A self-learning fuzzy logic controller using genetic algorithmswith reinforcements

This paper presents a new method for learning a fuzzy logic controller automatically. A reinforcement learning technique is applied to a multilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm through reinforcement learning architecture, called a genetic reinforcement fuzzy logic controller, can also learn fuzzy logic control rules even when only weak information such as a binary target of “success” or “failure” signal is available. In this paper, the adaptive heuristic critic algorithm of Barto et al. (1987) is extended to include a priori control knowledge of human operators. It is shown that the system can solve more concretely a fairly difficult control learning problem. Also demonstrated is the feasibility of the method when applied to a cart-pole balancing problem via digital simulations     

Design of adaptive fuzzy logic controller based on linguistic-hedgeconcepts and genetic algorithms

In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and ran speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module, According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simple-shape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three well-known nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design.     

Learning and tuning fuzzy logic controllers through reinforcements

A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.     

PID-type fuzzy logic controller tuning based on particle swarm optimization

In this paper, a new PID-type fuzzy logic controller (FLC) tuning strategy is proposed using a particle swarm optimization (PSO) approach. In order to improve further the performance and robustness properties of the proposed PID-fuzzy approach, two self-tuning mechanisms are introduced. The scaling factors tuning problem of these PID-type FLC structures is formulated and systematically resolved, using a proposed constrained PSO algorithm. The case of an electrical DC drive benchmark is investigated, within a developed real-time framework, to illustrate the efficiency and superiority of the proposed PSO-based fuzzy control approaches. Simulation and experimental results show the advantages of the designed PSO-tuned PID-type FLC structures in terms of efficiency and robustness.     

基于GA的非线性系统Fuzzy控制规则自调整

控制精度和自适应能力一直是模糊控制中较难解决的问题,对于非线性系统更是如此,解决这一技术的核心问题在于控制规则的选取,而遗传算法可以较好地解决常规的数学优化技术不能有效解决的问题。该文给出了对于具有修正因子的控制规则,采用遗传算法对其参数进行自调整的方法,以提高整个控制器的性能。仿真结果表明,这种方法可提高模糊控制器的性能,对非线性系统的控制是有效的。     

A fuzzy logic based multi-agents controller

This paper presents a fuzzy logic based controller (Multi-Agents System Controller (MASC)) which regulates the number of agents released to the network on a Multi-Agents Systems (MASs). A fuzzy logic (FL) model for the controller is as presented. The controller is a two-inputs-one-output system. The controllability is based on the network size (NTZ) and the available bandwidth (ABD) which are the inputs to the controller, the controller’s output is number of agents (ANG). The model was simulated using SIMULINK software. The simulation result is presented and it shows that ABD is the major constraint for the number of agents released to the network.     

Various hybrid methods based on genetic algorithm with fuzzy logic controller

In this paper we propose several efficient hybrid methods based on genetic algorithms and fuzzy logic. The proposed hybridization methods combine a rough search technique, a fuzzy logic controller, and a local search technique. The rough search technique is used to initialize the population of the genetic algorithm (GA), its strategy is to make large jumps in the search space in order to avoid being trapped in local optima. The fuzzy logic controller is applied to dynamically regulate the fine-tuning structure of the genetic algorithm parameters (crossover ratio and mutation ratio). The local search technique is applied to find a better solution in the convergence region after the GA loop or within the GA loop. Five algorithms including one plain GA and four hybrid GAs along with some conventional heuristics are applied to three complex optimization problems. The results are analyzed and the best hybrid algorithm is recommended.     

一种基于遗传算法的非线性PID控制器

基于PID控制器各增益参数与偏差信号之间呈现非线性关系,拟合各参数的非线性函数可分别对控制器的P／I／D各部分实施单独调节的思想,提出根据控制与误差之间的调节规律,给定一组增益参数的非线性函数,并采用遗传算法来优化和构造此非线性PID调节器．典型系统的仿真结果表明,该控制器可在一定程度上兼顾系统的动态和静态性能．     

A PWM fuzzy logic controller

Researchers usually implement fuzzy inference systems in software on digital computers or microprocessors. This approach copes with most problems, however real-time systems often require very short time responses. In this case, a hardware implementation becomes the only solution. This 1.5-μm CMOS implementation uses a current mode circuit to generate input membership functions and processes inferences using pulse width modulation     

一种基于模糊逻辑的入口匝道自适应控制器

高速公路的交通流存在很大的不确定性,模糊逻辑是解决其控制问题的有效方法。对传统的ALINEA模型进行了扩展,提出一种新的自适应模糊匝道控制器。当高速公路路段的临界密度不能被预先正确估计或者因交通环境的实时变化而难以估计时,提出的自适应模糊控制器将显示出其优越性。在仿真试验中,根据交通流的各种性能指标,将新的自适应控制器同传统的ALINEA方法做了详细的对比。     

Training fuzzy logic controller software components by combining adaptation algorithms

An approach for an effective and efficient off-line training of particular classes of reusable controller software components is presented. To build a necessary relationship between a component's abstract and concrete levels, each control software component is represented at the abstract level by means of a set of adaptive fuzzy logic rules and at the concrete level by means of adaptive fuzzy membership functions. Training includes two phases: testing and adapting. The testing phase is for identifying faulty fuzzy elements of a component, while the adapting phase is for modifying membership functions. We employ genetic algorithms, neural network algorithms, Monte Carlo algorithms, and their combinations in each phase. This approach is illustrated by training automotive controller software components (simulation). Experimental simulation results show that our off-line training approach supports controller software component adaptation effectively and efficiently in terms of controlled process operation accuracy and effort spent.     

遗传优化的径向基函数船舶模糊控制器

研究径向基函数模糊神经网络在船舶控制器设计中的应用 ,设计了一个新型的径向基函数模糊神经网络控制器用以适应船舶在时变和不确定环境下的控制性能要求 .控制器设计的主导思想是在传统的径向基函数神经网络中增加一个模糊隐层 ,并采用遗传算法对控制器参数进行优化 .与传统方法相比 ,控制器模糊规则库的设计过程所需的先验知识更少 .最后采用Matlab 6 .1的Simulink工具以船舶运动模型为对象进行了船舶控制的仿真试验 ,结果证明了其有效性     

基于模糊性能指标的广义预测控制器参数调整

针对广义预测控制算法在控制时域中求得的M个控制量，利用模糊模拟技术对系统的约束进行检验，不断修正目标函数中控制量的加权系数，充分利用系统预测控制量的信息，增强系统的鲁棒性，并满足系统的约束。     

Vague controller: A generalization of fuzzy logic controller

In this research, a vague controller (VC) is synthesized by using the notion of vague sets, which are a generalization of fuzzy sets and characterized by a truth-membership function and a falsity-membership function. The vague sets follow the basic set operations and logic operations defined for fuzzy sets, and are superior to fuzzy sets in that they could deal with the uncertainty encountered in real-world applications in a more natural way. Depending on the vague sets, the VC is developed as a generalization of fuzzy logic controller (FLC). The design procedures of the VC, which allow an arbitrary number of input variables, and each variable could have a distinct number of linguistic values, are outlined in this paper. In order to compensate the effort in constructing two series of membership functions for vague sets and to ease the difficulties in designing VCs, a new means of designating membership functions for VCs is also presented in this article. This method constructs a set of membership functions systematically by using only two parameters: number of linguistic values of a linguistic variable and shrinking factor. The membership functions generated by this method, shrinking-span membership functions (SSMFs), have different spans over the universe of discourse and, therefore, are more rational and more practical from the human expert's point of view.     

Bio-inspired fuzzy logic based tuning of power system stabilizer

In this paper, bacteria foraging optimization (BFO) – a bio-inspired technique, is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. A comparative performance study of these four variants of PSSs is also made. It is revealed that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. A comparison between the results of the BFO and that of genetic algorithm (GA) is conducted in this study. The comparison reveals that BFO is more effective than GA in finding the optimal transient performance. For on-line, off-nominal operating conditions Sugeno fuzzy logic (SFL) based approach is adopted. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer parameters.     

基于模糊控制器的自适应广义通用模型控制

广义通用模型控制(GCMC)方法是一般模型控制(GMC)的改进,适用于相对阶大于1的复杂多输入多输出系统,该控制器参数具有明显的物理意义,但鲁棒性不够强。将模糊控制与广义通用模型控制相结合,构成模型参考自适应控制系统,从而加强了系统的鲁棒性,仿真实验证明了该策略的有效性。     

高速公路非线性反馈模糊逻辑匝道控制器

入口匝道控制是高速公路交通控制和智能运输系统的重要组成部分,但现有的入口匝道控制效果尚不理想.为此,本文提出一种非线性反馈方法用模糊逻辑进行入口匝道控制.建立了高速公路交通流动态模型,在此基础上,结合模糊逻辑理论设计了非线性反馈匝道控制器,根据密度误差和误差变化用模糊控制决定匝道调节率,模糊变量选用三角形隶属度函数,并制定了包含56条模糊规则的规则库,最后用MATLAB软件进行系统仿真.结果表明该控制器具有优越的动态和稳态性能,它能使高速公路主线交通流密度保持为设定的期望密度,该方法用在高速公路入口匝道控制中效果良好.     

A high-speed, reconfigurable fuzzy logic controller

The modular architecture and reconfigurable inference engine of this analog fuzzy controller offer more flexibility than existing implementations. Its high inference speed and small size make the controller suitable for embedded system applications. The journal issue contains a concise summary of this article. The complete article is linked to Micro's home page on the World Wide Web (http://www.computer.org/pubs/micro/micro.htm)     

GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms

Fuzzy logic allows mapping of an input space to an output space. The mechanism for doing this is through a set of IF-THEN statements, commonly known as fuzzy rules. In order for a fuzzy rule to perform well, the fuzzy sets must be carefully designed. A major problem plaguing the effective use of this approach is the difficulty of automatically and accurately constructing the membership functions. Genetic Algorithms (GAs) is a technique that emulates biological evolutionary theories to solve complex optimization problems. Genetic Algorithms provide an alternative to our traditional optimization techniques by using directed random searches to derive a set of optimal solutions in complex landscapes. GAs literally searches towards the two end of the search space in order to determine the optimum solutions. Populations of candidate solutions are evaluated to determine the best solution. In this paper, a hybrid system combining a Fuzzy Inference System and Genetic Algorithms—a Genetic Algorithms based Takagi-Sugeno-Kang Fuzzy Neural Network (GA-TSKfnn) is proposed to tune the parameters in the Takagi-Sugeno-Kang fuzzy neural network. The aim is to reduce unnecessary steps in the parameters sets before they can be fed into the network. Modifications are made to various layers of the network to enhance the performance. The proposed GA-TSKfnn is able to achieve higher classification rate when compared against traditional neuro-fuzzy classifiers.     

基于约束逻辑规划和遗传算法的柔性调度

根据柔性生产环境的特点，描述了约束逻辑规划（CLP）和遗传算法（GA）在解决调度问题中的应用框架。CLP的解决作为满足约束的调度问题的起始解，保证了初始解的合理性。把CLP用作计算每一代样本的约束检验手段，有利于在遗传算法的搜索中获得更好的解和更高的解算效率。最后对一个规模足够大的调度实例进行了计算。