Application of a continuous supervisory fuzzy control on a discrete scheduling of manufacturing systems

This paper considers the design and the practical implementation of a stable multiple objective real-time scheduling problem for a complex production system. In this paper, a complex production system is viewed as a kind of systems producing a variety of products (multiple-part-type) under constraints and multiple production objectives often conflicting. Previously, fuzzy control theory and fuzzy intervals arithmetic have been used to develop a distributed and supervised continuous-flow control architecture. In this framework, the objective of the distributed control structure is to balance the production process by adjusting the continuous production rates of the machines on the basis of the average local behavior. The supervisory control methodology aims at maintaining the overall performances within acceptable limits. In the new proposed approach, the problem of a stable real-time scheduling of jobs is considered at the shop-floor level. In this context, as the stability of the control structure is ensured, the actual dispatching times are determined from the continuous production rates through a discretization procedure. To deal with conflicts between jobs at a shared machine, a decision is made. It concerns the actual part to be processed and uses some criterions representing a measure of the job's priority. The simulation results show the validity of the proposed approach in terms of production cost, robustness and system stability.     

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。     

Heuristic on-line tuning for nonlinear model predictive controllers using fuzzy logic

In this paper a systematic mechanism for on-line tuning of the non-linear model predictive controllers is presented. The proposed method automatically adjusts the prediction horizon P, the diagonal elements of the input weight matrix Λ, and the diagonal elements of the output weight matrix Γ for the sake of good performance. The desired good performance is cast as a time-domain specification. The control horizon (M) is left constant because of the importance of its relative value with respect to P. The concepts from fuzzy logic are used in designing the tuning algorithm. In the mechanism considered here, predefined fuzzy rules represent available tuning guidelines and the performance violation measure in the form of fuzzy sets determine the new tuning parameter values Therefore, the tuning algorithm is formulated as a simple and straightforward mechanism, which makes it more appealing for on-line implementation. The effectiveness of the proposed tuning method is tested through simulated implementation on three non-linear process examples. Two of these examples possess open-loop unstable dynamics. The result of the simulations shows that this method is successful and promising.     

A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot

Ant Colony Optimization is a population-based meta-heuristic that exploits a form of past performance memory that is inspired by the foraging behavior of real ants. The behavior of the Ant Colony Optimization algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired optimization algorithms. The present paper explores a new fuzzy approach for diversity control in Ant Colony Optimization. The main idea is to avoid or slow down full convergence through the dynamic variation of a particular parameter. The performance of different variants of the Ant Colony Optimization algorithm is analyzed to choose one as the basis to the proposed approach. A convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence is created. Encouraging results on several traveling salesman problem instances and its application to the design of fuzzy controllers, in particular the optimization of membership functions for a unicycle mobile robot trajectory control are presented with the proposed method.     

The challenge of modelling supervisory systems using fuzzy cognitive maps

This paper examines fuzzy cognitive map (FCM) theory and its use in supervisory control systems. An FCM is a graph used to depict cause and effect between concepts that stand for the states and variables of the system. An FCM represents the whole system in a symbolic manner, just as humans have stored the operation of the system in their brains, thus it is possible to help man's intention for more intelligent and autonomous systems. FCM representation, construction and a mathematical model are examined; a generic system is proposed and the implementation of FCM in a process control problem is illustrated and a model for supervisors of manufacturing systems is discussed. Although an FCM seems to be a simple model of system behaviour, it appears to be a powerful and effective tool describing the behaviour of a system and representing the accumulated knowledge of a system.     

Comparison of two inference methods for P-type fuzzy logic control through experimental investigation using a hydraulic manipulator

This paper presents a comparison of the two important inference schemes: “individual-rule-based inference” and “compositional rule of inference” as applied to fuzzy logic control, through experimental investigation. The techniques are implemented on a hydraulic manipulator of an industrial machine with P-type fuzzy control. The fuzzy logic controller is designed for automatic positioning of the cutter blade of an automated fish-cutting machine. The features of the machine, which uses hydraulic servo control for cutter positioning, are outlined. The performance of the machine under the two inference schemes is examined and contrasted. Some practical implementations of the results are indicated.     

Simplifying the automatic design of a fuzzy logic controller using evolutionary programming

With the availability of a wide range of Evolutionary Algorithms such as Genetic Algorithms, Evolutionary Programming, Evolutionary Strategies and Differential Evolution, every conceivable aspect of the design of a fuzzy logic controller has been optimized and automated. Although there is no doubt that these automated techniques can produce an optimal fuzzy logic controller, the structure of such a controller is often obscure and in many cases these optimizations are simply not needed. We believe that the automatic design of a fuzzy logic controller can be simplified by using a generic rule base such as the MacVicar-Whelan rule base and using an evolutionary algorithm to optimize only the membership functions of the fuzzy sets. Furthermore, by restricting the overlapping of fuzzy sets, using triangular membership functions and singletons, and reducing the number of parameters to represent the membership functions, the design can be further simplified. This paper describes this method of simplifying the design and some experiments performed to ascertain its validity.     

Training for vigilance on the move: a video game-based paradigm for sustained attention

The capacity for superior vigilance can be trained by using knowledge of results (KR). Our present experiments demonstrate the efficacy of such training using a first-person perspective movement videogame-based platform in samples of students and Soldiers. Effectiveness was assessed by manipulating KR during a training phase and withdrawing it in a subsequent transfer phase. Relative to a no KR control condition, KR systematically improved performance for both Soldiers and students. These results build upon our previous findings that demonstrated that a video game-based platform can be used to create a movement-centred sustained attention task with important elements of traditional vigilance. The results indicate that KR effects in sustained attention extend to a first person perspective movement based paradigm, and that these effects occur in professional military as well as a more general population. Such sustained attention training can save lives and the present findings demonstrate one particular avenue to achieve this goal.

Practitioner Summary: Sustained attention can be trained by means of knowledge of results using a videogame-based platform with samples of students and Soldiers. Four experiments demonstrate that a dynamic, first-person perspective video game environment can serve to support effective sustained attention training in professional military as well as a more general population.     

A general approach to rule aggregation in fuzzy logic control

We look at the representation and aggregation of individual rules in the fuzzy logic control system. Two extreme paradigms for rule representation are introduced, the Mamdani model and the logical model. We look at the characteristics of these approaches. We then combine these two approaches to get a general model for the representation of rules. From this general formulation we obtain two soft classes of rules aggregation, or-like and and-like aggregations.     

Hysteresis-based switching algorithms for supervisory control of uncertain systems

We address the problem of controlling a linear system with unknown parameters ranging over a continuum by means of switching among a finite family of candidate controllers. We present a new hysteresis-based switching logic, designed specifically for this purpose, and derive a bound on the number of switches produced by this logic on an arbitrary time interval. The resulting switching control algorithm is shown to provide stability and robustness to arbitrary bounded noise and disturbances and sufficiently small unmodeled dynamics.     

Systematic design of a stable type-2 fuzzy logic controller

Stability is one of the more important aspects in the traditional knowledge of automatic control. Type-2 fuzzy logic is an emerging and promising area for achieving intelligent control (in this case, fuzzy control). In this work we use the fuzzy Lyapunov synthesis as proposed by Margaliot and Langholz [M. Margaliot, G. Langholz, New Approaches to Fuzzy Modeling and Control: Design and Analysis, World Scientific, Singapore, 2000] to build a Lyapunov stable type-1 fuzzy logic control system, and then we make an extension from a type-1 to a type-2 fuzzy logic control system, ensuring the stability on the control system and proving the robustness of the corresponding fuzzy controller.     

Performance enhancement of unfalsified adaptive control strategy using fuzzy logic

ABSTRACT

Unfalsified Adaptive Switching Supervisory Control (UASSC) is a performance-based data-driven methodology to control uncertain systems with the least possible plant assumptions. There are a set of pre-designed controllers in the controller bank, and the goal is to select the best controller at each time instance. The Multi-Model UASSC (MMUASSC) uses the UASSC concept, but it also benefits from a set of pre-specified models in the model bank. This paper introduces a method to improve the performance of the UASSC and MMUASSC by cost function manipulations and fuzzy logic concepts. To achieve this, fuzzy UASSC and fuzzy MMUASSC methods are introduced. In these methods, a time-varying coefficient, which is the output of a fuzzy system, is used along with the conventional cost functions. The input of this fuzzy system is chosen to properly reflect the performance of the corresponding controller in the controller bank. Using this method, the performance of the outside loop controllers is accurately evaluated, and closed-loop stability proof is provided. Also, as the existence of non-minimum phase controllers is problematic, a solution is provided to handle such cases. Finally, simulation results are used to show the effectiveness of the introduced methods.     

Lateral control of autonomous vehicles based on fuzzy logic

Autonomous vehicles have attracted considerable attention in the research community and industry. This paper addresses a problem in designing lateral control law and develops a strategy to determine the given speed of autonomous vehicles. An improved method for calculating the lateral offset and heading angle error is proposed to reduce the impact of reference path data noise. Multiple fuzzy inference engines are used to design the steering controller and determine the given driving speed, including the forward and backward directions. The stability condition is given to guide the design of fuzzy inference engines. Satisfactory simulation and experimental results have been obtained from different reference paths.     

A fuzzy logic-based model for noise control at industrial workplaces

Ergonomics is a broad science encompassing the wide variety of working conditions that can affect worker comfort and health, including factors such as lighting, noise, temperature, vibration, workstation design, tool design, machine design, etc. This paper describes noise-human response and a fuzzy logic model developed by comprehensive field studies on noise measurements (including atmospheric parameters) and control measures. The model has two subsystems constructed on noise reduction quantity in dB. The first subsystem of the fuzzy model depending on 549 linguistic rules comprises acoustical features of all materials used in any workplace. Totally 984 patterns were used, 503 patterns for model development and the rest 481 patterns for testing the model. The second subsystem deals with atmospheric parameter interactions with noise and has 52 linguistic rules. Similarly, 94 field patterns were obtained; 68 patterns were used for training stage of the model and the rest 26 patterns for testing the model. These rules were determined by taking into consideration formal standards, experiences of specialists and the measurements patterns. The results of the model were compared with various statistics (correlation coefficients, max-min, standard deviation, average and coefficient of skewness) and error modes (root mean square error and relative error). The correlation coefficients were significantly high, error modes were quite low and the other statistics were very close to the data. This statement indicates the validity of the model. Therefore, the model can be used for noise control in any workplace and helpful to the designer in planning stage of a workplace.     

Formulation of qualitative models using fuzzy logic

Formulation of qualitative models for complex decision problems exhibiting less structure, more imprecision and uncertainty is not adequately addressed in DSS research. Typical characteristics and requirements of such problems prohibit the development of DSS using knowledge based system development methodologies. This paper presents a methodology for formulation of qualitative models using fuzzy logic to handle the imprecision and uncertainty in the problem domain. The problem domain, in this methodology, is represented using problem-solving knowledge, environmental knowledge, and control knowledge components. A high level non-procedural language for representing these components of knowledge is illustrated using a project selection and resource allocation problem. The paper also describes the implementation of a prototype decision support environment based on this methodology.     

无线传感器网络的模糊功率控制技术

无线传感器网络节点通过撒播造成节点分布不均,分簇后导致分簇疏密不均,影响了网络性能,需要对簇头进行功率控制使网络结构更加优化.但这是一个难以确定其准确数学模型的过程,因此提出了一种采用模糊技术调整簇头功率以改变拓扑结构的方案.最后进行了仿真,实验结果证明通过分簇大小控制以后,网络的生存时间和通信量都有增加.     

Hybrid control of the three phase induction machine using artificial neural networks and fuzzy logic

Nowadays, the microcomputer performs calculations at an incredibly high rate of billions of instructions per second. That represents an exponential increase in the processing speed since the early days of the computer development, eventhough such growth did not show complex reasoning that even the simple biological organisms can make. The artificial intelligence techniques as an attempt to work about those limitations, are a promising alternative.Each intelligent technique has its particular strengths and weaknesses and cannot be universally implemented to any problem. Mixed together, these techniques can improve the solutions quality and allow application to various tasks. It is the reason why the AI is used increasingly in order to solve complex problems in engineering. Where, it is still necessary to make progress in the controller tuning.The idea proposed in this paper is simple and original. It is the result of a study that compared the performances of two controls based on the artificial intelligence techniques: the artificial neural networks and the fuzzy logic. The control proposed in this paper combines in a different manner these two techniques in the form of a hybrid control. The aim is to benefit from performances of each of these techniques, by using them in the same control block at the most suitable place.The performances of the this proposed hybrid control; applied to the three-phase induction motor supplied by voltage source inverter; are investigated and compared to those obtained from the controls based on artificial neural networks; fuzzy logic and conventional techniques. The results of simulation show the feasibility and the good performances achieved by the proposed control.     

Adaptive nonlinear control of pH neutralization processes using fuzzy approximators

In this paper, an adaptive control scheme, based on fuzzy logic systems, for pH control is addressed. For implementation of the proposed scheme no composition measurement is required. Stability of the closed-loop system is established and it is shown that the solution of the closed-loop system is uniformly ultimately bounded and under a certain condition, asymptotical stability is achieved. Effectiveness of the proposed controller is tested through simulation and experimental studies. Results indicate that the proposed controller has good performances in set-point tracking and load rejection and much better than that of a tuned PI controller.     

Controlling the power output of a nuclear reactor with fuzzy logic

The application of fuzzy logic control (FLC) in the domain of nuclear industry presents a tremendous challenge. The main reason for this is the public awareness of the risks of nuclear reactors and the very strict safety regulations in force for nuclear power plants. The very same regulations prevent a researcher from quickly introducing novel control methods into this field. On the other hand, the application of FLC has, despite the ominous sound of the word “fuzzy” to nuclear engineers, a number of very desirable advantages over classical control, e.g. its robustness and the capability to include human experience into the controller. In the present paper we describe an FLC for controlling the power level of a nuclear reactor. The study is intended to assess the applicability of FLC in this domain. The final goal is to develop an optimised and intrinsically safe controller. After reviewing the available literature on FLC in nuclear reactors, an FLC is proposed and first tested by comparing it with the classical controller of BR1 (Belgium's first research reactor). In the next step the BR1 at the Belgian Nuclear Research Centre (SCK · CEN) was used as a test bed to implement a PLC-based hardware controller. The BR1 reactor is internationally regarded as a nuclear calibration reference. It therefore provides an excellent environment for this type of experiments, because over the years considerable knowledge of the static and dynamic properties of the reactor has been accumulated. The progress made in these experiments will be presented and discussed.