On-line learning model reference self-organizing fuzzy logic control

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.

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.

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 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.

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 a self-organizing fuzzy logic control scheme based on neural networks, which consists of a traditional fuzzy logic (FL) controller and a conventional derivative (D) controller. Since membership functions regarding change-in-error ė represent the feedback of velocity they strongly affect transient behaviors of a system. In the proposed scheme, therefore, such membership functions are parameterized by the use of the cubic splines. Then, neural networks are adopted to optimize them in self-organizing process. To demonstrate the effectiveness of the proposed method, we report a number of simulation results involving both step and tracking control of a nonlinear plant.

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

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. >

In this chapter, the design and implementation of Self-Organizing Fuzzy Logic Controller (SOFLC) is explored with a particular application to control a multivariable model of anesthesia. A concept called decomposition of multivariable self-organizing fuzzy logic structure is proposed in this chapter. Hence, the basic forms of a simple 2 terms SOFLC to a multi-term complex multi-input/multi-output (MIMO) controller will be presented. Different design strategies of MIMO will be outlined and the application of SOFLC systems to muscle relaxation and depth of anesthesia control will be explored in the simulations. After comparison with four different MIMO controllers, the successful simulation results have given confidence to perform on-line clinical trials at the operating theatre in the near future.

Fuzzy inference has a multigranular architecture consisting of symbols and continuous values, and this architecture has worked well to incorporate experts' know-how into fuzzy controls. The paper focuses on the important characteristic of fuzzy control, "symbolic expression" of a fuzzy control system. The paper first introduces a "chain of rules" to clarify the description of the behavior of the control systems. A coincidence of the symbolic and continuous behaviors is defined. With these definitions, the paper proposes a new condition, "relaxed nonseparate condition" and a new fuzzy inference method. The relaxed condition together with the new inference method guarantees the coincidence of symbolic and continuous behavior of the control system. The paper proposes a new design method of fuzzy controller that guarantees this coincidence. Simulations are done to show the feasibility of the relaxed nonseparate condition and proposed design method.

Research on A Fuzzy Adaptive PID Control Method for Four Rotor UAV Control System With the continuous improvement of national living standards, people have higher and higher requirements for the reliability of power supply. The emergence of unmanned aerial vehicle inspection technology provides a new idea to solve the contradiction between the increasing equipment scale and the increasing inspection requirements. However, at present, the stability of the traditional proportion integration differentiation (PID) control system of the four rotor unmanned aerial vehicle (UAV) is not high enough. During the inspection, there are accidents of drones hitting power equipment from time to time. In view of the above problems, this paper studies the fuzzy adaptive PID control method to support the stable flight of four rotor UAV. The principle of fuzzy control is analyzed. The fuzzy control method and PID control method are deeply integrated. A fuzzy adaptive PID control system is designed. The universe and membership function of the relevant control parameters of the system are formulated. The tuning rules of fuzzy adaptive PID control system are formulated. Finally, the improved control system is simulated. The simulation results show that the fuzzy adaptive PID control algorithm can effectively improve the stability, reliability and emergency ability of UAV. This method can effectively improve the adaptability of UAV to perform complex missions in complex environments. The research results have guiding significance for expanding the application field of UAV.

The antilock braking system (ABS) is designed to optimize braking effectiveness and maintain steerability; however, the ABS performance will be degraded in the case of severe road conditions. In this study, a self-learning fuzzy sliding-mode control (SLFSMC) design method is proposed for ABS. The SLFSMC ABS will modulate the brake torque for optimum braking. The SLFSMC system is comprised of a fuzzy controller and a robust controller. The fuzzy controller is designed to mimic an ideal controller and the robust controller is designed to compensate for the approximation error between the ideal controller and the fuzzy controller. The tuning algorithms of the controller are derived in the Lyapunov sense; thus, the stability of the system can be guaranteed. Also, the derivation of the proposed SLFSMC ABS does not need to use a vehicle-braking model. Simulations are performed to demonstrate the effectiveness of the proposed SLFSMC ABS in adapting to changes for various road conditions.

A new design method of PID self-tuning immune fuzzy control was proposed to control the steam temperature system in fossil-fired power plant. Two fuzzy controllers, fuzzy self-tuning controller and immune P fuzzy controller, are designed to strengthen the normal PID cascade control. The outputs of self-tuning fuzzy controller are applied to tune the parameters of PID; the output of immune P fuzzy controller is transformed to regulate the proportion gain by the law of immunity feedback response in biology. Simulation results show that the control performance of this strategy is superior to that of normal PID cascade control. It is featured by strong robustness and self-adaptability, and can readily accommodate itself to the parameters variation.

Summary form only given. The field of the so-called emerging technologies, ie., fuzzy logic control, neuro-fuzzy control, has witnessed quite a sizable introduction of application products, mainly from Japan and Europe and recently from the US. The soft-computing community (fuzzy logic, neural networks and genetic algorithms) has begun to respond to the barrage of concerns about the analytical issues in fuzzy control and neuro-fuzzy control. This paper discusses the essential dynamical properties of a system that is controlled via fuzzy logic and fuzzy sets. It considers issues of controllability, stability and robustness.

We present a new technique to the design and real-time implementation of fuzzy control system basedon digital signal processors in order to improve the precision and robustness for system of industrial robot in this paper. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C80 is used in implementing real time fuzzy control to provide an enhanced motion control for robot manipulators. In this paper, a Self-Organizing Fuzzy Controller for the industrial robot manipulator with a actuator located at the base is studied. A fuzzy logic composed of linguistic conditional statements is employed by defining the relations of input-output variables of the controller. In the synthesis of a Fuzzy Logic Controller, one of the most difficult problems is the determination of linguistic control rules from the human operators. To overcome this difficult Self-Organizing Fuzzy Controller is proposed for a hierarchical control structure consisting of basic and high levels that modify control rules. The proposed Self-Organizing Fuzzy Controller scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the SOFC is illustrated by simulation and experimental results for a Dual-Arm robot with eight joints.