• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.8
• /
• pp.92-98
• /
• 1995

The position control of flexible joint manipulator is investigated by applying the self-organizing fuzzy logic controller (SOC) proposed by Procyk and Mamdani. The SOC is a heuristic rule-based controller and a further extension of an ordinary fuzzy controller, which has a hierachy structrue which consists of an algorithm being identical to a fuzzy controller at the lower ollp and a learning algorithm accomodating the performance evalution and rule modification function at the upper ollp. This form of control can be used in those complex systems which have been too difficult to control or which in the past have had to rely on the experience of a human operator. Even though the significant dynamic coupling of the motors and links on the flexible joint manipulator, the performance of command-following is good by applying the proposed SOC.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.40 no.8
• /
• pp.774-781
• /
• 1991

This paper proposes an auto-tuning method of a discrete -PIC controllers which is based on the Ziegler and Nichols's PID Tuning Rule. This tunign rule is derived using the Pade's first order approximation and it prevents the performance degradation caused by the time-delay effect of zero order holder when the Ziegler-Nichols tuning rule is applied to a discrete PID controller. A simple and practical auto-tuning method is proposed through combining this discrete tuning rule with the relay control. The auto-tuning scheme is implemented on a microprocessor based system and is applied to a position control system to show the effectiveness of the discrete tuning rule.

• Nuclear Engineering and Technology
• /
• v.44 no.4
• /
• pp.363-368
• /
• 2012

A feedwater controller targeted for an ABWR has been implemented using a modern field programmable gate array (FPGA), and verified using the full scope simulator at Taipower's Lungmen nuclear power station. The adopted control algorithm is a rule-based fuzzy logic. Point to point validation of the FPGA circuit board has been executed using a digital pattern generator. The simulation model of the simulator was employed for verification and validation of the controller design under various plant initial conditions. The transient response and the steady state tracking ability were evaluated and showed satisfactory results. The present work has demonstrated that the FPGA based approach incorporated with a rule-based fuzzy logic control algorithm is a flexible yet feasible approach for feedwater controller design in nuclear power plant applications.

• Proceedings of the KIPE Conference
• /
• 2002.07a
• /
• pp.166-169
• /
• 2002

The purpose of this paper is to develop systematic analysis and automatic tuning rule of PID controller for industry servo applications. Considering the coupling of inner current control loop and speed loop delay, the target plant fit into second-order plus time delay model. Based on PID controller design for high-order plus known/unknown time delay plant model, some formulars are provided for the control gain calculation and system-based theoretical analysis is developed, and it also allows an automatic controller setup to benefit the inexperienced user. In addition, the proposed design rule gives uniformly satisfactory performance and the motor speed stays on a desired response curve with minimal oscillation and settling time. This approach can be applicable in conjunction with the cascaded control loop which is widely used in practice.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.12
• /
• pp.1056-1060
• /
• 2002

Model-based tuning rules of the PID controller are proposed incorporating with real-coded genetic algorithms. The optimal parameter sets of the PID controller for step set-point tracking are obtained based on the first-order time delay model and a real-coded genetic algorithm as an optimization tool. As for assessing the performance of the controllers, performance indices(ISE, IAE and ITAE) are adopted. Then tuning rules are derived using the tuned parameter sets, potential rule models and another real-coded genetic algorithm A set of simulation works is carried out to verify the effectiveness of the proposed rules.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.5
• /
• pp.485-493
• /
• 1990

The idea of expert control is to incorporate a rule based expert system in a feedback control system. In this paper, we present some heuristic rules based on relay experiment for the choice of controller structure and the setting of the controller parameters. Heuristic rules are used as an element of the feedback loop in an auto-tuning PID controller. The algorithms are coded in a form which is as pure as possible and the heuristic logic is implemented with the rules. This paper reports an implementation of an expert controller on microcomputer-based system, including an industrial programmable controller.

• Journal of Industrial Technology
• /
• v.20 no.B
• /
• pp.155-162
• /
• 2000

Although Fuzzy Logic Controller(FLC) adopted three terms as input gives better performance, FLC is in general composed of two-term control because of the difficulty in the construction of fuzzy rule base. In this paper, a three-term FLC which is similar to PID control but acts as a nonlinear controller is proposed. To reduce the complexity of the rule base design and to increase efficiency. a simplified fuzzy PID control is induced from a hybrid velocity/position type PID algorithm by sharing a common rule base for both fuzzy PI and fuzzy PD parts. It is simple in structure, easy in implementation, and fast in calculation. The phase plane technique is applied to obtain the rule base for fuzzy two-term control and the resultant rule base is Macvicar-Whelan type. And the membership function is a Gaussian function. The frequency response information is used in tuning of the membership functions. Also a tuning strategy for the scaling factors is proposed based on the relationship between PID gain and the scaling factors. Simulation results show better performance and the effectiveness of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.50 no.1
• /
• pp.14-21
• /
• 2001

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. In indirect adaptive fuzzy control, based on the proved approximation capability of fuzzy systems, they are used to capture the unknown nonlinearities of the plant. Until now, most of the papers in the field of controller design for nonlinear system considers the affine system using fuzzy systems which have fixed grid-rule structure. We proposes a dynamic fuzzy rule insertion scheme where fuzzy rule-base grows as time goes on. With this method, the dynamic order of the controller reduces dramatically and an appropriate number of fuzzy rules are found on-line. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed-loop system is guaranteed.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.41 no.4
• /
• pp.355-361
• /
• 1992

In this paper, some rules for auto- tuning of feedback-feedforward controller in variable load and disturbance are presented. The parameters of feedback PID controller are determined by heuristic rules based on input regulation experiment, and the parameters of feedforward controller are determined by result rules based on spectral factorization, minimum variance, and polynomial equation. These heuristic and result rules are used as an element of the feedback loop in an auto-tuning feedback-feedforward controller. The robust and accurate control performance is demonstrated by computer simulation.

• Journal of the Institute of Convergence Signal Processing
• /
• v.11 no.3
• /
• pp.250-254
• /
• 2010

As the number of input and fuzzy set of a fuzzy system increase, the size of the rule base increases exponentially and becomes unmanageable (curse of dimensionality). In this paper, tree architectures of fuzzy controller (TAFC) is proposed to overcome the curse of dimensionality problem occurring in the design of fuzzy controller. TAFC is constructed with the aid of AND and OR fuzzy neurons. TAFC can guarantee reduced size of rule base with reasonable performance. For the development of TAFC, genetic algorithm constructs the binary tree structure by optimally selecting the nodes and leaves, and then random signal-based learning further refines the binary connections (two-step optimization). An inverted pendulum system is considered to verify the effectiveness of the proposed method by simulation.