Fuzzy Control Concept Research Articles

Vehicle Simulation System: Controls and Virtual-reality-based Dynamics Simulation

This paper introduces a cable-suspended robot, the vehicle simulation system, designed for pilot and driver training. The main idea of this work is to make a system that will provide realistic simulations of vehicle motion using virtual reality and a motion platform. A method to produce inertial forces acting on the vehicle user is proposed. A detailed description of the robot's dynamics constraints is provided, accompanied with a numerical solution. Using the developed mathematical model, the robot workspace is analyzed, as well as the influence of the end-effector orientation angle ranges on the available workspace. Adequate robot platform motion is provided through four control systems in a form of a cascade control with a fuzzy logic controller in outer loops. The cable robot is non-linear and difficult from a classical control point of view, since it requires a local linearization of the dynamics equations. A fuzzy logic control concept has been applied in order to control the end-effector position, velocity and acceleration and it turns out to be convenient in simulation, allied with inner-loop classical control. As the system is under-constrained, with nine unknown cable tensions and six dynamics equations, an optimization method is used. The control complexity is increased by the requirement, specific to cable robots, of maintaining positive cable tensions. The system simulations are done using a virtual reality environment and results are presented accompanied with the discussion.

Implementation of call admission control scheme in next generation mobile communication networks using particle swarm optimization and fuzzy logic systems

In the present and next generation wireless networks, cellular system remains the major method of telecommunication infrastructure. Since the characteristic of the resource constraint, call admission control is required to address the limited resource problem in wireless network. The call dropping probability and call blocking probability are the major performance metrics for quality of service (QoS) in wireless network. Many call admission control mechanisms have been proposed in the literature to decrease connection dropping probability for handoffs and new call blocking probability in cellular communications. In this paper, we proposed an adaptive call admission control and bandwidth reservation scheme using fuzzy logic control concept to reduce the forced termination probability of multimedia handoffs. Meanwhile, we adopt particle swarm optimization (PSO) technique to adjust the parameters of the membership functions in the proposed fuzzy logic systems. The simulation results show that the proposed scheme can achieve satisfactory performance when performance metrics are measured in terms of the forced termination probability for the handoffs, the call blocking probability for the new connections and bandwidth utilization.

Fuzzy predictive control of a solar power plant

This work presents the application of fuzzy predictive control to a solar power plant. The proposed predictive controller uses fuzzy characterization of goals and constraints, based on the fuzzy optimization framework for multi-objective satisfaction problems. This approach enhances model based predictive control (MBPC) allowing the specification of more complex requirements. A brief description of the solar power plant and its simulator is given. Basic concepts of predictive control and fuzzy predictive control are introduced. Two fuzzy predictive controllers using different membership functions are designed for a solar power plant, and they are compared with a classical predictive controller. The simulation results show that the fuzzy MBPC formulation, based on a well proven successful algorithm, gives a greater flexibility to characterize the goals and constraints than classical control.

An add-on type fuzzy controller for control system retrofit

The present paper advocates the concept of a supervisory fuzzy controller. The proposed FLC algorithm employs a parallel architecture where an additional PD-type FLC can be employed either in conjunction with a conventional PID controller or a static fuzzy controller to improve system performance. The additional FLC implements a different rule base compared to a standard PD-type FLC, when employed alone. The effectiveness of the proposed scheme has been demonstrated by implementing it in simulation for two linear plant models of marine diesel engines and a nonlinear process plant. Its performance has been compared vis-` a-vis a standard PD-type FLC, employed in simulation, for the same processes. The system performance is evaluated under both transient and steady state load disturbance conditions and also under noisy measurement environment.

113 ルール型擬似スライディングモード制御に関する研究 : 制御パラメータの設計法

As a new control technique, rule-based pseudo sliding-mode control system is developed for an inverted pendulum system. In the rule based pseudo sliding-mode control system, the transient solution is constrained on the subspace that is spanned by the corresponding eigenvector with negative eigenvalue of the linearized system, and consequently, the similar motion to the sliding-mode is generated. However, the chattering does not occur at all. The control input is evaluated by applying the concept of fuzzy control. The validity of the present control technique is confirmed from the results of numerical simulation.

Capturing and Tuning Nonlinear Characteristics of Economic Stabilization Systems by Fuzzy Control Techniques

Various techniques have been used to control or stabilize economic processes: from econometric models based on rational expectations (assuming that shifts in economic policy produce revised expectations of rational agents), to regulatory mechanisms, normally regarded as engineering tools. However, applying nonconventional techniques, such as fuzzy control, could provide more flexible alternatives to the conventional way. Presumably, the interest in applying fuzzy control to economic processes consists of at least two advantages: on one hand in prescribing control actions by linguistic descriptions and on the other hand in the capability of transition from linear to nonlinear modes of control, conjugated with fine-tuning procedures. This paper is intended to exemplify the latter opportunity. After briefly describing certain fundamental concepts of fuzzy control, we focus on the design of fuzzy linear controllers that emulate conventional modes of control in the first stage and on the refinement of such fuzzy controllers by making them progressively nonlinear in the second stage. As an application of fuzzy control to economic processes, we provide a fuzzy extension of the Phillips' stabilization model in two variants: for a closed economy as well as for an open economy. To implement the control schemata, tools like Matlab and Simulink are used.

Design considerations for a Model Reference Fuzzy Adaptive Controller

A major difficulty with fuzzy logic control is the development of an adequate rule-base and its modification such that the desired performance is maintained with changing operating conditions. Rule elicitation is a lengthy process and specific to each application. To overcome this problem, the concepts of self-organizing fuzzy logic control and other self-learning approaches have been widely investigated. In this paper, a different type of learning fuzzy control algorithm has been discussed. The control method has been called Model Reference Fuzzy Adaptive Control (MRFAC) indicating the method of adaptation. A reference model is used to provide performance feedback for automatically synthesizing and modifying a fuzzy controller’s rule-base on-line, as new information on how to control the system is gathered. The method employs a simple iterative adaptation algorithm compared to other methods in the literature. The design issues involving the method have been thoroughly investigated. Extensive simulation tests have been executed on the MRFAC to examine the effectiveness of choice of its different parameters. The results provide an useful insight into the design considerations involving the MRFAC.

Structural vibration control

Undesirable time-variable motions of dynamical structures (e.g. scales, balances, vibratory platforms, bridges and buildings) are mainly caused by unknown or uncertain excitations. In a variety of applications it is desirable or even necessary to attenuate these disturbances in an effective way and with moderate effort. Hence, several passive as well as active methods and techniques have been developed in order to treat these problems. However, employment of active techniques often fails because of their considerable financial costs. We propose an affordable control scheme which accounts for the above-mentioned deficiencies. In addition, we allow constraints on control actions. Furthermore, the number of control inputs (actuators) may be arbitrary, i.e., the system may be mismatched. The scheme is based on Lyapunov stability theory and, provided that the bounds of the uncertainties are a priori known, a stable attractor (ball of ultimate boundedness) of the structure can be computed. In case measurement errors or uncertainties, respectively, are significant, it is shown how the Lyapunov-based control scheme may be combined with a fuzzy control concept. The effectiveness and behavior of the control scheme is demonstrated on two simplified models of elastic structures such as a two story building and a bridge subjected to a moving truck.

Fuzzy dynamic path planning using genetic algorithms

Fuzzy control concepts are useful in both global and local path planning tasks for autonomous mobile objects. The search for an optimal decision table to be used as the inference engine in fuzzy-based planning and navigation algorithms is highly important. The authors address the problem using genetic algorithms as a search and optimisation tool. The approach yields better planned paths compared to those obtained from random decision tables generated using rules of thumb.

Fuzzy logic controller design utilizing multiple contending software agents

The concept of fuzzy logic control based on a distributed multi-agent system is described in this paper. It is shown that a system composed of a number of cooperating and contending intelligent agents can perform well in terms of sensing the measured input data and producing output control signals. Each agent's implementation consists of a number of fuzzy logic control rules. Although at each time step, all the agents compete for the control of the system by proposing a possible action, the main controller chooses among the selections based on a number of evaluation criteria. It is through the simultaneous cooperation (to overall control the system) and competition (to take control at a given time), that the system yields convincing results. The effectiveness of the novel methodology is illustrated through the control of a robotic system that keeps an inverted pendulum in balance while being perturbed by a human force. In addition, the proposed methodology is applied to the practical control of a service mobile manipulator interacting with a human. It is shown that fuzzy logic control combined with intelligent multi-agent architecture can yield effective and efficient intelligent controllers.

Fuzzy Control Concept of Artificial Pneumatic Muscle

Fuzzy Control Concept of Artificial Pneumatic Muscle

Improvement of control performance for low-dimensional number of fuzzy labelings using simplified inference method

The central concept of fuzzy control is fuzzy inference, and the “ simplified inference method” which is aimed at increasing the speed of the fuzzy inference has recently been used to realize a high-speed fuzzy controller. Also, in designing a fuzzy controller, a high dimension such as 7 x 7 and 5 x 5 partitions is frequently used as the number of fuzzy labelings. However, as the number of fuzzy labelings becomes high-dimensional, the number of control parameters increases rapidly and the tuning of a fuzzy controller becomes difficult. Therefore, it is required that a fuzzy controller is partitioned into a low-dimensional number of fuzzy labelings such as 3 × 3 partition. With this in mind, first, an improvement method of control performance for low-dimensional number of fuzzy labelings using the “simplified inference method” with high-speed inference is proposed. Next, the effectiveness of this improvement method is examined based on several simulation results. Lastly, this improvement method is investigated using a control map which represents the nonlinear characteristics of a fuzzy controller.

Fuzzy control in a single-machine system

Abstract As a new application area we have been tried to apply the fuzzy control concept in discrete event dynamic systems (DEDS) control problems. The fuzzy control of a single-machine system is presented. Simulation studies have been performed on the basis of a descriptive model for investigating the control performance. Two other control approaches, namely proportional control (P-control) and load-oriented order release control (LOOR-control), are employed to compare with the fuzzy control approach. It is concluded that fuzzy control is a practical and effective way to control DEDS. The fuzzy control module developed during this study can be easily adapted for fuzzy control of other DEDS, for example job-shop systems. This paper should be considered as the first step in using fuzzy control concept for the control of DEDS.

Comparison of Fuzzy Control Schemes on Real-Time Pressure Control

Direct fuzzy control of the Mamdani type, fuzzy supervised PI control and fuzzy predictive control were applied to nonlinear pressure control. The main goal of this study was to compare the three different fuzzy control concepts in terms of the development time, type and amount of prior information needed for the controller design, the tuning requirements and the closed loop performance.

The fuzzy logic of visuomotor control

Biological sensorimotor control is characterized by the use of signals from large numbers of sensors, monitoring numerous variables. Among these are the exteroceptive signals from the eyes and ears. Many of the sensory signals are under efferent control, and the motor responses they evoke, whether at a simple reflex level or routed through the higher centres, appear to be task and context dependent. In technology the analysis and management of multiple-input, multiple-output systems clearly exceed the capabilities of classical servo control theory. In this commentary, new types of control system based on conditional logic are discussed in relation to the rules animals use to control movement. It is argued that the concepts of fuzzy logic control provide a useful and "biologically compatible" way of describing sensorimotor behaviour. An example is given of a robotic device under fuzzy control, in which behaviours are selected according to a visual assessment of motor task and context. Each behaviour is associated with a small subset of rules relating specific sensory variables to specific motor actions. The rule-based approach is also discussed in relation to neurophysiological theories regarding the interneuronal control of locomotion, including the recently adduced "parliamentary principle." The analysis and classification of behaviours and rules is seen as a useful preliminary to the future study of interneuronal systems.

Subject index of volume 33

This paper presents the design of a new nonlinear expert controller for single-machine infinite-bus and multimachine power systems. The controller uses the concepts of fuzzy linguistic control for normal and abnormal operating conditions of the power system. A nonlinear supervisory controller based on variable-structure system theory is designed to augment the linguistic fuzzy controller for severe transient disturbances when there is a possibility of the stability being lost. This robust nonlinear expert controller is applied to a single-machine infinite-bus system and some other selected machines in a multimachine power system subject to a variety of transient disturbances. The damping performance of this controller is found to be significantly better than the traditional fuzzy and conventional power system stabilizers.

Fuzzy control concept for a total artificial heart.

The development of an electromechanically driven total artificial heart (Helmholtz-TAH) was initiated in 1990. Anatomical fitting, biocompatibility, and automatic physiologic adaptation of pump output are the basic requirements that characterize the overall TAH concept. For evaluation of these features, a TAH labtype was developed. It provides most features of the conceptual artificial heart and supports in vitro testing of energy conversion, pump behavior, structural parts, sensors, and control concepts. A fuzzy controller has been implemented for adaptation of the pump rate to body perfusion demand by left pump chamber filling detection. This controller will be an important element of a future extensive TAH control system. The implementation is supported by a professional fuzzy control development tool that allows on-line and real time optimization of control strategies for dynamic processes. The first experiments proved the feasibility and the advantages of this fuzzy control concept. The first in vitro test results are presented.

Design of nonlinear expert supervisory controllers for power system stabilization

This paper presents the design of a new nonlinear expert controller for single-machine infinite-bus and multimachine power systems. The controller uses the concepts of fuzzy linguistic control for normal and abnormal operating conditions of the power system. A nonlinear supervisory controller based on variable-structure system theory is designed to augment the linguistic fuzzy controller for severe transient disturbances when there is a possibility of the stability being lost. This robust nonlinear expert controller is applied to a single-machine infinite-bus system and some other selected machines in a multimachine power system subject to a variety of transient disturbances. The damping performance of this controller is found to be significantly better than the traditional fuzzy and conventional power system stabilizers.

Hypertension control during anesthesia. Fuzzy logic regulation of nicardipine infusion

In the present study, the authors used nicardipine as the hypotensive drug, and determined its infusion rate based on the state-action diagram of fuzzy control rules, that the authors developed, to evaluate the concept of Zadeh's fuzzy logic controller. The authors then developed an automated blood pressure control system based on a fuzzy expert system, and evaluated its clinical efficacy and safety. The main characteristic of a fuzzy logic controller is that its action is very close to human judgement. It was concluded that the state-action diagram and the automated blood pressure controller, both of which were made possible with the concept of Zadeh's fuzzy logic control rules, are satisfactory and safe methods of administering nicardipine.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Use of Fuzzy Control in Automated Soil Testing

Abstract A servo-control algorithm that uses fuzzy logic has been developed for automated soil testing. The algorithm emulates the reasoning process of an experienced operator in controlling the pressure. The amount of pressure adjustment and time delay between adjustments is determined by fuzzy inference matrices. The fuzzy control logic provides a framework for the development of intelligent control algorithms which can be adapted in other aspects of automated soil testing. This paper introduces the concept of fuzzy control and its application in soil testing. Results of controlled gradient consolidation tests are presented to demonstrate the efficacy of fuzzy control.