-state responses can be obtained by applying the proposed controller     

A genetic algorithm for the design of a fuzzy controller for active queue management

Active queue management (AQM) policies are those policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the hosts on the network borders, and the adoption of a suitable control policy. This paper proposes the adoption of a fuzzy proportional integral (FPI) controller as an active queue manager for Internet routers. The analytical design of the proposed FPI controller is carried out in analogy with a proportional integral (PI) controller, which recently has been proposed for AQM. A genetic algorithm is proposed for tuning of the FPI controller parameters with respect to optimal disturbance rejection. In the paper the FPI controller design methodology is described and the results of the comparison with random early detection (RED), tail drop, and PI controller are presented.     

具有神经网络前馈控制器的污水处理模糊控制系统设计

为解决污水处理过程中非线性、大时滞及干扰严重的问题,提出一种将模糊控制器和前馈控制器相接合的控制方法。其中在建立前馈控制器时,提出了使用神经网络建立前馈控制器的新方法。使用这种方法建立前馈控制器无需建立控制系统控制通道和干扰通道的数学模型,也不要求干扰是可以测量的。在MATLAB环境下对控制系统进行仿真,结果表明,将模糊控制和前馈控制结合可以有效克服干扰,提高系统的稳态精度;基于神经网络的前馈控制器可以有效补偿污水处理过程中的干扰,具有实际意义。