Robust tracking designs for both holonomic and nonholonomicconstrained mechanical systems: adaptive fuzzy approach

Adaptive fuzzy-based tracking control designs are proposed in the paper for both holonomic mechanical systems as well as a large class of nonholonomic mechanical systems with plant uncertainties and external disturbances. A unified and systematic procedure is employed to derive the controllers for both holonomic and nonholonomic mechanical control systems, respectively. First, a fuzzy logic system is introduced to learn the behavior of unknown (or uncertain) mechanical dynamics by using an adaptive algorithm. Next, the effect of approximation error on the tracking error must be efficiently eliminated by employing an additional robustifying algorithm. Consequently, hybrid adaptive-robust controllers can be constructed such that the resulting closed-loop mechanical systems guarantee a satisfactorily transient and asymptotic performance. Furthermore, a partitioned procedure with respect to the above developed adaptive fuzzy logic approximators is introduced such that the number of fuzzy IF-THEN rules is significantly reduced and the developed control schemes can be easily implemented from the viewpoint of practical applications. Finally, simulation examples are presented to illustrate the tracking performance of a two-link constrained robot manipulator and a vertical wheel rolling on a plane surface by the proposed adaptive fuzzy-based control algorithms     

A new evolving compact optimised Takagi–Sugeno fuzzy model and its application to nonlinear system identification

A new encoding scheme is presented for a fuzzy-based nonlinear system identification methodology, using the subtractive clustering and non-dominated sorting genetic algorithm. The proposed method consists of two parts. The first part is related to the selection of most relevant or influencing inputs to the system and the second one is related to the tuning of fuzzy rules and parameters of the membership functions. The main purpose of the proposed scheme is to reduce the complexity and increase the accuracy of the model. In particular, three objectives are considered in the process of optimisation, namely, the number of inputs, number of rules and the root mean square of the modelling error. The performance of the developed method is validated by identifying the Box–Jenkins nonlinear benchmark system, and to the modelling of the forward and inverse dynamic behaviours of a magneto-rheological (MR) damper. The latter is also a challenging problem due to the inherent hysteretic and highly nonlinear dynamics of the MR damper. It is shown that the developed evolving Takagi–Sugeno (T–S) fuzzy model can identify and grasp the nonlinear dynamics of both systems very well, while a small number of inputs and fuzzy rules are required for this purpose.     

Fuzzy-based methodology for multi-objective scheduling in a robot-centered flexible manufacturing cell

A fuzzy logic based methodology for generating the sequence of part movements in a multi-product batch processing through a computerized machine cell is presented in this paper. A number of production objectives are taken into account. Two fuzzy based strategies: fuzzy-job and fuzzy-machine are proposed and their performance is compared to two well known dispatching rules such as SPT (Shortest Processing Time) and WEED (Weighted Earliest Due Date). The sequencing algorithm was implemented on a standard personnel computer and the scheduler was interfaced to a robot controller for implementing loading and unloading strategy within the cell. The proposed fuzzy-based methodologies especially fuzzy-job shows a superior performance compared to the traditional dispatching rules considered.     

规则简化与模糊决策树剪枝的比较

决策树归纳学习算法是机器学习领域中解决分类问题的最有效工具之一。由于决策树算法自身的缺陷了，因此需要进行相应的简化来提高预测精度。模糊决策树算法是对决策树算法的一种改进，它更加接近人的思维方式。文章通过实验分析了模糊决策树、规则简化与模糊规则简化；模糊决策树与模糊预剪枝算法的异同，对决策树的大小、算法的训练准确率与测试准确率进行比较，分析了模糊决策树的性能，为改进该算法提供了一些有益的线索。     

Dynamic fuzzy logic and reinforcement learning for adaptive energy efficient routing in mobile ad-hoc networks

In this paper, a dynamic fuzzy energy state based AODV (DFES-AODV) routing protocol for Mobile Ad-hoc NETworks (MANETs) is presented. In DFES-AODV route discovery phase, each node uses a Mamdani fuzzy logic system (FLS) to decide its Route REQuests (RREQs) forwarding probability. The FLS inputs are residual battery level and energy drain rate of mobile node. Unlike previous related-works, membership function of residual energy input is made dynamic. Also, a zero-order Takagi Sugeno FLS with the same inputs is used as a means of generalization for state-space in SARSA-AODV a reinforcement learning based energy-aware routing protocol. The simulation study confirms that using a dynamic fuzzy system ensures more energy efficiency in comparison to its static counterpart. Moreover, DFES-AODV exhibits similar performance to SARSA-AODV and its fuzzy extension FSARSA-AODV. Therefore, the use of dynamic fuzzy logic for adaptive routing in MANETs is recommended.     

Interference aware vertical handoff decision algorithm for quality of service support in wireless heterogeneous networks

Next generation wireless networks concept aims at collaboration of various radio access technologies in order to provide quality of service (QoS) supported and cost efficient connections at anywhere and anytime. Since the next generation wireless systems are expected to be of heterogeneous topology, traditional handoff (horizontal handoff/handover) mechanisms are not sufficient to meet the requirements of these types of networks. More intelligent vertical handoff algorithms which consider user profiles, application requirements, and network conditions must be employed in order to provide enhanced performance results for both user and network. Moreover, frequency reuse of one (FRO) seems to be the strongest candidate of deployment options for next generation wireless networks; therefore, interference conditions gains a significant attention in vertical handoff decision making process. In this study, a fuzzy logic-based handoff decision algorithm is introduced for wireless heterogeneous networks. The parameters; data rate, received signal strength indicator (RSSI), and mobile speed are considered as inputs of the proposed fuzzy-based system in order to decide handoff initialization process and select the best candidate access point around a smart mobile terminal. Also, in contrast to the traditional fuzzy-based algorithms, the method proposed takes ambient interference power, which is referred to as interference rate, as another input to the decision process. The results show that the performance is significantly enhanced for both user and network by the method proposed.     

Interval type-2 fuzzy logic based antenatal care system using phonocardiography

The assessment of fetal wellbeing depends heavily on variations in fetal heart rate (FHR) patterns. The variations in FHR patterns are very complex in nature thus its reliable interpretation is very difficult and often leads to erroneous diagnosis. We propose a new method for evaluation of fetal health status based on interval type-2 fuzzy logic through fetal phonocardiography (fPCG). Type-2 fuzzy logic is a powerful tool in handling uncertainties due to extraneous variations in FHR patterns through its increased fuzziness of relations. Four FHR parameters are extracted from each fPCG signal for diagnostic decision making. The membership functions of these four inputs and one output are chosen as a range of values so as to represent the level of uncertainty. The fuzzy rules are constructed based on standard clinical guidelines on FHR parameters. Experimental clinical tests have shown very good performance of the developed system in comparison with the FHR trace simultaneously recorded through standard fetal monitor. Statistical evaluation of the developed system shows 92% accuracy. With the proposed method we hope that, long-term and continuous antenatal care will become easy, cost effective, reliable and efficient.     

Multiple attribute decision-making methods for the dynamic operator allocation problem

This study explores two multiple attribute decision-making (MADM) methods to solve a dynamic operator allocation problem. Both methods use an analytic hierarchy process (AHP) to determine attribute weights a priori. The first method uses a technique for order preference by similarity to ideal solution (TOPSIS). The second method incorporates a fuzzy-based logic that uses linguistic variable representation, fuzzy operation, and fuzzy defuzzification. The TOPSIS uses deterministic performance ratings and attribute weights, whilst the fuzzy-based is a linguistic method. An applied case study drawn from existing literature is used to demonstrate and test findings. The proposed methods systematically evaluate alternative scenarios, with the result indicating promise for solving an operator allocation decision problem.     

命题逻辑公式模糊软集语义及其在决策分析中的应用

基于论域公式引入软命题逻辑公式概念,给出软命题逻辑公式的模糊软语义解释.将决策模糊信息系统转化为决策模糊软集,软决策规则表示为包含有蕴含联结词的软命题逻辑公式.引入软命题逻辑公式的基本真度、条件真度、绝对真度等指标,从充分性、必要性等方面评价软决策规则的有效性、合理性.提出基于决策软集的典型软决策规则提取算法和基于软决策分析的推荐算法,并通过实例和数值实验证明算法的有效性.     

A type-2 fuzzy logic controller design for buck and boost DC–DC converters

Conventional (type-1) fuzzy logic controllers have been commonly used in various power converter applications. Generally, in these controllers, the experience and knowledge of human experts are needed to decide parameters associated with the rule base and membership functions. The rule base and the membership function parameters may often mean different things to different experts. This may cause rule uncertainty problems. Consequently, the performance of the controlled system, which is controlled with type-1 fuzzy logic controller, is undesirably affected. In this study, a type-2 fuzzy logic controller is proposed for the control of buck and boost DC–DC converters. To examine and analysis the effects of the proposed controller on the system performance, both converters are also controlled using the PI controller and conventional fuzzy logic controller. The settling time, the overshoot, the steady state error and the transient response of the converters under the load and input voltage changes are used as the performance criteria for the evaluation of the controller performance. Simulation results show that buck and boost converters controlled by type-2 fuzzy logic controller have better performance than the buck and boost converters controlled by type-1 fuzzy logic controller and PI controller.     

Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network (WSN), Visual-WSN (VWSN) utilizes cameras to obtain relevant data including visual recordings and static images. Data from the camera is sent to energy efficient sink to extract key-information out of it. VWSN applications range from health care monitoring to military surveillance. In a network with VWSN, there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy, memory and I/O resources. In this case, Mobile Sinks(MS) can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head (CH), it also collects data from nearby nodes as well. The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system. However, making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account. We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe, learn and understand things from manual perspective. Proposed architecture is designed based on Mamdani’s fuzzy model. Following parameters are derived based on the model residual energy, node centrality, distance between the sink and current position, node centrality, node density, node history, and mobility of sink as input variables for decision making in CH selection. The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN. The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm (GA) and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules. Genetic algorithm-based machine learning optimizes the interpretability aspect of fuzzy system. Simulation results are obtained using MATLAB. The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-Expected Residual Energy (LEACH-ERE).     

基于遗传算法的管道机器人分层模糊控制

为了适应空调管道的复杂环境,提高空调管道清扫机器人控制性能,降低控制复杂度,构建了一个分层的自适应模糊控制器.通过多输入系统进行分层输入和叠加输出,大量简化了模糊逻辑推理,减少了控制规则数.应用遗传算法对控制器参数进行优化,有效地融合了控制信息,达到了较理想的控制特性.实验和仿真结果表明,模糊控制器适应性强,优化后模糊控制器具有很好的控制效果,实现了机器人在管道中自主导航.     

Using B-spline neural network to extract fuzzy rules for a centrifugal pump monitoring

In mechanical equipment monitoring tasks, fuzzy logic theory has been applied to situations where accurate mathematical models are unavailable or too complex to be established, but there may exist some obscure, subjective and empirical knowledge about the problem under investigation. Such kind of knowledge is usually formalized as a set of fuzzy relationships (rules) on which the entire fuzzy system is based upon. Sometimes, the fuzzy rules provided by human experts are only partial and rarely complete, while a set of system input/output data are available. Under such situations, it is desirable to extract fuzzy relationships from system data and combine human knowledge and experience to form a complete and relevant set of fuzzy rules. This paper describes application of B-spline neural network to monitor centrifugal pumps. A neuro-fuzzy approach has been established for extracting a set of fuzzy relationships from observation data, where B-spline neural network is employed to learn the internal mapping relations from a set of features/conditions of the pump. A general procedure has been setup using the basic structure and learning mechanism of the network and finally, the network performance and results have been discussed.     

AGGREGATION OF FUZZY NUMBERS IN A DECISION-MAKING SITUATION

The fuzzy logic operators are used to decide the following multicriteria decision-making problem. A finite set of alternatives is evaluated by a set of fuzzy criteria, i.e., the alternatives' estimations are fuzzy numbers. An aggregation procedure mapping each m -tuple of fuzzy numbers into one fuzzy number, which represents the alternative according to the whole set of criteria, is presented. As functions of this mapping some known fuzzy logic operators are used. A method for comparison of ordinary fuzzy numbers, based on f -level subareas, is proposed. This method gives a possibility to decide ranking or choice problem. A numerical example is given as well.     

Knowledge-Driven versus Data-Driven Logics

The starting point of this work is the gap between two distinct traditions in information engineering: knowledge representation and data-driven modelling. The first tradition emphasizes logic as a tool for representing beliefs held by an agent. The second tradition claims that the main source of knowledge is made of observed data, and generally does not use logic as a modelling tool. However, the emergence of fuzzy logic has blurred the boundaries between these two traditions by putting forward fuzzy rules as a Janus-faced tool that may represent knowledge, as well as approximate non-linear functions representing data. This paper lays bare logical foundations of data-driven reasoning whereby a set of formulas is understood as a set of observed facts rather than a set of beliefs. Several representation frameworks are considered from this point of view: classical logic, possibility theory, belief functions, epistemic logic, fuzzy rule-based systems. Mamdani's fuzzy rules are recovered as belonging to the data-driven view. In possibility theory a third set-function, different from possibility and necessity plays a key role in the data-driven view, and corresponds to a particular modality in epistemic logic. A bi-modal logic system is presented which handles both beliefs and observations, and for which a completeness theorem is given. Lastly, our results may shed new light in deontic logic and allow for a distinction between explicit and implicit permission that standard deontic modal logics do not often emphasize.     

一种自调整模糊控制器的设计与仿真研究

提出了一种基于规则，参数自整定的模糊控制器的设计方法，它通过引入误差加权因子和误差变化率加权因子，以系统阶跃响应的上升时间和超调量为性能评价指标，并根据评价结果建立自适应算法，对模糊控制器的模糊推理规则及参数进行自调整。仿真结果进行表明，误差变化率加权因子以明显改善模糊控制器的稳定性，性能评价指标函数，模糊推理规则和参数调整算法简单实用，便于实现，对被控对象的参数变化具有较强的适应能力，控制效果良好。     

Fuzzy decision support system for ship lock control

This paper presents the development of a Decision Support System (DSS) for the management of ship locks that relies on fuzzy logic. It contains a brief overview of the history and the construction of locks and basic information related to fuzzy logic, fuzzy linguistic variables and methods used in approximate reasoning. In reality, ship lock control is mostly based on the subjective estimations and the experience of a lock master (ship lock operator). The fuzzy set theory is the most favourable mathematical approach for consideration of indefiniteness and subjective estimates. This paper analyses the control process of a ship lock on a two-way waterway, with one chamber designed for one vessel. A control algorithm is constructed according to a set of linguistic rules that describes the operator’s control strategy. The subjective estimations are therefore implemented in the algorithm as fuzzy sets. Fuzzy rules aggregate the final fuzzy set and defuzzification produces a decision. A set of ship traffic data is generated for analysis and simulation purposes based on the annual distribution of ship arrivals at the lock. Two criteria are presented and used in parallel with the Fuzzy DSS (FDSS). These two extreme criteria reflect the interests of shippers on one side and workers and owners of the lock on the other side. These interests occur in actual systems and are used here to evaluate the results obtained using the FDSS. This paper additionally describes the design of the SCADA (Supervisory Control And Data Acquisition) software. This software relies on a PLC (Programmable Logic Controller) and provides a platform on which to implement the desired fuzzy algorithm. The software was developed with the suggestions of operators who have extensive experience in ship lock control. The presented control system can be used for support in decision making in control processes and in the training of new operators of ship locks.