Monitoring and Control of Anesthesia Using Multivariable Self-Organizing Fuzzy Logic Structure

This paper proposes a design approach for a self-organizing self-tuning fuzzy logic controller, and is applied to the design of a field oriented drive system. The basic structure of a fuzzy logic controller is outlined and the design problems associated with the conventional trial-and-error schemes are addressed. The suitability of the genetic algorithm optimization technique as a means to determine and optimize the fuzzy logic controller design is discussed. In the proposed approach normalization factors and/or membership function parameters and/or the controller policy, are translated into bit-strings. These bit-strings are processed by the genetic algorithm and if the selection process as well as the objective function are chosen properly, a near-optimal solution can be found. To examine the efficiency of the proposed approach, a self-tuning and self-organizing fuzzy logic controller for an indirect field oriented induction motor drive is designed in both a sequential and a concurrent manner. A particular objective function (i.e., a performance index) is chosen to achieve a high dynamic performance. The simulation results demonstrate a significant enhancement in shortening the development time, and improving system performance over a manually tuned fuzzy logic controller.

This paper deals with the real time implementation of Fuzzy logic controllers on a liquid level control system. By applying fuzzy logic controller based on two different rule sets, it is shown that the selection of proper rules is critical for acheiving good system performance. The ability of the Self-organising fuzzy logic controller(SOFLC) to learn and modify a crude set of rules to acheive a good performance is demonstrated. The SOFLC algorithm used in this work needs less computation time and hence suitable for real time control applications.

This paper includes the design and implementation of a fuzzy logic controller for level control system to overcome the limitations of the conventional controllers. Mostly preferred controller is a Proportional Integral Derivative (PID) controller which has fixed control parameters. Selection of wrong parameters in a PID design results into uncontrollable process. But fuzzy logic controller is a rule based. These rules are designed on the basis of heuristic information provided by an operator. Process model is identified and used for the design of Proportional Integral (PI) and fuzzy controllers by using MATLAB. To evaluate the performance of both the controllers, it is tested on a real time system with the help of Real Time Workspace (RTW) module of a MATLAB. From real time results it is understood that, fuzzy controller provides more stable and improved output for level control. Even fuzzy controller has ability to remove overshoot which is present in PI controller. Also it requires less settling time than PI controller. Hence fuzzy control system is more relevant control strategy to control the level.

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

A compartmental model is modified to simulate the capillary leak associated with sepsis and cardiopulmonary bypass (CPB). Sepsis refers to a systemic inflammatory response to bacterial infection. This involves continuously updating the oncotic pressures with regard to the flows in and out of each compartment. As a result, it is possible to model the effects of administering fluids with different oncotic pressures on filtration. The model allows for simulated infusions of vasoactive and inotropic drugs with a range of sensitivities to these drugs, giving a comprehensive platform for testing various control strategies. Furthermore, self-organising fuzzy logic control (SOFLC), drug sensitivity estimation, linear decoupling and noise filtering were used to control this system in a multivariable control scheme.

This paper presents the design, implementation and analysis of a Pi-type fuzzy logic controller (PIFLC). The proposed controller was designed using a very simple control rule-base, with the error and change of error as inputs. The purpose of this controller was to provide stability when sudden changes happened in the reference air flow of an industrial fan system. The proposed scheme was tested and implemented in a PLC (Programmable Logic Controller). This scheme was also simulated in Matlab®. Performance comparison between the conventional Pi-type and the PIFLC was carried out in terms of the responses due to sudden changes in air flow reference. An analysis from the point of view of energy efficiency was also performed. Results show the powerful industrial application of this control method which searches for intelligent and dedicated automation. The PIFLC performs very well in this case.

Helicopters have generated considerable interest in both the control community due to their complex dynamics, and in military community because of their advantages over regular aerial vehicles. In this paper, we present the modeling and control of a four rotor vertical take-off and landing (VTOL) unmanned air vehicle known as quadrotor aircraft. This model has been generated using Newton-Euler equations. In order to control the helicopter, classical PD (proportional derivative) and Hybrid Fuzzy PD controllers have been designed. Although fuzzy control of various dynamical systems has been presented in literature, application of this technology to quadrotor helicopter control is quite new. A quadrotor helicopter has nonlinear characteristics where classical control methods are not adequate especially when there are time delays, disturbances and nonlinear vehicle dynamics. On the other hand, Fuzzy control is nonlinear and it is thus suitable for nonlinear system control. Matlab Simulink has been used to test, analyze and compare the performance of the controllers in simulations. For the evaluation of the autonomous flight controllers, some experiments were also performed. For this purpose, an experimental test stand has been designed and manufactured. This study showed that although, both of the classical PD and the Fuzzy PD controllers can control the system properly, the Fuzzy PD controllers performed slightly better than the classical PD controllers, and have benefits such as better disturbance rejection, ease of building the controllers.

This paper describes the design and implementation of simple fuzzy logic controller (SFLC) for a nonlinear quasi-resonant converter (QRC). It is shown that the two-dimensional rule table of conventional FLCs (CFLCs) used in power converters can be reduced to one-dimensional rule table using SFLC. The proposed SFLC reduces the memory space and computing time of the fuzzy algorithm. The tuning of scaling factors and the generation of fuzzy rules are also easier when compared with CFLC. As a result, realizing SFLC in a Microcontroller or Digital Signal Processor (DSP) is made simple. The proposed controller structure is universal and it can be directly applied to any of the dc–dc converter topologies. In this paper, the application of SFLC is successfully verified for the voltage control of Extended Period (EP)-ZCS/ZVS-QRC through simulation and experimental studies using TMS320F2407A Processor. The ability of SFLC, compared to CFLC, to achieve good transient response for load variations and supply disturbances is demonstrated.

Genetic programming (GP) is applied to the design of fuzzy logic controllers (FLCs) for mobile robot path tracking. GP is applied to automatic discovery of full knowledge bases for use in fuzzy logic control applications. An extension to a rule learning GP system is presented that achieves this objective. In addition, GP is employed to handle selection of fuzzy set intersection operators (t-norms). The new GP system is applied to design a mobile robot path tracking controller and performance is shown to be comparable to that of a manually designed controller. GP was successfully applied to discover FLCs capable of steering a mobile robot to track straight-line paths in the plane. Instances of simultaneous evolution of membership functions and rules showed that GP was capable of evolving a FLC that demonstrated satisfactory responsiveness to various initial conditions while utilizing minimal human interface.

A power system stabilization technique with an on-line self-organizing fuzzy logic controller is developed to enhance the damping of low frequency oscillation in a power system. The frequency deviation of a synchronous machine is utilized to calculate the stabilizing signal. Control rules and membership functions for the fuzzy logic controller are generated automatically without using any plant model. The generated rules are stored in the fuzzy rule space and updated on-line by a self-organizing procedure. To show the effectiveness of the proposed controller, the comparison with a conventional controller is made for a single-machine infinite-bus system.

A new design method of on-line learning self-organizing fuzzy logic controller is proposed. The proposed model reference self-organizing fuzzy logic control system contains two sets of fuzzy inference logic; which are the fuzzy controller and the rule modifier. A new learning method of the rule modifier, based on a fuzzy learning algorithm, is developed. The modification value of each rule is based on the fuzzy firing weight, so the learning algorithm can proceed reasonably. The proposed design method can be applied for on-line learning real-time control and can start to work even the control values of all rules are initiated from zeros.

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.

This study investigates the use of Genetic Algorithms (GA) to the design and implementation of Fuzzy Logic Controllers (FLC). A fuzzy logic is fully defined by its membership function. What is the best to determine the membership function is the first question that has been tackled. Thus it is important to select the accurate membership functions but these methods possess one common weakness where conventional FLC use membership function generated by human operators. The membership function selection process is done with trial and error and it runs step by step which is too long in completing the problem. This research develops a system that may help users to determine the membership function of FLC using the technique of GA optimization for the fastest processing in completing the problems. The data collection is based on the simulation results and the results refer to the transient response specification is maximum overshoot. From the results presented, the system which we developed is very helpful to determine membership function and it is clear that GA is very promising in improving the performance of the FLC to get more accurate in order to find the optimum result.

This paper presents the design of a fuzzy logic controller for a process air heater system which produced an overdamped temperature response. The process air heater system is modelled mathematically and simulated using MATLAB/Simulink. Five types of membership functions (Gaussian, triangular, trapezoidal, combined trapezoid and triangular and generalized Bell) with four different sizes of rule base (3×3, 5×5, 7×7 and 9×9) were designed and tested. The performances of the fuzzy logic controllers were compared based on maximum overshoot, settling time and integral of absolute error. The results showed that fuzzy logic controller with combined trapezoid and triangular membership function with 3×3 rule base can achieve the desired response.

In this paper, some learning control methods are presented and their potential applications to the control of residual nitrogen concentration by controlling the nitrogen feed rate to the fed-batch fermentation process are investigated. The learning control methods presented include neural network control, self-organising fuzzy logic control, state learning control and time-sequence iterative learning control. These methods have something in common, but also each method has its own features. >