Order Fuzzy Controller Research Articles

Based on Road Green Wave Effect of Collaborative Strategy of Signal Timing Fuzzy Control

With growth of Urban Road Traffic Volume and the increase of Road Network Density, correlation between adjacent road intersections is becoming more and more obvious. An intersection traffic signal adjustment tends to affect the health of a number of adjacent intersections road traffic flow. Its congestion may over time gradually spread to within a few blocks and regions all around the intersection. Therefore increasingly high demands of urban traffic signal control make a variety of advanced control technology integration, achieve the purpose to adjust a control parameter, in order to achieve dynamic coordination within the city - wide traffic control, to satisfy traffic demands, and then let the road traffic and the transport demand make a new balance. And This article introduces is the use of the green wave effect collaborative strategies adjacent green extension of fuzzy control in order to solve the problem of coupling between intersections road. This algorithm makes Signal Timing to be more flexible.

Fractional order fuzzy control of nuclear reactor power with thermal-hydraulic effects in the presence of random network induced delay and sensor noise having long range dependence

Nonlinear state space modeling of a nuclear reactor has been done for the purpose of controlling its global power in load following mode. The nonlinear state space model has been linearized at different percentage of reactor powers and a novel fractional order (FO) fuzzy proportional integral derivative (PID) controller is designed using real coded Genetic Algorithm (GA) to control the reactor power level at various operating conditions. The effectiveness of using the fuzzy FOPID controller over conventional fuzzy PID controllers has been shown with numerical simulations. The controllers tuned with the highest power models are shown to work well at other operating conditions as well; over the lowest power model based design and hence are robust with respect to the changes in nuclear reactor operating power levels. This paper also analyzes the degradation of nuclear reactor power signal due to network induced random delays in shared communication network and due to sensor noise while being fed-back to the Reactor Regulating System (RRS). The effect of long range dependence (LRD) which is a practical consideration for the stochastic processes like network induced delay and sensor noise has been tackled by optimum tuning of FO fuzzy PID controllers using GA, while also taking the operating point shift into consideration.

Tuning of a TS Fuzzy Output Regulator Using the Steepest Descent Approach and ANFIS

The exact output regulation problem for Takagi-Sugeno (TS) fuzzy models, designed from linear local subsystems, may have a solution if input matrices are the same for every local linear subsystem. Unfortunately, such a condition is difficult to accomplish in general. Therefore, in this work, an adaptive network-based fuzzy inference system (ANFIS) is integrated into the fuzzy controller in order to obtain the optimal fuzzy membership functions yielding adequate combination of the local regulators such that the output regulation error in steady-state is reduced, avoiding in this way the aforementioned condition. In comparison with the steepest descent method employed for tuning fuzzy controllers, ANFIS approximates the mappings between local regulators with membership functions which are not necessary known functions as Gaussian bell (gbell), sigmoidal, and triangular membership functions. Due to the structure of the fuzzy controller, Levenberg-Marquardt method is employed during the training of ANFIS.

Chaos suppression in a fractional order financial system using intelligent regrouping PSO based fractional fuzzy control policy in the presence of fractional Gaussian noise

Financial systems are known to have irregular and erratic fluctuations due to diverse influences and often result in economic crisis and huge financial losses. Recent models of financial systems show that they behave chaotically and have long range memory dependence. Mitigating these undesirable chaotic natures of financial systems by appropriate control policies is important in order to reduce investment risks and improve economic performance. In this paper, a fractional order fuzzy control policy is employed to suppress the chaotic dynamics of a representative chaotic fractional order financial system. An intelligent Regrouping Particle Swarm Optimization (Reg-PSO) is used to design the numeric weights of the control policy and the methodology is demonstrated by credible simulations. The designed fractional fuzzy control policies are shown to work well with respect to conventional fuzzy control policies in the presence of persistent and anti-persistent noise, which can be due to additional extraneous influences on the system.

Sensorless MPPT Fuzzy Controller for DFIG Wind Turbine

In this work, a maximum power point tracking method, is developed for the DFIG wind turbine system under study to control the rotor side converter by fuzzy controller in order to capture the maximum wind energy without measuring the wind velocity. Simulations results, are given and discussed with MATLAB/SIMULINK to validate the proposed control strategy for 10KW DFIG wind generator.

Design of Fractional Order Fuzzy Controller Based on Sliding Mode Control for Robotic Flexible Joint Manipulators

In this paper, a new design approach that combines the advantages in terms of robustness of the fractional control, the fuzzy scheme and the Sliding Mode Control (SMC) is proposed for robotic manipulators. A fractional order fuzzy sliding-mode controller (FOFSMC) can drive system tracking error to converge to zero in finite time. The FOFSMC is applied to a level control in robotic manipulators. Performance of the proposed controller evaluated to compare the performance with respect the conventional sliding mode controller. The simulation results demonstrate that the FOFSMC can provide a reasonable tracking performance.

Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following

Autonomous road following is one of the major goals in intelligent vehicle applications. The development of an autonomous road following embedded system for intelligent vehicles is the focus of this paper. A fuzzy logic controller (FLC) is designed for vision-based autonomous road following. The stability analysis of this control system is addressed. Lyapunov's direct method is utilized to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for designers to choose a suitable rule base for the fuzzy controller in order to make the system stable. Stability of the proposed fuzzy controller is guaranteed theoretically and also demonstrated by simulation studies and experiments. Simulations using the model of the four degree of freedom nonholonomic robotic vehicle are conducted to investigate the performance of the fuzzy controller. The proposed fuzzy controller can achieve the desired steering angle and make the robotic vehicle follow the road successfully. Experiments show that the developed intelligent vehicle is able to follow a mocked road autonomously.

FRACTIONAL FUZZY LOGIC PID CONTROLLER

An algorithm for designing a fuzzy controller for fractional systems is presented. The essential idea is to design a nonlinear PID controller using fuzzy reasoning logic at the same time fuzzying the fractional orders of both the integrator and the differentiator, to produce a fractional order fuzzy controller. The gains of the fractional order controller are automatically and continuously adjusted. The fractional orders of the integration and differentiation are obtained based on a designed rule base, so they will be changed according to the error and the change of the error. Numerical simulation results demonstrate the effectiveness of the proposed algorithm in handling linear and nonlinear fractional systems as well as chaotic systems.

Design and hybrid control of the pneumatic force-feedback systems for Arm-Exoskeleton by using on/off valve

This article models a pneumatic force-feedback system consisting of the double-acting cylinder and a set of high-speed on–off valves, and its fuzzy controller in order to provide an insight into pneumatic system design and force-feedback control requirements of the Arm-Exoskeleton, which is applied in robot-teleoperation and robotics. In modeling, effects of nonlinear flow through the valves, air compressibility in cylinder chambers, and time delay and attenuation of the pressure input in the connecting tubes are considered. Based on this mathematical model, the hybrid fuzzy control method for the precise force-feedback control is proposed and the fuzzy controllers are realized with the Mega8 MCUs as the units of the distributed control system in the Arm-Exoskeleton. At last a series of experiments validated the models and control method.

Application of Improved Fuzzy Controller in Networked Control System

Abstract Aiming at the problem of network-induced delay and data dropout in networked control system, an improved fuzzy controller is proposed in this paper. Considering the great influence of a controller on the performance of control system, an improved controller with a second order fuzzy controller and network-induced delay compensator being added to the basic fuzzy controller is proposed to realize self-regulation on-line. For this type of controller, neither plant model nor measurement of network delay is required. So it is capable of automatically adjusting quantified factor, proportional factor, and integral factor according to the control system error and its derivative. The design makes full use of the advantages of quickness in operation and reduction of steady state error because of its integral function. The controller has a good control effect on time-delay and can keep a better performance by self-regulation on-line in the network with data dropout and interference. It is good in quickness, adaptability, and robustness, which is favorable for controlling the long time-delay system.

NEURO-FUZZY CONTROL OF A PH PLANT

This paper studies the control of a pH process by using a neuro fuzzy controller with gain scheduling. As the process to be controlled is highly non-linear the PI-type fuzzy controller that will be used generally is not able to control the system adequately. For this, a very simple feedforward neural network trained on-line, is put at the output of the PI-type fuzzy controller in order to calculate the gain of the controller. This neuro-fuzzy regulator has been tested in real-time on a bench plant. On-line results show that the designed control system allows the plant to operate in a range of pH values, despite perturbations and variations of the plant parameters, obtaining good performance at the desired workings points.

Multiobjective wing design using genetic algorithms and fuzzy logic

The designer frequently faces problems in which the project depends on many parameters and the final solution must be evaluated according to several optimization objectives. This may be the case for the aeroelastic and aeromechanical design of lifting surfaces. The aim of the present paper is to show how the combined techniques of genetic algorithms (GAs) and fuzzy logic can be useful in these situations. The leading idea is the development of a tool to assist the designer in the preliminary phase of activity by assigning a genetic code to every possible solution and assessing its performance by means of a fuzzy controller in order to handle simultaneously different design criteria. Examples are given for some typical problems in aeronautical design.

Genetic algorithms optimized fuzzy controller for the indoor environmental management in buildings implemented using PLC and local operating networks

In this paper, an optimized fuzzy controller is presented for the control of the environmental parameters at the building zone level. The occupants’ preferences are monitored via a smart card unit. Genetic algorithm optimization techniques are applied to shift properly the membership functions of the fuzzy controller in order to satisfy the occupants’ preferences while minimizing energy consumption. The implementation of the system integrates a smart card unit, sensors, actuators, interfaces, a programmable logic controller (PLC), local operating network (LON) modules and devices, and a central PC which monitors the performance of the system. The communication of the PLC with the smart card unit is performed using an RS 485 port, while the PLC-PC communication is performed via the LON network. The integrated system is installed and tested in the building of the Laboratory of Electronics of the Technical University of Crete.

Self-generating rule-mapping fuzzy controller design using a genetic algorithm

A genetic algorithm (GA) based method is proposed to design a self-generating rule-mapping fuzzy controller. Its construction is based on the concept of a template rule base, as suggested by MacVicar & Whelan (see R.R. Yager et al., 1994). In the GA approach, an individual is constructed to represent a fuzzy controller. A short coded string is proposed such that, when associated with an individual, it can map a rule to a fuzzy controller structure, including the number of membership functions for each input variable, the shapes of the membership functions associated with each input variable and the index function. Then, a fitness function is proposed to guide the search procedure to select an appropriate fuzzy controller in order to satisfy the desired performance. Finally, the inverted pendulum control problem is utilised to illustrate the efficiency of the proposed method.

Electronic control of braking force distribution for vehicles using a Direct adaptive fuzzy controller

In brake systems, a proportioning valve(P. V), which reduces the brake line pressure on each wheel cylinder for the anti-locking of rear wheels, is closely related to the safety of vehicles. However, it is impossible for current P. V. s to completely control brake line pressure because, mechanically, it is an open loop control system. In this paper we describe an electronic brake force distribution system using a direct adaptive fuzzy controller in order to completely control brake line pressure using a closed loop control system. The objective of the electronic brake force distribution system is to change the cut-in-pressure and the valve slop of the P. V in order to obtain better performance of the brake system than with mechanical systems.

Higher order fuzzy control structure for higher order or time-delay systems

Attempts to develop new structures for higher order fuzzy logic control (FLC) systems, which could handle the higher order process and the time-delay system as well. Two different approaches are proposed to construct the higher order FLC, namely, the hierarchical structure and the distributed structure. Both structures can be designed via the theory of variable structure control (VSC). The complexity of the structure depends on the order of the process. The necessary and sufficient stability condition provided by the VSC theory facilitates the design of the nominal scaling gains. The difficulty of the design is to estimate the upper bound of the undesirable dynamics. Practically, this upper bound can be approximated by the peak value of the undesirable dynamics in the steady-state period. The simulation shows that this approximation is feasible and nominal scaling gains designed can lead to a stable and reasonably good performance. The fine tuning can be more easily carried out to provide a better performance from these nominal values instead of from scratch. The proposed higher order structure can also be used to control the time-delayed process if the delay time is known. The structure configuration depends on the approximation of the delay compensation factor. A first-order approximation is usually accurate enough when the delay is not very long. A partially known delay will result in either overcompensation or undercompensation. The overcompensation seems better than the undercompensation due to the former contribution to the PID effects. The simulation shows the effectiveness of the proposed structure to both the higher order process and the time-delay system.

Transport Carriage Speed Fuzzy Control

A non conventional method of a transport carriage speed control by means of a fuzzy logic controller is presented. The carriage speed control is based on the use of a fuzzy algorithm in order to obtain the best performance bounds. A new technique of fuzzy control in order to obtain a continuous control change from the finite look-up tables is introduced. The performance of the fuzzy logic controller is improved in spite of the transport carriage speed control saturation. To show that, some simulation results are illustrated.

Fuzzy position control of hydraulic robots with valve deadbands

The development of a real-time fuzzy-logic controller for a class of industrial hydraulic robots is described. The main element of the controller is a PD-type fuzzy control technique which utilizes a simple set of membership functions and rules to meet the basic control requirements of such robots. The controller, although effective, is shown to produce steady-state errors. The steady-state error in a step input response is mainly due to the hydraulic valve deadband in which the control action is not effective. The steady-state position error in a ramp input response is due to both valve deadband and the nature of the controller, since zero position and velocity errors would result in the controller closing the valve and stopping the motion. Conceptually simple actions are then identified to be applied in parallel with the above PD-type fuzzy controller in order to eliminate the steady-state errors. In particular, two methods are developed. In one method, a control term proportional to the integral of the error is calculated and added to the output from the main controller. In the second method, the control measure of the steady-state error obtained from the PD-type fuzzy controller is used as an offset. Both actions are activated only in the region of fuzzy zero velocity error. The performances of the techniques developed in this paper are evaluated experimentally. The experiments are performed on an instrumented Unimate MK-II industrial hydraulic robot which has been retrofitted as a research robot.