发电机组汽门系统的多模型自学习控制

针对发电机组的非线性、大范围运行等实际问题，研究了用于汽门系统的多模型自学习控制（MMSC），首先根据各种工况下的样本数据归纳出模糊控制规则；然后由模糊聚类算法将多种工况约简为典型工况，得到相应的子模型模糊控制器（FLC）．以子模型FLC输出的加权集成作为MMSC的控制输出，而加权系数取决干子模型匹配度．在子模型FLC学习优化中，由支持向量机离线逼近模糊规则曲面，再由梯度下降算法在线自学习．仿真实验验证了所设计控制器的优良性能．     

Approximated fuzzy logic controlled shunt active power filter for improved power quality

Shunt active power filters have been widely used for power quality improvement. With the advancement in artificial intelligence techniques, the applications of fuzzy logic‐based control systems have increased manifolds. This paper proposes a reduced rule fuzzy logic controller (FLC) in the voltage control loop of a shunt active power filter (APF), which is approximating a conventional large rule FLC. The difference between the controlled outputs of two controllers is compensated by proposed compensating factors. The dynamic response and harmonic compensation performance of proposed 4‐rule approximated fuzzy logic controller (AFLC) is compared with 25‐rule FLC. A three‐phase shunt APF is used for harmonic and reactive power compensation. The proposed scheme is tested with randomly varying single and multiple non‐linear loads. The simulation results presented under transient and steady‐state conditions confirm that the proposed 4‐rule AFLC efficiently approximates the 25‐rule FLC. The proposed control methodology takes less computational time and computational memory as the numbers of rules are reduced significantly.     

MODEL-BASED FUZZY LOGIC CONTROL OF A NONLINEAR PLANT

A full-envelope controller for nonlinear systems is developed based on a novel model-based fuzzy logic control (MBFLC) approach. This approach envisages a bank of linear compensators, with each compensator optimized for a different operating point throughout the operational envelope. The fuzzy logic module properly blends the compensator outputs into a composite control signal, and avoids instability when none of the individual compensators are valid. In general practice, in the strongly nonlinear system under consideration, the fuzzy element of the MBFLC dominates due to the extremely small regions of validity of perturbation models of the strongly nonlinear plant. The MBFLC, with the composite control input smoothed by a linear compensator, provides full-envelope control of the benchmark plant. © 1997 by John Wiley & Sons, Ltd. This paper was produced under the auspices of the US Government and it is therefore not subject to copyright in the US.     

Improving asynchronous motor speed and flux loop control by using hybrid fuzzy-SMC controllers

This paper presents a new method combining sliding mode control (SMC) and fuzzy logic control (FLC) to enhance the robustness and performance for a class of non-linear control systems. This fuzzy sliding mode control (FSMC) is developed for application in the area for controlling the speed and flux loops of asynchronous motors. The proposed control law can solve those problems associated with the conventional control by sliding mode control, such as high current, flux and torque chattering, variable switching frequency and variation of parameters, in which a robust fuzzy logic controller replaces the discontinuous part of the classical sliding mode control law. Simulation results of the proposed FSMC technique on the speed and flux rotor controllers present good dynamic and steady-state performances compared to the classical SMC in terms of reduction of the torque chattering, quick dynamic torque response and robustness to disturbance and variation of parameters.     

From Nonlinear to Fuzzy Approaches in Trajectory Tracking Control of Wheeled Mobile Robots

In this paper, two knowledge based controllers are proposed to overcome the difficulties of a computed torque nonlinear controller (NC) in perfect trajectory tracking of nonholonomic wheeled mobile robots (WMRs). First, the effects of different dynamic models developed in angular and Cartesian coordinate systems are fully examined on the persistent excitation condition and consequently on the trajectory tracking performance of WMRs. Using the dynamic model coordinated in the Cartesian frame as the base of the NC results in perfect compensation of large position off‐tracks and unbiased estimation of the plant's unknown parameters. However, using the WMR's dynamic model with rotation angles of driving wheels as the base of nonlinear and fuzzy controllers leads to accurate orientation tracking. Through replacing the proportional and differential terms of the NC by fuzzy functions, a fuzzy nonlinear controller (FNC) is generated. Due to the complicated dynamics of the WMR in which the center of mass does not coincide with the center of rotation, the expert knowledge of fuzzy controllers is extracted considering the rotation angles and rates of driving wheels as input variables. Fuzzy tuning of the NC results in a superior tracking performance against measurement noises, though the control torques are decreased and smoothed significantly. Second, a complete fuzzy controller (FC) is generated to make perfect tracking of the WMR's position and orientation. The local stability analysis of fuzzy controllers is examined considering the corresponding analytical structures as nonlinear controllers. The superior performances of the proposed fuzzy controllers compared to those of the NCs are evaluated through simulations.     

Fuzzy PI control design for an industrial weigh belt feeder

An industrial weigh belt feeder is used to transport solid materials into a manufacturing process at a constant feedrate. It exhibits nonlinear behavior because of motor friction, saturation, and quantization noise in the sensors, which makes standard autotuning methods difficult to implement. The paper proposes and experimentally demonstrates two types of fuzzy logic controllers for an industrial weigh belt feeder. The first type is a PI-like fuzzy logic controller (FLC). A gain scheduled PI-like FLC and a self-tuning PI-like FLC are presented. For the gain scheduled PI-like FLC the output scaling factor of the controller is gain scheduled with the change of setpoint. For the self-tuning PI-like FLC, the output scaling factor of the controller is modified online by an updating factor whose value is determined by a rule base with the error and change of error of the controlled variable as the inputs. A fuzzy PI controller is also presented, where the proportional and integral gains are tuned online based on fuzzy inference rules. Experimental results show the effectiveness of the proposed fuzzy logic controllers. A performance comparison of the three controllers is also given.     

Fuzzy logic based adaptive admittance control of a redundantly actuated ankle rehabilitation robot

Ankle rehabilitation robots have recently attracted great attention since they provide various advantages in terms of rehabilitation process from the viewpoints of patients and therapists. This paper presents development and evaluation of a fuzzy logic based adaptive admittance control scheme for a developed 2-DOF redundantly actuated parallel ankle rehabilitation robot. The proposed adaptive admittance control scheme provides the robot to adapt resistance/assistance level according to patients' disability level. In addition, a fuzzy logic controller (FLC) is developed to improve the trajectory tracking ability of the rehabilitation robot subject to external disturbances which possibly occur due to human-robot interaction. The boundary scales of membership functions of the FLC are tuned using cuckoo search algorithm (CSA). A classical proportional-integral-derivative (PID) controller is also tuned using the CSA to examine the performance of the FLC. The effectiveness of the adaptive admittance control scheme is observed in the experimental results. Furthermore, the experimental results demonstrate that the optimized FLC significantly improves the tracking performance of the ankle rehabilitation robot and decreases the steady-state tracking errors about 50% compared to the optimized PID controller. The performances of the developed controllers are evaluated using common error based performance indices indicating that the FLC has roughly 50% better performance than the PID controller.     

发电机组的一种多模型自学习控制

针对发电机组励磁与汽门的综合控制,研究了一种多模型自学习控制(MMSC).首先,建立机组不同工况下的样本数据并归纳模糊控制器(FLC)规则,随后采用模糊聚类算法将样本约简为典型工况,并得到对应于典型工况的模型库与控制器库.MMSC的控制量为多个FLC输出的加权集成,而加权系数由模型匹配程度决定.采用学习能力强的支持向量机来实现FLC的自学习和在线优化.仿真实验验证了MMSC的控制性能和效果.     

Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties

This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.     

基于FPSO的电力巡检机器人的广义二型模糊逻辑控制

针对电力巡检机器人(Power-line inspection robot, PLIR)的平衡调节问题, 设计了广义二型模糊逻辑控制器(General type-2 fuzzy logic controller, GT2FLC); 针对GT2FLC中隶属函数参数难以确定的问题, 通过模糊粒子群(Fuzzy particle swarm optimization, FPSO)算法来优化隶属函数参数. 将GT2FLC的控制性能与区间二型模糊逻辑控制器(Interval type-2 fuzzy logic controller, IT2FLC)和一型模糊逻辑控制器(Type-1 fuzzy logic controller, T1FLC) 的控制性能进行对比. 除此之外, 还考虑了外部干扰对三种控制器控制效果的影响. 仿真结果表明, GT2FLC具有更好的性能和处理不确定性的能力.     

Design of interval type-2 fuzzy sliding-mode controller

In this paper, an interval type-2 fuzzy sliding-mode controller (IT2FSMC) is proposed for linear and nonlinear systems. The proposed IT2FSMC is a combination of the interval type-2 fuzzy logic control (IT2FLC) and the sliding-mode control (SMC) which inherits the benefits of these two methods. The objective of the controller is to allow the system to move to the sliding surface and remain in on it so as to ensure the asymptotic stability of the closed-loop system. The Lyapunov stability method is adopted to verify the stability of the interval type-2 fuzzy sliding-mode controller system. The design procedure of the IT2FSMC is explored in detail. A typical second order linear interval system with 50% parameter variations, an inverted pendulum with variation of pole characteristics, and a Duffing forced oscillation with uncertainty and disturbance are adopted to illustrate the validity of the proposed method. The simulation results show that the IT2FSMC achieves the best tracking performance in comparison with the type-1 Fuzzy logic controller (T1FLC), the IT2FLC, and the type-1 fuzzy sliding-mode controller (T1FSMC).     

Control of a distillation column by type-2 and type-1 fuzzy logic PID controllers

The aim of this paper is to develop a type-1 and a type-2 fuzzy logic PID controller (type-1 FLC and type-2 FLC, respectively) for the control of a binary distillation column, the mathematical model of which is characterized by both high nonlinearities and parameter uncertainties. Attention was focused on the tuning procedure proposed by the authors and representing a development of the original Jantzen [1] method for type-1 and type-2 fuzzy controllers, in particular including input type-2 Gaussian membership functions. A theoretical explanation of the differences in fuzzy controller performance was in fact provided in the light of simulation results. The performance of a type-1 FLC was then compared in simulation with the one of type-2 FLC. All the simulation results confirmed the robustness and the effective control action of each fuzzy controller, with evident advantages for the type-2 FLC.     

Design and analysis of a fuzzy logic controller

The fuzzy logic controller (FLC) presented by Siler and Ying (1989) is discussed here and is proved to be equivalent to a non-fuzzy, nonlinear, proportional-integral (PI) controller. Some characteristic properties of this fuzzy logic controller are then investigated. The achievable performance of such a specific fuzzy controller is examined and found to be not necessarily better than that of the conventional, linear, non-fuzzy PI controller. Various extended designs of the basic FLC, including the FLC with dual control laws and the three-piece FLC, are then presented to enhance control performance. These extensions can provide servo-control performance. These extensions can provide servo-control performance superior to that of the basic FLC design, as illustrated by simulation results. Finally a highly nonlinear neutralization process is advanced to demonstrate the applicability of the various FLCs to industrial process control.     

混沌系统的一种自学习模糊控制

提出一种基于遗传算法的自学习模糊控制方法控制混沌系统。用一种改进的遗传算法学习模糊控制器的隶属度函数，以改善模糊控制器的性能，使其达到良好的控制效果。用此方法控制Ｈｅｎｏｎ系统的混沌行为，效果良好。     

Advanced control algorithms embedded in a programmable logic controller

This paper presents an innovative self-tuning nonlinear controller ASPECT (advanced control algorithms for programmable logic controllers). It is intended for the control of highly nonlinear processes whose properties change radically over its range of operation, and includes three advanced control algorithms. It is designed using the concepts of agent-based systems, applied with the aim of automating some of the configuration tasks. The process is represented by a set of low-order local linear models whose parameters are identified using an online learning procedure. This procedure combines model identification with pre- and post-identification steps to provide reliable operation. The controller monitors and evaluates the control performance of the closed-loop system. The controller was implemented on a programmable logic controller (PLC). The performance is illustrated on a field test application for control of pressure on a hydraulic valve.     

A new methodology of fuzzy constraint-based controller design viaconstraint-network processing

A complete design framework for a fuzzy constraint-based controller based on fuzzy-constraint processing and its semantics and relationship to fuzzy logic is presented. In this paper, the concept of “fuzzy constraints” in problem solving is introduced, and some basic definitions of fuzzy-constraint processing in a constraint network and its semantic modeling are addressed. Then a fuzzy local propagation inference mechanism for reasoning about imprecise information applying the filter operation in a network of constraints is proposed. Moreover, we advance the concurrent fuzzy-logic controller (FLC) to a new type of controller, the fuzzy constraint-based controller (FCC), using a more general predicate calculus and full first-order logic knowledge representation and making use of the idea of fuzzy-constraint processing to model practical dynamic control systems. Finally, simulation results show that a FCC achieves equivalent performance as PD type and PI type FLCs and it also demonstrates superior outcomes to a conventional PID controller in terms of rise time and peak-percent overshoot     

Limit-cycle prediction of a fuzzy control system based ondescribing function method

A limit-cycle is the phenomenon that can be observed in systems composed of nonlinear elements. The phenomenon is of fundamental importance in nonlinear systems and, as far as the design of a nonlinear system is concerned, it should be considered along with the stability analysis. In the paper, the limit-cycle of a system controlled by a fuzzy logic controller (FLC) is addressed via some of the classical control techniques used to analyze nonlinear systems in the frequency domain. First, reasonable assumptions are made on the structure of the FLC by using fuzzy basis functions (FBFs) and the describing function of the FLC is derived to analyze and predict the existence of the limit-cycle of the closed-loop system including the FLC. Finally computer simulation is performed to show how the analysis given in the paper is used to predict the existence of the limit-cycle of the fuzzy control system     

Applying fuzzy logic and genetic algorithms to enhance the efficacy of the PID controller in buffer overflow elimination for better channel response timeliness over the Internet

In this paper two novel intelligent buffer overflow controllers: the fuzzy logic controller (FLC) and the genetic algorithm controller (GAC) are proposed. In the FLC the extant algorithmic PID controller (PIDC) model, which combines the proportional (P), derivative (D) and integral (I) control elements, is augmented with fuzzy logic for higher control precision. The fuzzy logic divides the PIDC control domain into finer control regions. Every region is then defined either by a fuzzy rule or a ‘don't care’ state. The GAC combines the PIDC model with the genetic algorithm, which manipulates the parametric values of the PIDC as genes in a chromosome. The FLC and GAC operations are based on the objective function . The principle is that the controller should adaptively maintain the safety margin around the chosen reference point (represent by the ‘0’ of ) at runtime. The preliminary experimental results for the FLC and GAC prototypes indicate that they are both more effective and precise than the PIDC. After repeated timing analyses with the Intel's VTune Performer Analyzer, it was confirmed that the FLC can better support real‐time computing than the GAC because of its shorter execution time and faster convergence without any buffer overflow. Copyright © 2005 John Wiley & Sons, Ltd.     

The simplest fuzzy controllers using different inference methods are different nonlinear proportional-integral controllers with variable gains

The author analytically proves that the simplest fuzzy controllers using different inference methods are different nonlinear proportional-integral (PI) controllers with proportional-gains and integral-gains changing with inputs of the controllers. The inference methods involved are Mamdani's minimum inference method, Larsen's product inference method, the drastic product inference method and the bounded product inference method. Configuration of the fuzzy controllers is minimal, which includes two input fuzzy sets, three output fuzzy sets, four control rules, Zadeh fuzzy logic AND, Lukasiewicz fuzzy logic OR and a center of gravity defuzzification algorithm. After analytically investigating properties of the nonlinear PI controllers, the author reveals that the bounded product inference method is inappropriate for the control purpose while the other three inference methods are appropriate. Dynamic and static control behaviors of the fuzzy controllers with the appropriate inference methods are analytically compared with each other, and are also compared with those of the linear PI controller. Finally, it is analytically proven that the fuzzy control systems have the same local stability at the equilibrium point as the corresponding linear PI control system does.     

Nonlinear controllers for solar thermal plants: A comparative study

Solar plants have nonlinear dynamics which must be taken into account when a control system is applied to them. The main purpose of the control systems is to maintain the outlet temperature in a desired reference value and, at the same time, attenuate the undesirable transients caused by the disturbances. Linear controllers, like PID ones, are not able to obtain good performance over the whole operation range of these kind of plants. To overcome these limitations two nonlinear controllers, a nonlinear model-based predictive controller and a distributed sliding mode controller, are applied to a solar plant in this work. The performance of these controllers is tested through experimental and simulation results, which show the tracking and disturbance rejection capabilities of the proposed controllers.