Hierarchical Fuzzy Logic Controller Research Articles

Fractional-order models identification and control within a supervisory control framework for efficient nutrients removal in biological wastewater treatment plants.

Wastewater treatment plants (WWTPs) are highly non-linear processes that must be optimized to meet rigorous environmental water regulations. In this context, efficiency and costs are equally important terms. The ASM3bioP framework is employed in this study to enable simultaneous nitrogen and phosphorus removal using an activated sludge process model with seven-reactor configurations. The activated sludge process is the most complicated and energy-intensive phase of a WWTP. To control dissolved oxygen in aerobic reactors and nitrate levels in anoxic reactors, two robust PI controllers - a classical PI and a non-integer (fractional)-order PI - with both integer-order and fractional-order models are designed. The controllers are created and simulated with the use of a mathematical model that has been developed based on the input data. The lower level fractional controller with a fractional-order model improves both the effluent quality (EQI) and operational cost (OCI) indices significantly. For such biological WWTP, a hierarchical fuzzy logic controller is designed to adjust the dissolved oxygen in the seventh reactor (DO7) to control ammonia. The implemented supervisory layer control strategy improves effluent quality EQI while increasing OCI marginally.

Developing hierarchical fuzzy logic controllers to improve the energy efficiency and cutting rate stabilization of natural stone block-cutting machines

In the natural stone sector, rock blocks are cut as a plate using block-cutting machines (BCM), and the plates are polished in the polishing machines. Energy consumption is the primary operating cost in both the cutting and polishing processes. The energy consumption of BCMs is affected by many factors, with the most influential being traverse speed. The energy efficiency of BCMs can be improved by regulating the traverse speed throughout the cutting process, and the roughness of the cut surface can be reduced by keeping the ratio between the traverse speed and peripheral speed constant as an additional benefit. This also reduces energy consumption and waste production in the polishing process. In this paper, two controllers used for the regulation of traverse and peripheral speeds are applied to a laboratory-scale BCM, one of which regulates only the traverse speed, while the other regulates both the traverse and peripheral speeds. A limped hierarchical fuzzy logic controller (LHFLC) approach is used in the design of the controllers for the first time on the BCM. In the proposed LHFLC method, the design procedure of the controller is simplified, and its memory usage, computational load, and response time are decreased. The performance of the controllers (a three-input/one-output (3I1O) FLC, 3I1O LHFLC, and a three-input/two-output (3I2O) LHFLC) was tested with cutting experiments performed on three rock samples (Bilecik Beige limestone, Uşak Green marble, and Afyon travertine), and the acquired results reveal that the proposed controllers (3I1O LHFLC and 3I2O LHFLC) are more efficient than the previous controller (3I1O FLC), with over 21%. A statistical analysis shows that to improve the energy efficiency of the BCM, the 3I1O LHFLC could be used rather than the 3I1O FLC. It is further noted that statistically, the cutting rate can be kept more constant with the 3I2O LHFLC than with the 3I1O LHFLC, meaning that the surface roughness of the plates produced by the BCM can be reduced using the 3I2O LHFLC.

Hierarchical Pitch Control for Small Wind Turbines Based on Fuzzy Logic and Anticipated Wind Speed Measurement

Bringing electricity to areas of difficult terrain is a complicated task, so it is convenient to generate power using local natural resources, such as wind, through a small horizontal-axis wind turbine (S-HAWT). However, at the rotor height of these wind turbines, the wind is often turbulent due to obstacles such as trees and buildings. For a turbine to function properly in these conditions, the action of the wind force on the rotor must be smoothed out by controlling the pitch angle. A commercial derivative-integral-proportional (PID)-type pitch controller works well when system dynamics are stable, but not when there are disturbances in the system. This paper proposes a hierarchical fuzzy logic controller (HFLC) to solve the nonlinear system effects produced by atypical winds. The methodology includes a statistical analysis of wind variability at the installation site, which determines the functions of belonging and its hierarchy. In addition, installing an anemometer in front of the turbine allows an advanced positioning of the blades in the presence of wind gusts. The algorithm was implemented in an S-HAWT, and a comparison was made to quantify the performance difference between the proposed control strategy and a conventional PID controller.

Domain specific concept ontologies and text summarization as hierarchical fuzzy logic ranking indicator on malay text corpus

<span>Ranking function is a predictive algorithm that is used to establish a simple ordering of documents according to its relevance. This step is critical because the results’ quality of a Domain Specific Information Retrieval (IR) such as Hadith Information Retrieval is fundamentally dependent of the ranking function. A Hierarchical Fuzzy Logic Controller of <em>Mamdani</em>-type Fuzzy Inference System has been built to define the ranking function, based on the Malay Information retrieval’s BM25 Model. The model examines three-inputs (Ontology BM25 Score, Fabrication Rate of Hadith and Shia Rate of Hadith) and four-output values of Final Ranking Score which consist of three triangular membership functions. The proposed system has outperformed the BM25 original score and the Vector Space Model (VM) on 16 queries, while the BM25 original score and Vector Space Model only yield better result in 9 and 2 queries respectively on the P@10, %no measures and MAP. P@10 represent the values of Precision at Rank 10 P@10), %no measures represent the percentage of queries with no relevant documents in the top ten retrieved and MAP represents Mean Average Precision of the queries. The results show the proposed system have capability to demote negative documents and move up the relevant documents in the ranking list and its capability to recall unseen document with the application of ontology in text retrieval. For the future works, the researcher would like to apply the usage of other Malay Semantic elements and another corpus for positive ranking indicator.</span>

Prediction of optimum sampling rates of air quality monitoring stations using hierarchical fuzzy logic control system

Air quality monitoring systems are being developed and optimized continuously through different methods, and emerging technologies in computational software and simulations have provided efficient and promising results. The process of air quality monitoring requires taking air samples, and data sampling can consume energy, produce a redundant traffic load on the communication channel, and memory space in monitoring station electronic control units. Researches showed that adaptive sampling rate can be a solution to save energy and memory space by turning on sampling module during sensing different events. Therefore, the purpose of this paper was to predict the optimum sampling rates of air quality monitoring stations without compromising data accuracy. For this aim, a hierarchical fuzzy logic control system (simulated in MATLAB®/Simulink) was proposed based on contextual conditions including meteorological data of the country and measured concentrations of air pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). The analysis of the results showed that the sampling rate had a positive correlation with the concentration of the pollutants and the permissible thresholds. Also, the meteorological factors had a lower influence in determining the sampling rate compared to the concentrations of air pollutants.

Cooperative Task Assignment and Trajectory Planning of Unmanned Systems Via HFLC and PSO

This paper investigates the problems of cooperative task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization (PSO) is proposed. Initially, teams of UAVs are moving in a pre-defined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the target’s degree of threat, degree of importance, and the separating distance between each team and each detected target. Based on the gathered information, the ground station assigns the teams to the targets. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Next, each team plans its own path by formulating the path planning problem as an optimization problem. The objective in this case is to minimize the time to reach their destination considering the UAVs dynamic constraints and collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve this optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.

Hierarchical Fuzzy Control Applied to Parallel Connected UPS Inverters Using Average Current Sharing Scheme

Parallel connection of UPS inverters to enhance power rating is a widely accepted practice. Inter-modular circulating currents appear when multiple inverter modules are connected in parallel to supply variable critical load. Interfacing of modules henceforth requires an intensive design, using proper control strategy. The potentiality of human intuitive Fuzzy Logic (FL) control with imprecise system model is well known and thus can be utilised in parallel-connected UPS systems. Conventional FL controller is computational intensive, especially with higher number of input variables. This paper proposes application of Hierarchical-Fuzzy Logic control for parallel connected Multi-modular inverters system for reduced computational burden on the processor for a given switching frequency. Simulated results in MATLAB environment and experimental verification using Texas TMS320F2812 DSP are included to demonstrate feasibility of the proposed control scheme.

Optimal trajectory of a mobile robot using hierarchical fuzzy logic controller

This paper presents the development of a complete navigation problem of an autonomous mobile robot. The problem for which the robot tries to reach the target is treated using a hierarchical fuzzy logic controller HFLC. The optimisation of the consequences of fuzzy rules is solved using genetic algorithms. A new approach based on HFLC is presented in this paper to avoid collisions with obstacles. In fact, HFLC consists of a number of low-dimensional standard fuzzy systems connected in a hierarchical fashion. Hence, it can provide a good candidate for solving high-dimensional problems. Simulation results show that the designed hierarchical fuzzy controller gives good trajectory of the robot from its current position to the target with obstacle avoidance.

Evaluation of an FPGA-based fuzzy logic control of feed-water for ABWR under automatic power regulating

This paper presents a new feed-water controller under the automatic power regulating system (APR) for an advanced boiling water reactor (ABWR). The new feed-water controller is designed by using a rule-based hierarchical fuzzy logic control algorithm and is implemented by using the modern field programmable gate array (FPGA) technology. The Lungmen nuclear power plant (LMNPP), an ABWR design, is chosen as a study case. The FPGA-based feed-water controller is integrated into the LMNPP's full-scope simulator with APR system for validation and performance evaluation. Under automatic power maneuvers, two trajectories in the power/flow map have been tested and compared with the origin design. The transient response and the steady state tracking capability were evaluated and showed satisfactory results. The results demonstrate that the FPGA-based hierarchical fuzzy logic controller is a practical approach for automatic power operations in advanced nuclear power plant applications.

Fuzzy Logic-Based Techniques for Modeling the Correlation between the Weld Bead Dimension and the Process Parameters in MIG Welding

Fuzzy logic-based techniques have been developed to model input-output relationships of metal inert gas (MIG) welding process. Both conventional and hierarchical fuzzy logic controllers (FLCs) of Mamdani type have been developed, and their performances are compared. The conventional FLC suffers from the curse of dimensionality for handling a large number of variables, and a hierarchical FLC was proposed earlier to tackle this problem. However, in that study, both the structure and knowledge base of the FLC were not optimized simultaneously, which has been attempted here. Simultaneous optimization of the structure and knowledge base is a difficult task, and to solve it, a genetic algorithm (GA) will have to deal with the strings having varied lengths. A new scheme has been proposed here to tackle the problem related to crossover of two parents with unequal lengths. It is interesting to observe that the conventional FLC yields the best accuracy in predictions, whereas the hierarchical FLC can be computationally faster than others but at the cost of accuracy. Moreover, there is no improvement of interpretability by introducing a hierarchical fuzzy system. Thus, there exists a trade-off between the accuracy obtained in predictions and computational complexity of various FLCs.

Autonomous Navigation of Forest Trails by an Industrial-Size Robot

<abstract><title><italic>Abstract. </italic></title> A commercially available tracked vehicle was modified to make it capable of autonomous operation in a forest environment. The vehicle, of 1364 kg mass (3000 lbs weight), was powered by a 22 kW (30 hp) diesel engine coupled to a hydraulic drive. For autonomous operation, the vehicle was equipped with shaft encoders on the drive wheels, a magnetic compass, three ultrasonic sensors, and a stereoscopic camera. Autonomous operation was enabled by hierarchical fuzzy logic controllers. Two controllers were considered. One controller used shaft encoder counts on the drive wheels, ultrasonic waveform echoes, the magnetic compass for heading, and training data to navigate a forest path. The other controller added stereoscopic vision and dispensed with the training data to reactively navigate the autonomous outdoor vehicle. The vehicle was equipped with GPS, but GPS was only used as an independent measure of vehicle performance in the autonomous mode. Tests using both controllers showed that the autonomous vehicle was able to reliably navigate several forest paths with lengths ranging from 229 to 430 m (750 to 1410 ft) without relying on GPS for navigation. These tests indicate that an industrial-size vehicle may be capable of performing useful forestry functions in an autonomous mode.

An Adaptive Genetic-Based Incremental Architecture for the On-Line Coordination of Embedded Agents

This paper proposes a novel embedded agent architecture that aims to coordinate a system of interacting embedded agents in real-world intelligent environments using a unique on-line multi-objective and multi-constraint genetic algorithm. The embedded agents can be complex ones such as mobile robots that would operate hierarchical fuzzy logic controllers or simple ones such as desk lamps that would bear threshold functions instead. The architecture would enable the agents to learn the users’ desires and act in real time without the users having to repeatedly configure the system. The multi-embedded-agent system can adapt on-line to handle sudden changes such as unreliable sensors and actuators as well as agents that break down or come into the system. Multifarious experiments were performed on implementations of the aforementioned architecture where the system was tested in different scenarios of varying circumstances, and most importantly on mobile robots and embedded agents in the iDorm—a smart room at the University of Essex.

Connection admission control of MPEG streams in ATM network using hierarchical fuzzy logic controller

Asynchronous transfer mode (ATM) operates in a connection-oriented mode; if sufficient resources are not available for transporting data over a network, the connection is refused. Connection admission control (CAC) deals with the question of whether or not an ATM switch can accept a new connection. This paper presents the design of a fuzzy logic controller (FLC) for CAC of Motion Picture Experts Group (MPEG) audio and video streams in an ATM network. Our approach is different from the plain use of sample input–output data for automating FLC design as frequently reported in the literature. One of the differences is that we do not use any sample data that happen to be available. Instead, we perform intensive simulations of the system, to generate sample data which are then processed to obtain unique entries for the selected combination of the values of the input variables. We compared the performance of our fuzzy-CAC to conventional CACs such as those based on average bandwidth, peak bandwidth and equivalent bandwidth and we found our scheme to produce better results.

Hierarchical fuzzy controllers for an astronomical telescope tracking

The paper presents an application of fuzzy logic controller (FLC) with hierarchically structured rule base for a two-link direct drive Celestron telescope. The hierarchical fuzzy logic controller (HFLC) is implemented, as nonlinear blocks used in a control system, for supervision of PID controller for position tracking of the telescope driven by electric motors. The HFLC is composed of two FLC stages connected in cascade. The input variables, for the first stage, of the HFLC are the position error and the rate of change in the position error. Then the output from the first stage and the position error integral are used as input variables for the second stage of the HFLC PID. The simulation results of the proposed HFLC PID, when the telescope is subjected to a unit step in reference position, are compared with the PID controller. The dynamic responses of the HFLC PID improve the rise time, damping factor and settling time compared with the PID controller. Also, the proposed techniques, hierarchical fuzzy PID controller, reduce the computation time due to reduction of rule base. The simulation results show the effectiveness of the proposed HFLC PID controller scheme as a promising technique.

AN ENHANCED PATIENT CONTROLLED ANALGESIA (EPCA) FOR THE EXTRACORPOREAL SHOCK WAVE LITHOTRIPSY (ESWL)

The conventional patient-controlled-analgesia (PCA) can be further improved in terms of less drug consumption and better pain control if advanced feedback control algorithm is applied. The purpose of this research was to develop an enhanced patient-controlled analgesia (EPCA) using a hierarchical fuzzy logic control system with two levels and to apply it to patients in an extracorporeal shock wave lithotripsy (ESWL) operation. The two-level control system utilizes the conventional PCA as a basic level on the top of which is added an adaptive self-learning fuzzy logic control (SLFLC) level. Twenty-three patients involved in these clinical trials were to study the clinical effectiveness of EPCA for the management or pain during the ESWL process. A two-button device was designed, one button for severe pain (SP) and another for mild/moderate pain (MP) to investigate the frequencies of analgesic demands or delivery related to either severe or mild/moderate pain. The results show that the average percentage of demand/delivery to total demand/delivery due to MP is significantly higher than that due to SP, which implies the pain levels that most patients experienced were mostly mild to moderate in the ESWL process with the EPCA. The drug consumption of alfentanil with the EPCA is comparable to that with a fixed FLC; however, it is much lower than that with conventional PCA. In conclusion, the efficiency of the therapy, pain relief, and the patients' satisfaction of pain management with the EPCA in the ESWL operation are superior to those with the conventional PCA.

Designing a hierarchical fuzzy logic controller using the differential evolution approach

In conventional fuzzy logic controllers, the computational complexity increases with the dimensions of the system variables; the number of rules increases exponentially as the number of system variables increases. Hierarchical fuzzy logic controllers (HFLC) have been introduced to reduce the number of rules to a linear function of system variables. However, the use of hierarchical fuzzy logic controllers raises new issues in the automatic design of controllers, namely the coordination of outputs of sub-controllers at lower levels of the hierarchy. In this paper, a method is described for the automatic design of an HFLC using an evolutionary algorithm called differential evolution (DE). The aim in this paper is to develop a sufficiently versatile method that can be applied to the design of any HFLC architecture. The feasibility of the method is demonstrated by developing a two-stage HFLC for controlling a cart-pole with four state variables. The merits of the method are automatic generation of the HFLC and simplicity as the number of parameters used for encoding the problem are greatly reduced as compared to conventional methods.

AN AUTONOMOUS FOREST ROBOT THAT USES A HIERARCHICAL, FUZZY LOGIC CONTROLLER

This article presents a hierarchical fuzzy logic controller that arbitrates recommendations from lower-levelmodules in order to autonomously guide a robot down forest trails. This robot would be used to move logs or trees from wherethey are cut to a location where they could be further processed or loaded onto trucks. The lower-level modules contributerecommendations derived from lower-level tasks or behaviors. Each lower-level module provides its own recommendationbased on its limited perception of the environment. A fuzzy quality measure is used by the hierarchical controller to determinehow best to fuse the individual control recommendations of lower-level modules. The quality measure provides an approximatedetermination of the value of each lower-level module recommendation. These quality measures, as well as other fuzzy setsused in the controller, were defined with a set of parameters. A computer simulation was used to tune subsets of the fuzzy setparameters to improve the performance of the controller. After these parameters were tuned, they were used in the fuzzy logiccontroller, which was tested in a vehicle. The results of these vehicle tests are also discussed in the article.

A computer screen-based simulator for hierarchical fuzzy logic monitoring and control of depth of anaesthesia

A computer screen-based simulator has been developed to simulate the administration of intravenous and analgesic drugs. It merges on-line measurements (such as systolic arterial pressure (SAP) and heart rate (HR)) and nonnumerical clinical signs (such as sweating, lacrimation and pupil response), using anaesthetists' experience or self-organizing fuzzy logic control (SOFLC) algorithms to administer drugs into a patient. The hierarchical control architecture includes five parts, which are: monitoring depth of anaesthesia (DOA), drug controller, deciding the sensitivity of the patient, narcotic supplementation and recovery time. It has been developed to predict drug profiles, control the drug levels and assess recovery time during anaesthesia. Meanwhile, linguistic rules and fuzzy set theory have been used to model a patient's SAP and HR during induction and maintenance stages. Successful simulation results have given confidence to perform on-line clinical trials in operating theatre.

Hierarchical fuzzy logic controller for a flexible link robot arm performing constrained motion tasks

An examination is performed on the dynamics and control issues for a robotic manipulator with link structural flexibility modelled during the execution of a task that requires the robot tip to contact fixed rigid objects. A multi-time-scale fuzzy logic controller is applied to this system. The large-scale system is decomposed into a finite number of reduced-order subsystems using the singular perturbation approach. A hierarchical ordering of fuzzy rules is used to reduce the size of the inference engine. Real-time implementation of fuzzy controllers can help reduce the burden of large-sized rule sets by fusing sensory data before input and the system&apos;s output to the inference engine. Using this approach the control of the force and position of the robot end point is possible while the end-effector moves on the constraint surface.

Two-time scale fuzzy logic controller of flexible link robot arm

A flexible link arm is a distributed parameter system of infinite order, but due to onboard computer limitations, sensor inaccuracy, and system noise, it must be approximated by a lower-order model and controlled by a finite-order controller. The main object of this paper is concentrated on the hierarchical fuzzy logic by the singular perturbation approach for flexible-link robot arm control. A composite control design is adopted. Therefore, a two-time scale fuzzy logic controller will be applied for such system. In this paper, the fast-subsystem controller will be damp out the vibration of the flexible structure by two hierarchical fuzzy logic controllers. Moreover, the other slow-subsystem fuzzy controller dominates the trajectory tracking. We guarantee the stability of the internal dynamics by adding a boundary-layer correction based on singular perturbations. In addition, various case studies are given in illustration to verify the control algorithm. It appears that the fuzzy control method is quite useful as regards reliability and robustness.