基于PLC程序设计的温度模糊控制系统设计

PLC作为工业控制器已广泛应用于工业生产的各个领域。模糊控制系统是一种典型的智能控制系统,它比传统逻辑更接近人类的思维和语言。本文将PLC控制器与模糊控制相结合,提出了基于PLC控制器实现水温模糊控制系统的设计方法。     

模糊控制在交流伺服电机系统中的应用

工程应用中将一种模糊PID控制器应用于交流伺服系统的控制,它可以完善PID控制器的性能。本文中详细说明,采用本文所表述的模糊PID控制器系统能克服传统PID控制器的弊端,并且具有较高的稳定精度,较强的鲁棒性。本文给出了一些基于模糊控制的电机拖动系统的行之有效的结构形式,提出了有待进一步研究的问题和模糊控制的发展前景。     

模糊控制浅析

模糊控制是-种基于模糊数学的智能控制方式,它适用于系统参数不确定,待处理的数据量过大大以及被控制对象复杂等情况的系统当中.与传统控制器依赖于系统参数的控制与设计,模糊控制器依赖于操作者即人的经验,并且其参数与控制输出的调整都是由已建立的数学逻辑模型产生的.     

基于MATLAB的模糊控制器设计与仿真

采用MATLAB7.6的模糊逻辑工具箱(Fuzzy Logic)设计一个模糊控制器来控制一复杂过程模型,并从SIMULINK仿真结果对比分析模糊控制和PID控制的效果。     

基于自调整因子模糊PID直流电机调速系统设计

针对传统控制规则的不足,本文设计了基于自调整因子的模糊控制规则,并将基于自调整因子模糊PID控制器应用于直流电机调速系统进行仿真。仿真结果表明直流电机调速系统基于自调整因子模糊PID控制器比传统模糊PID控制器具有更快的响应速度和更好的抗扰动能力。     

模糊控制系统研究及应用浅析

详细介绍了模糊控制技术的研究与发展。主要论述了模糊控制理论的分类及其组成,对模糊控制器的设计及应用进行了综述。重点阐述了近年来模糊控制与其他先进控制算法的融合与改进,其中包括与预测控制结合的模糊预测控制,与神经网络的融合,以及基于遗传算法的模糊控制等。     

基于一类不确定多变量非线性系统的自适应模糊控制

对一类不确定系统提出了模糊控制方法,基于矩阵的奇异值分解方法,采用模糊逻辑系统组成的矩阵逼近控制器中的逆矩阵以避免控制器的奇异性问题,用误差补偿项对模糊逻辑系统的逼近误差进行补偿。     

基于Matlab的模糊控制器仿真研究

提出了一种模糊控制器的设计与仿真的实现方法,该方法利用MATLAB模糊控制工具箱中模糊控制器的控制规则和隶属度函数,建立模型,并进行模糊控制器设计与仿真。仿真结果比较表明,用模糊控制能够得到较好的动态响应性能、较高的稳态控制精度,适应性强,上升时间快,鲁棒性好,保持着模糊控制器对干扰较强的适应能力。     

基于模糊神经网络的智能控制研究

模糊神经网络是一个新型的研究领域,随着人们对人工智能进行更深入、多层次的研究,使其在模糊控制、模糊决策、专家系统、模式识别等众多领域都发挥了重要作用.模糊控制的逻辑推理技术与神经网络的结合,将弥补神经网络在模糊数据处理方面的不足和纯模糊逻辑在学习方面的缺陷.     

基于模糊控制的永磁同步电机直接转矩控制技术

基于模糊控制理论,设计了一种自适应模糊控制器,在传统的直接转矩控制技术基础上加入模糊控制器和自适应机构,使整个控制系统具有较强的鲁棒性和自适应性,从而改善了永磁同步电机的交流伺服系统的动、静态品质。仿真和实验结果表明所提出的控制策略的可行性。     

A novel adaptive three stages model predictive control based on fuzzy systems: Application in MIMO controlling of MED-TVC process

In the present study, a novel technique is suggested for the adaptive non-linear model predictive control based on the fuzzy approach in three stages. In the presented approach, in the first stage, the prediction and control horizons are obtained from a fuzzy system in each control step. Another fuzzy system is employed to determine the weight factors before the optimization stage of developing new controller. The proposed controller gives the parameters of the model predictive control (MPC) in each control step in order to improve the performance of nonlinear systems. The proposed control scheme is compared with the traditional MPC and Generic Model Control for controlling MED-TVC process. The performances of the three proposed controllers have been investigated in the absence and presence of disturbance in order to evaluate the stability and robustness of the proposed controllers. The results reveal that the novel adaptive controller based on fuzzy approach performs better than the two other controllers in set-point tracking and disturbance rejection with lower IAE criteria. In addition, the average computational time for the adaptive MPC exhibits a decline of 34% in comparison with the traditional MPC.     

Nonlinear control design based on generalized Takagi–Sugeno fuzzy systems

This paper is concerned with control design for a generalized Takagi–Sugeno fuzzy system. The Takagi–Sugeno fuzzy system generally describes nonlinear systems by employing local linear system representations, while a generalized fuzzy system to be considered in this paper describes even a wider class of nonlinear systems by representing locally nonlinear systems. For such a generalized system, a stabilizing controller design method is proposed by introducing a new class of non-PDC controllers. A non-PDC controller is a generalized controller of PDC one, which is a traditional fuzzy controller. Stabilizing controller design conditions are given in terms of a set of linear matrix inequalities (LMIs), which are easily numerically solvable. A relaxation method is used to reduce the conservatism of design conditions. Finally, numerical examples are given to illustrate our nonlinear control design and to show the effectiveness over other existing results.     

Design of digital controllers for uncertain chaotic systems using fuzzy logic

A new and systematic method to design digital controllers for uncertain chaotic systems with structured uncertainties is presented in this paper. Takagi-Sugeno (TS) fuzzy model is used to model the chaotic dynamic system, while the uncertainties are decomposed such that the uncertain chaotic system can be rewritten as a set of local linear models with an additional disturbed input. Conventional control techniques are utilized to develop the continuous-time controllers first. Then, the digital controllers are obtained as the digital redesign of the continuous-time controllers using the state-matching approach. The performance of the proposed controller design is illustrated through numerical examples.     

Adaptive T–S fuzzy controller using reinforcement learning based on Lyapunov stability

In this paper, an adaptive Takagi–Sugeno (T–S) fuzzy controller based on reinforcement learning for controlling the nonlinear dynamical systems is proposed. The parameters of the T–S fuzzy system are learned using the reinforcement learning based on the actor-critic method. This on-line learning algorithm improves the controller performance over the time, which it learns from its own faults through the reinforcement signal from the external environment and tries to reinforce the T–S fuzzy system parameters to converge. The updating parameters are developed using the Lyapunov stability criterion. The proposed controller is faster in learning than the T–S fuzzy that parameters learned using the gradient descent method under the same conditions. Moreover, it is able to handle the load changes and the system uncertainties. The test is carried out based on two mathematical models. In addition, the proposed controller is applied practically for controlling a direct current (DC) shunt machine. The results indicate that the response of the proposed controller has a good performance compared with other controllers.     

A new seismic control framework of optimal PIλDµ controller series with fuzzy PD controller including soil-structure interaction

A new framework of optimal fractional order proportional-derivative-integral (FOPID) controller series with fuzzy proportional-derivative (PD) controller, namely OFPD-FOPID controller, is proposed in this study for seismic control of structures equipped with active tuned mass damper (ATMD). Three controllers including optimal PID, optimal FOPID, and fuzzy PID (FPID) controllers are also implemented for comparison purposes. Simulation results carried out on a 15-story building show the FOPID controller than the PID and FPID controllers can remarkably reduce the maximum floor displacement, but they represent a poor performance in mitigation of the maximum floor acceleration in different soil conditions, while the proposed OFPD-FOPID controller tracking the amount of the maximum floor acceleration to estimate the optimal control force of the actuator can provide superior performance. An average reduction of 41%, 45%, and 33% in the maximum floor displacement; 36%, 33%, and 20% in the maximum inter-story drift are given by FOPID in the dense, medium, and soft soils, while it results in an increase of 45%, 52% and 24% in the maximum floor acceleration. Similarly, the proposed OFPD-FOPID controller represents an average reduction of 52%, 55%, and 45% in the maximum floor displacement; 42%, 44%, and 28% in the maximum inter-story drift in the dense, medium, and soft soils, while it also slightly reduces the maximum floor acceleration of the studied structure located on different soil conditions.     

A simple approach to mathematical modelling of integer order and fractional order fuzzy PID controllers using one-dimensional input space and their experimental realization

Though over the years, mathematical modelling of fuzzy PID controllers is carried out extensively with two-dimensional and three-dimensional input spaces, the modelling is rarely attempted using one-dimensional input space. In this paper, this gap is reduced by proposing a simple approach where each of the fuzzy P, fuzzy I, and fuzzy D components is modelled using one-dimensional input space and merged to provide the complete PID action. Another speciality of the proposed approach is that it does not require any AND or OR operator for obtaining the mathematical models of individual PID components. To the best of author’s knowledge, such a modelling approach is completely new. This newly introduced idea of modelling is further extended to fractional order fuzzy PID controllers. Applicability of the proposed fuzzy controllers is delineated with four simulation examples and one real-time experimentation case study. To understand the usefulness of the proposed control schemes, performances of the newly obtained controllers are compared with the results available in literature. As the proposed controllers are model-free controllers, they can easily be implemented for other control applications also.     

PDC and Non-PDC fuzzy control with relaxed stability conditions for contintuous-time multiplicative noised fuzzy systems

This paper presents a relaxed scheme of fuzzy controller design for continuous-time nonlinear stochastic systems that are constructed by the Takagi–Sugeno (T–S) fuzzy models with multiplicative noises. Through Nonquadratic Lyapunov Functions (NQLF) and Non-Parallel Distributed Compensation (Non-PDC) control law, the less conservative Linear Matrix Inequality (LMI) stabilization conditions on solving fuzzy controllers are derived. Furthermore, in order to study the effects of stochastic behaviors on dynamic systems in real environments, the multiplicative noise term is introduced in the consequent part of fuzzy systems. For decreasing the conservatism of the conventional PDC-based fuzzy control, the NQLF stability synthesis approach is developed in this paper to obtain relaxed stability conditions for T–S fuzzy models with multiplicative noises. Finally, some simulation examples are provided to demonstrate the validity and applicability of the proposed fuzzy controller design approach.     

Improving the performance of networked control systems with time delay and data dropouts based on fuzzy model predictive control

This paper proposes a fuzzy model predictive control (FMPC) combined with the modified Smith predictor for networked control systems (NCSs). The network delays and data dropouts are problems, which greatly reduce the controller performance. For the proposed controller, the model of the controlled system is identified on-line using the Takagi – Sugeno (T-S) fuzzy models based on the Lyapunov function. There are two internal loops in the proposed structure. The first is the loop around the FMPC, which predicts the future outputs. The other is the loop around the plant to give the error between the system model and the actual plant. The proposed controller is designed for controlling a DC servo system through a wireless network to improve the system response. The practical results based on MATLAB/SIMULINK are established. The practical results are indicated that the proposed controller is able to respond the networked time delay and data dropouts compared to other controllers.     

A Performance evaluation of a fuzzy logic controller-based Photovoltaic-fed multi-level inverter for a three-phase induction motor

Today, the quality of accessible power and the response of the system involved are most significant. Many controllers have been used by researchers dealing with issues pertaining to power. Controllers play a significant role in the renewable energy sector in terms of improving the quality of power and offering a speedy system response. This research analyzes the speed of response and harmonics and enhances system performance overall. The existing system consists of a photovoltaic panel, boost converter, multi-level inverter, classic proportional-integral (PI) controller, and three-phase induction motor. The proposed system comprises a photovoltaic-based multi-level inverter, fuzzy logic controller, buck-boost converter, and three-phase induction motor. The output of the photovoltaic panel is connected to a buck-boost converter, and that of the converter to an inverter, while the output of the seven-level multi-level inverter is connected to a three-phase induction motor. Motor speed is controlled by a fuzzy logic controller (FLC). The output of the proportional-integral (PI) controller is compared to that of the fuzzy logic controller. The behavior and performance of the existing system are verified with experimental setup and proposed system are verified by MATLAB and Simulink, and the results recorded.     

Generic two-degree-of-freedom linear and fuzzy controllers for integral processes

This paper presents a new framework for the design of generic two-degree-of-freedom (2-DOF), linear and fuzzy, controllers dedicated to a class of integral processes specific to servo systems. The first part of the paper presents four 2-DOF linear PI controller structures that are designed using the Extended Symmetrical Optimum method to ensure the desired overshoot and settling time. The second part of the paper presents an original design method for 2-DOF Takagi-Sugeno PI-fuzzy controllers based on the stability analysis theorem. Experimental results for the speed control of a servo system with variable load illustrate the performance of the new generic control structures.