Genetic Fuzzy Logic Controller Research Articles

An Improved Genetic Fuzzy Logic Control Method to Reduce the Enlargement of Coal Floor Deformation in Shearer Memory Cutting Process

In order to reduce the enlargement of coal floor deformation and the manual adjustment frequency of rocker arms, an improved approach through integration of improved genetic algorithm and fuzzy logic control (GFLC) method is proposed. The enlargement of coal floor deformation is analyzed and a model is built. Then, the framework of proposed approach is built. Moreover, the constituents of GA such as tangent function roulette wheel selection (Tan-RWS) selection, uniform crossover, and nonuniform mutation are employed to enhance the performance of GFLC. Finally, two simulation examples and an industrial application example are carried out and the results indicate that the proposed method is feasible and efficient.

An Educational Tool for the Genetic Algorithm-Based Fuzzy Logic Controller of a Permanent Magnet Synchronous Motor Drive

In this study, a novel educational tool is introduced for the genetic algorithm (GA)-based fuzzy logic controller (FLC) of a permanent magnet synchronous motor (PMSM) drive. This tool provides the operating principle of PMSM, essentials of GAs, and effects of FLC parameters on the performance of GA-based FLC. The proposed educational tool regarding GA-based FLC is a unique one in its field in the literature, as such a computer-based study aiming to teach the corresponding intelligent system has not been studied before, though both GA and FLC are well-known powerful techniques. The graphical interface, developed in Microsoft Visual C#. NET platform, has a flexible and functional structure. By changing motor operating conditions as well as GA and controller parameters, useful practices can be done on the whole system under various situations without wasting time. An evaluation of the tool is given based on student feedback.

Genetic Algorithm Based Fuzzy Logic Controller for Wire-EDM Machine

Wire tension as well as wire feed should be controlled tightly for the geometry and corner accuracy of wire-EDM. A closed-loop wire tension control system for Micro-Wire-EDM is presented to guarantee a smooth wire transport and a constant tension value. To keep smooth wire transportation and avoid wire breakage during wire feeding, the reel roller is modified and the clip reel is removed from the wire transport mechanism. A genetic algorithm-based fuzzy logic controller (FLC) is proposed to investigate the dynamic performance of the closed-loop wire tension control system. Theoretical analysis and experimental research demonstrate that the developed wire transport system can result in satisfactory transient response, steady-state response and robustness. The proposed FLC can obtain faster transient response and smaller steady-state error than PI controller.

Genetic-fuzzy logic ramp metering control for ramp metering of motorway

This paper proposes a local responsive ramp metering algorithm based on genetic fuzzy logic control (GFLC). In this algorithm, the traffic conditions for an isolated on-ramp are responded by a knowledge-based system containing a set of membership functions and a set of fuzzy rules. In order to stabilise the traffic density at the critical density where traffic volumes reach the maximum throughput, genetic algorithm is employed to tune the parameterised membership functions. The performance of GFLC is measured by total travel time (TTT) in a simulation scenario constructed by a stochastic and microscopic simulator, Aimsun. The proposed algorithm is also compared to FLC ramp metering and no-ramp-control case to show the improvement in terms of the percentage changes of TTT. The simulation results have shown that the GFLC ramp metering provides significant improvement of TTT and better ability to maintain system flow density than the FLC ramp metering.

Genetic fuzzy logic traffic signal control with cell transmission modeling

This study develops an adaptive traffic signal control model based on an iterative genetic fuzzy logic controller (GFLC). The proposed model considers traffic flow and queue length as state variables and extension of green time as control variable, toward the minimization of total vehicle delays. For the learning efficiency of GFLC and the capability in capturing traffic behaviors, cell transmission model is used to replicate the traffic condition. To investigate, the performance of the proposed model in the case of an isolated intersection, comparisons to pretimed signal timing plans determining by Webster and total enumeration methods, and two queue-length based adaptive models are conducted. Results show that our proposed GFLC model performs best. As traffic flows vary more noticeably, the GFLC traffic signal control model performs even better than any timing plans. In the case of sequential intersections with four coordinated signal systems: simultaneous, progressive, alternate, and independent, the experimental example study also show that the proposed GFLC model can also perform better than current and pretimed timing plans, suggesting that the proposed GFLC signal control model is effective, robust, and adaptable.

Development of genetic algorithm-based fuzzy logic controller for conical tank process

The proportional integral derivative controllers are widely used in industries for controlling the different process variables due to its simplicity, flexibility and efficiency. Recently, the control of non-linear processes in the industries have turned the attention towards the intelligent controllers such as neural networks, fuzzy logic controller (FLC), genetic algorithm-(GA) tuned controllers, adaptive controller, predictive controller, robust controller, etc. This work focuses on developing a GA-based FLC for conical tank. A conical tank is a highly non-linear process due to the variation in the area of cross section of the level system with change in shape. Conventionally, a parameter adaptive proportional integral (PI) controller has been designed for non-linear process. Alternatively, in this work, an intelligent controller (GA-based FLC) is designed for the control of non-linear process to ensure the exact level control. The experimental results are obtained for servo and regulatory response of the process. The GA-based FLC is compared with adaptive PI controller.

Stepwise genetic fuzzy logic signal control under mixed traffic conditions

SUMMARYThis paper proposes a stepwise genetic fuzzy logic controller (SGFLC) by considering traffic flows and queue lengths of cars and motorcycles as state variables and extension of green time as control variable, towards the minimization of total vehicle delays. For the learning efficiency of SGFLC and the capability in capturing traffic behaviors of Asian urban streets, where mixed traffic of cars and motorcycles are prevailing, the mixed traffic cell transmission model (MCTM) is introduced to replicate traffic behaviors. To investigate the control performance of the proposed SGFLC model, comparisons with two pre‐timed timing plans and three adaptive signal timing models are conducted at an isolated intersection. Results show our proposed SGFLC model performs best. Moreover, as traffic flows vary more noticeably, the SGFLC model performs even better. In the experimental and field cases of three‐intersection arterial under four coordinated signal systems, namely simultaneous, progressive, alternate and independent, both cases consistently show that the proposed SGFLC model perform best, suggesting that the proposed SGFLC signal control model is efficient and robust. Copyright © 2012 John Wiley & Sons, Ltd.

Optimal variable speed-limit control under abnormal traffic conditions

Application of variable speed-limit (VSL) control has become promising in managing incidents in the context of intelligent highway systems, wherein advanced VSL signs are densely placed along the roadways. This paper aims to develop a genetic-fuzzy-logic- controller (GFLC)-based VSL control model with an objective function to enhance both safety and throughput efficiency at incident sites. In order to evaluate the performance of learned logic rules and tuned membership function, a revised cell transmission model is proposed, which is validated with field traffic data. The results indicate that the proposed VSL control model can effectively curtail crash likelihood while only slightly decreasing the throughput efficiency under different tested incident cases. It suggests that our proposed GFLC-based VSL control model is promising for practical applications.

ACTIVE VIBRATION CONTROL OF SEISMICALLY EXCITED STRUCTURES BY ATMDS: STABILITY AND PERFORMANCE ROBUSTNESS PERSPECTIVE

This paper demonstrates the trade-off between nominal performance and robustness in intelligent and conventional structural vibration control schemes; and, proposes a systematic treatment of stability robustness and performance robustness against uncertainty due to structural damage. The adopted control strategies include an intelligent genetic fuzzy logic controller (GFLC) and reduced-order observer-based (ROOB) controllers based on pole-placement and linear quadratic regulator (LQR) conventional schemes. These control strategies are applied to a seismically excited truss bridge structure through an active tuned mass damper (ATMD). Response of the bridge-ATMD control system to earthquake excitation records under nominal and uncertain conditions is analyzed via simulation tests. Based on these results, advantages of exploiting heuristic intelligence in seismic vibration control, as well as some complexities arising in realistic conventional control are highlighted. It has been shown that the coupled effect of spill-over (due to reduction and observation) and mismatch between the mathematical model and the actual plant (due to uncertainty and modeling errors) can destabilize the conventional closed-loop system even if each is alone tolerated. Accordingly, the GFLC proves itself to be the dominant design in terms of the compromise between performance and robustness.

A trajectory tracking genetic fuzzy logic controller for nuclear research reactors

In this study, a trajectory tracking genetic fuzzy logic controller was designed for research reactors. Membership functions and action weights of the fuzzy controller were optimally determined by genetic algorithms. The behavior of the controller was tested for various initial and desired power levels as well as under disturbance. It was seen that the controller could control the system successfully under all conditions within the acceptable error tolerance.

Development of genetic fuzzy logic controllers for complex production systems

Complex production systems can produce more than one part type. For these systems, production rate and priority of production for each part type is determined by production controllers. In this paper, genetic fuzzy logic control (GFLC) methodology is used to develop two production control architectures namely “genetic distributed fuzzy” (GDF), and “genetic supervisory fuzzy” (GSF) controllers. Previously these controllers have been applied to single-part-type production systems. In the new approach the GDF and GSF controllers are developed to control complex production systems. The methodology is illustrated and evaluated using two test cases; two-part-type production line and re-entrant production systems. Genetic algorithm is used to tune the membership functions of input variables of GSF or GDF controllers. The objective function of the GSF controller minimizes the production cost based on work-in-process (WIP) and backlog costs, while surplus minimization is considered by GDF controller. The results show that GDF and GSF controllers can improve the performance of production systems. GSF controllers decrease the WIP level and its variations. GDF controllers show their abilities in reducing the backlog level but generally, production cost for GDF controller is greater than GSF controller.

Genetic fuzzy modelling and control of bispectral index (BIS) for general intravenous anaesthesia

Based on an adaptive genetic fuzzy clustering algorithm, a derived fuzzy knowledge model is proposed for quantitatively estimating the systolic arterial pressure (SAP), heart rate (HR), and bispectral index (BIS) using 12 patients and it validates them according to pharmacological reasoning. Also, a genetic proportional integral derivative controller (GPIDC) to adaptive three controller parameters and a genetic fuzzy logic controller (GFLC) to adaptive controller rules using genetic algorithms (GAs) were simulated and compared each other in a patient model using the BIS value as a controlled variable. Each controller was tested using a set of 12 virtual patients undergoing a Gaussian random surgical disturbance repeated with BIS targets set at 40, 50, and 60. Controller performance was assessed using mean absolute error (MAE) of the BIS target, the percentage of time with acceptable BIS control (PTABC), and drug consumption (DC). It was found that the MAE value of the BIS target was significantly lower ( P < 0.05) and the values of PTABC and DC of BIS target were significantly higher ( P < 0.05) in BIS targets set at 40 than at 50 or 60 in both GPIDC and GFLC. However, when compared with two controllers in terms of the values of MAE, PTABC, and DC each other in BIS targets set at 40, 50, and 60, there were no significant differences ( P > 0.05). Furthermore, when the simulation results in these two controllers were compared with routine standard practice of 12 clinical trials (i.e., manual control) in BIS target set at 50, the values of PTABC in both GPIDC and GFLC groups were significantly higher ( P < 0.05) than in the manual control group. In contrast, there were no significant differences ( P > 0.05) for these three groups in terms of drug consumption. This indicates that either GPIDC or GFLC can control the BIS target set at 50 better than manual control, although the similar drug consumption is used.

COORDINATED TRANSIT-PREEMPTION SIGNAL ALONG AN ARTERIAL: ITERATIVE GENETIC FUZZY LOGIC CONTROLLER (GFLC) METHOD

This paper develops an iterative genetic fuzzy logic controller (GFLC) model to adaptively control the transit-preemption signal (TPS) along an arterial with consideration of the traffic flows from all approaches. Two transit-preemption strategies, green extension and red truncation, are incorporated with simultaneous, progressive, and alternative coordinated signal systems. Compared with the signal timing without TPS, the case study shows that the GFLC conditional TPS model can curtail the total person-delay by as high as 18.10% while the unconditional TPS model can only reduce 14.47% at most. This study also finds that the GFLC conditional TPS model performs better in light traffic than in heavy traffic.

Genetic fuzzy logic controller: an iterative evolution algorithm with new encoding method

Logic rules and membership functions are two key components of a fuzzy logic controller (FLC). If only one component is learned, the other one is often set subjectively thus can reduce the applicability of FLC. If both components are learned simultaneously, a very long chromosome is often needed thus may deteriorate the learning performance. To avoid these shortcomings, this paper employs genetic algorithms to learn both logic rules and membership functions sequentially. We propose a bi-level iterative evolution algorithm in selecting the logic rules and tuning the membership functions for a genetic fuzzy logic controller (GFLC). The upper level is to solve the composition of logic rules using the membership functions tuned by the lower level. The lower level is to determine the shape of membership functions using the logic rules learned from the upper level. We also propose a new encoding method for tuning the membership functions to overcome the problem of too many constraints. Our proposed GFLC model is compared with other similar GFLC, artificial neural network and fuzzy neural network models, which are trained and validated by the same examples with theoretical and field-observed car-following behaviors. The results reveal that our proposed GFLC has outperformed.