Self-organizing Fuzzy Logic Controller Research Articles

An Optimized Type-2 Self-Organizing Fuzzy Logic Controller Applied in Anesthesia for Propofol Dosing to Regulate BIS

During general anesthesia, anesthesiologists who provide anesthetic dosage traditionally play a fundamental role to regulate bispectral index (BIS). However, in this article, an optimized type-2 self-organizing fuzzy logic controller (SOFLC) is designed for a target controlled infusion pump related to propofol dosing guided by BIS, to realize automatic control of general anesthesia. The type-2 SOFLC combines a type-2 fuzzy logic controller with a self-organizing mechanism to facilitate online training while able to contend with operational uncertainties. A novel data-driven surrogate model and genetic programming-based strategy is introduced for optimizing the type-2 SOFLC parameters offline to handle interpatient variability. A pharmacological model is built for simulation in which different optimization strategies are tested and compared. Simulation results are presented to demonstrate the applicability of our approach and show that the proposed optimization strategy can achieve better control performance in terms of steady-state error and robustness.

Type-2 fuzzy sets applied to multivariable self-organizing fuzzy logic controllers for regulating anesthesia

In this paper, novel interval and general type-2 self-organizing fuzzy logic controllers (SOFLCs) are proposed for the automatic control of anesthesia during surgical procedures. The type-2 SOFLC is a hierarchical adaptive fuzzy controller able to generate and modify its rule-base in response to the controller's performance. The type-2 SOFLC uses type-2 fuzzy sets derived from real surgical data capturing patient variability in monitored physiological parameters during anesthetic sedation, which are used to define the footprint of uncertainty (FOU) of the type-2 fuzzy sets. Experimental simulations were carried out to evaluate the performance of the type-2 SOFLCs in their ability to control anesthetic delivery rates for maintaining desired physiological set points for anesthesia (muscle relaxation and blood pressure) under signal and patient noise. Results show that the type-2 SOFLCs can perform well and outperform previous type-1 SOFLC and comparative approaches for anesthesia control producing lower performance errors while using better defined rules in regulating anesthesia set points while handling the control uncertainties. The results are further supported by statistical analysis which also show that zSlices general type-2 SOFLCs are able to outperform interval type-2 SOFLC in terms of their steady state performance.

Control of Electric Motor Machine Tools using Self-Organizing Fuzzy Logic Controller

Control of Electric Motor Machine Tools using Self-Organizing Fuzzy Logic Controller

MOOS-IvP를 이용한 무인잠수정 제어기 개발의 효용성

This paper demonstrates the benefit of using MOOS-IvP in the development of control system for Unmanned Underwater Vehicles(UUV). The demand for autonomy in UUVs has significantly increased due to the complexity in missions to be performed. Furthermore, the increased number of sensors and actuators that are interconnected through a network has introduced a need for a middleware platform for UUVs. In this context, MOOS-IvP, which is an open source software architecture, has been developed by several researchers from MIT, Oxford University, and NUWC. The MOOS software is a communication middleware based on the publish-subscribe architecture allowing each application to communicate through a MOOS database. The IvP Helm, which is one of the MOOS modules, publishes vehicle commands using multi-objective optimization in order to implement autonomous decision making. This paper explores the benefit of MOOS-IvP in the development of control software for UUVs by using a case study with an auto depth control system based on self-organizing fuzzy logic control. The simulation results show that the design and verification of UUV control software based on MOOS-IvP can be carried out quickly and efficiently thanks to the reuse of source codes, modular-based architecture, and the high level of scalability.

Self-organizing fuzzy optimal control for under-actuated systems

This study combines self-organizing fuzzy logic control technology with optimal control theory and presents a self-organizing fuzzy optimal controller for under-actuated systems. Instead of calibrating the control input directly, the self-organizing fuzzy optimal controller employs the self-organizing fuzzy system as a superior regulator to adjust the weighting matrix of the cost function for the optimal controller. Through this operation and the hierarchical control architecture design, self-organizing fuzzy optimal controller adaptively regulates the control strategy and manipulates the optimal control procedure according to the system dynamic behavior in real time. The optimal control law corresponding to the proposed regulator is then derived from the maximum principle for optimal control theory. The stability of the self-organizing fuzzy optimal controller is analyzed and proved through the Lyapunov stability theorem. Simulations on an under-actuated gantry crane system and a ball-and-beam system have verified that the self-organizing fuzzy optimal controller provides superior control performances as compared with the traditional linear quadratic regulating controller.

Performance analysis of extracted rule-base multivariable type-2 self-organizing fuzzy logic controller applied to anesthesia.

We compare type-1 and type-2 self-organizing fuzzy logic controller (SOFLC) using expert initialized and pretrained extracted rule-bases applied to automatic control of anaesthesia during surgery. We perform experimental simulations using a nonfixed patient model and signal noise to account for environmental and patient drug interaction uncertainties. The simulations evaluate the performance of the SOFLCs in their ability to control anesthetic delivery rates for maintaining desired physiological set points for muscle relaxation and blood pressure during a multistage surgical procedure. The performances of the SOFLCs are evaluated by measuring the steady state errors and control stabilities which indicate the accuracy and precision of control task. Two sets of comparisons based on using expert derived and extracted rule-bases are implemented as Wilcoxon signed-rank tests. Results indicate that type-2 SOFLCs outperform type-1 SOFLC while handling the various sources of uncertainties. SOFLCs using the extracted rules are also shown to outperform those using expert derived rules in terms of improved control stability.

A Study of Novel Hybrid Antilock Braking System Employing Magnetorheological Brake

A novel hybrid antilock braking system (ABS) with the combination of auxiliary brake and a multipole magnetorheological (MR) brake was proposed in this paper. The MR brake with innovative operation concept can replace existed hydraulic brake system or works as an auxiliary brake. Two simulation models of the MR brakes, inner rotor and outer rotor structures, have been built. The outer rotor design was chosen due to its better braking performance and suitable mechanism for using on motorcycle. After that, motorcycle simulation software was employed to validate the hybrid ABS system under appropriated working condition. Two controllers, the ordinary and self-organizing fuzzy logic controllers (FLC and SOFLC), were evaluated on ABS performance to pick the suitable one. Simulation results confirm the more adaptations to different road conditions of the SOFLC with 18% higher brake performance compared to ones of ordinary FLC. Brake performance can increase 12% more with the combination of SOFLC and road condition estimator (RCE). It is concluded that this hybrid ABS is feasible for actual application by effectively improving the brake performance for ensuring driving stability.

Innovative Strategy to Improve Precision and to Save Power of a Real-Time Control Process Using an Online Adaptive Fuzzy Logic Controller

The main objective of this paper is to prove the great advantage that brings our novel approach to the intelligent control area. A set of various types of intelligent controllers have been designed to control the temperature of a room in a real-time control process in order to compare the obtained results with each other. Through a training board that allows us to control the temperature, all the used algorithms should present their best performances in this control process; therefore, our self-organized and online adaptive fuzzy logic controller (FLC) will be required to present great improvements in the control task and a real high control performance. Simulation results can show clearly that the new approach presented and tested in this work is very efficient. Thus, our adaptive and self-organizing FLC presents the best accuracy compared with the remaining used controllers, and, besides that, it can guarantee an important reduction of the power consumption during the control process.

Effectiveness of a fuzzy controller on the damage index of nonlinear benchmark buildings

In this paper, a semi-active control method for seismically excited nonlinear benchmark buildings equipped with controllable fluid viscous dampers is presented and evaluated. A Fuzzy Logic Controller (FLC) is designed to alter the damping coefficient of controllable viscous dampers during an earthquake. A Genetic Algorithm (GA) is employed to find optimum fuzzy controller rules, as well as adjusting the membership functions to minimize the overall damage index. This combination of a Genetic algorithm and a Fuzzy Logic Controller (GFLC) is also used to minimize the peak value of the top story displacement response, in order to verify the effectiveness of minimizing displacement on the damage index. The effectiveness of the FLC is verified and illustrated using the simulated response of 3-story and 20-story full-scale nonlinear benchmark buildings excited by several historical earthquake records, through a computer simulation on MATLAB. For comparison, a Linear Quadratic Gaussian (LQG) active controller is designed to minimize the peak value of the top story displacement response. Moreover, the results of a study that has used Self-Organizing Fuzzy Logic Controllers (SOFLC) with active actuators are used. The results demonstrate that the GFLCs are quite effective in overall damage reduction for a wide range of motions, compared with the SOFLC and LQG controllers.

Multivariable self-organizing fuzzy logic control using dynamic performance index and linguistic compensators

As far as fuzzy logic based multivariable control systems are concerned, it is not always an easy task to express control strategies in the form of related multi-situations to multi-actions control rules. Decoupled control is one possible and attractive strategy to simplify this problem. However, the control performance of the decoupled controller relies greatly on ‘a prior’ knowledge of the system dynamics to build suitable compensators. This paper aims at introducing a new model-independent decoupled control architecture with the ability of on-line learning, which ensures a fast tracking performance. In this architecture, the dominating controller is developed using a new model-free Self-Organizing Fuzzy Logic Control (SOFLC) architecture whereby the Performance Index table is ‘dynamic’, of a free structure, and starting from no knowledge. Furthermore, a switching mode scheme, with a compensating action triggered by the interaction between the channels, is proposed to improve the tracking performance of the closed-loop system. A series of simulations are carried out on a two-input and two-output biomedical process, with the conclusion that the proposed control mechanism has the ability to deal with varying system dynamics and noise and is tolerant to the choice of the compensator gains effectively.

A Hierarchical Self-Organising Fuzzy Logic-Based On-Line Advisor for the Management of Cardiac Septic Patients

AbstractAn on-line intelligent advisory system (IAS) for the management of septic patients emerging from cardiopulmonary bypass (CPB) has been developed and evaluated on a comprehensive physiological model which incorporates sepsis-induced capillary leak and the hemodynamic response to Intensive Care Unit (ICU)-like therapeutic interventions. The IAS is composed of the expert's decision-making model which performs diagnosis and designates the appropriate therapeutic actions based on the patient's hemodynamic status and a multi-inputs multi-outputs self-organising fuzzy logic controller (SOFLC) for adjusting the infusion rates of the selected drugs to maintain the hemodynamics variables at the specified target values. Simulated patient scenarios reproducing post-CPB hemodynamic abnormalities were developed with the expert anesthetist to test the IAS ability to advice for the appropriate therapeutic interventions whilst controlling individual drugs infusion rates to maintain the hemodynamic parameters at the prescribed targets values.

A model-free self-organizing fuzzy logic control system using a dynamic performance index table

The ‘raison d’être’ of self-organizing fuzzy logic control (SOFLC) algorithms is the performance index table, which normally issues the adequate corrections to the low-level control given certain performance criteria. In the standard SOFLC architecture, the performance index table is generic, fixed a priori and is of a ‘grid-partition’ structure making the whole scheme inefficient in terms of computational complexity and performance. In this paper, we propose a new model-free SOFLC architecture whereby the performance index table is ‘dynamic’, of free structure and starting from an empty table. The new proposed architecture includes the required mechanisms to ensure self-organization such as those associated with the individual evaluation via an online genetic algorithm (GA) and the fine-tuning of individual regeneration range for micro-GAs, to optimize the rules of the performance index table and enhance the system’s performance. Results of experiments on a non-linear muscle relaxation process show that the proposed control scheme is superior to the standard SOFLC algorithm in terms of performance and robustness against the system input/output scaling factors selection and parameter variations.

Comparison of the Applicability of Rule-Based and Self-Organizing Fuzzy Logic Controllers for Sedation Control of Intracranial Pressure Pattern in a Neurosurgical Intensive Care Unit

This paper assesses the controller performance of a self-organizing fuzzy logic controller (SOFLC) in comparison with a routine clinical rule-base controller (RBC) for sedation control of intracranial pressure (ICP) pattern. Eleven patients with severe head injury undergoing different neurosurgeries in a neurosurgical intensive care unit (NICU) were divided into two groups. In all cases the sedation control periods lasted 1 h and assessments of propofol infusion rates were made at a frequency of once per 30 s. In the control group of 10 cases selected from 5 patients, a RBC was used, and in the experimental group of 10 cases selected from 6 patients, a self-organizing fuzzy logic controller was used. A SOFLC was derived from a fuzzy logic controller and allowed to generate new rules via self-learning beyond the initial fuzzy rule-base obtained from experts (i.e., neurosurgeons). The performance of the controllers was analyzed using the ICP pattern of sedation for 1 h of control. The results show that a SOFLC can provide a more stable ICP pattern by administering more propofol and changing the rate of delivery more often when rule-base modifications have been considered.

Fuzzy logic control for intracranial pressure via continuous propofol sedation in a neurosurgical intensive care unit

The major goal of this paper is to provide automatically continuous propofol sedation for patients with severe head injury, unconsciousness, and mechanical ventilation in order to reduce the effect of agitation on intracranial pressure (ICP) using fuzzy logic control in a neurosurgical intensive care unit (NICU). Seventeen patients were divided into three groups in which control was provided with three different controllers. Experimental control periods were of 60 min duration in all cases. Group A used a conventional rule-based controller (RBC), Group B a fuzzy logic controller (FLC), and Group C a self-organizing fuzzy logic controller (SOFLC). The performance of the controllers was analyzed by ICP pattern of sedation. The ICP pattern of errors was analyzed for mean and root mean square deviation (RMSD) for the entire duration of control (i.e., 1 h). The results indicate that FLC can easily mimic the rule-base of human experts (i.e., neurosurgeons) to achieve stable sedation similar to the RBC group. Furthermore, the results also show that a SOFLC can provide more stable sedation of ICP pattern because it can modify the fuzzy rule-base to compensate for inter-patient variations.

A NEW EFFICIENT SELF-ORGANISING FUZZY LOGIC CONTROL (SOFLC) ALGORITHM USING A DYNAMIC PERFORMANCE INDEX TABLE

The “raison d'etre” of Self-Organising Fuzzy Logic Control (SOFLC) algorithms is the Performance Index table which normally issues the adequate corrections to the low-level control given certain performance criteria. In the standard SOFLC architecture, the Performance Index table is generic, fixed a priori, and is of a ‘grid-partition’ structure making the whole scheme inefficient in terms of computational complexity and performance. In this paper we propose a new SOFLC architecture whereby the Performance Index table is ‘dynamic', of a free structure, and starting from an empty table. The architecture includes 3 mechanisms for optimising the rules of the Performance Index table and enhancing the performance as well as the robustness of the algorithm in terms of disturbance rejection and noise. Results of experiments on a nonlinear muscle relaxation process showed that the proposed control scheme was superior to the standard SOFLC algorithm in terms of performance and robustness against parameter variations, stochastic activity, and sensitivity to the selection of scaling factors.

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.

Self-organizing fuzzy learning CLOS guidance law design

A new self-organizing fuzzy logic control (SOFLC) design method is proposed. The proposed method is applied to the command line-of-sight (CLOS) guidance law design. The SOFLC contains two sets of fuzzy inference logic. One is the fuzzy logic controller and the other is the rule modifier. The new learning method of the rule modifier is developed based on a fuzzy learning algorithm. The modification value of each rule is based on the fuzzy firing weight, so that learning of the rule bases is reasonable. Finally, two engagement scenarios are examined, and a comparison between a fuzzy logic control (FLC), an optimal learning FLC, and the proposed SOFLC CLOS guidance laws is made. Simulation results show that the proposed SOFLC guidance law can achieve better guidance performance than the other guidance laws.

Online elicitation of mamdani-type fuzzy rules via TSK-based generalized predictive control

Many synergies have been proposed between soft-computing techniques, such as neural networks (NNs), fuzzy logic (FL), and genetic algorithms (GAs), which have shown that such hybrid structures can work well and also add more robustness to the control system design. In this paper, a new control architecture is proposed whereby the on-line generated fuzzy rules relating to the self-organizing fuzzy logic controller (SOFLC) are obtained via integration with the popular generalized predictive control (GPC) algorithm using a Takagi-Sugeno-Kang (TSK)-based controlled autoregressive integrated moving average (CARIMA) model structure. In this approach, GPC replaces the performance index (PI) table which, as an incremental model, is traditionally used to discover, amend, and delete the rules. Because the GPC sequence is computed using predicted future outputs, the new hybrid approach rewards the time-delay very well. The new generic approach, named generalized predictive self-organizing fuzzy logic control (GPSOFLC), is simulated on a well-known nonlinear chemical process, the distillation column, and is shown to produce an effective fuzzy rule-base in both qualitative (minimum number of generated rules) and quantitative (good rules) terms.

Development of a model and simulation of a water hydraulic manipulator joint

Increasingly, governments are passing stringent laws on the use of environmentally damaging substances, and the success of alternative fluids has been short lived as the limitations of each has become evident which will ultimately result in their redundancy. History has shown that water is a more than viable fluid for use in hydraulic machinery, taking the place of oil and competing favourably with other power media. This paper details the development of a MATLAB-based computer simulation of the dynamic operation of a water hydraulic actuation system used in the development of a reduced rule self-organizing fuzzy logic control system.

A boiler-turbine system control using a fuzzy auto-regressive moving average (farma) model

This paper presents an application of an online self-organizing fuzzy logic controller to a boiler-turbine system of a fossil power plant. The control rules and the membership functions of the proposed fuzzy logic controller are generated automatically without using a plant model. A boiler-turbine system is described as a multi-input multioutput (MIMO) nonlinear system in this paper. Then, three single-loop fuzzy logic controllers are designed independently. Simulation shows robust results for various kinds of electric load demand changes and parameter variations of boiler-turbine system.