• Journal of the Korean Society for Precision Engineering
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• v.12 no.4
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• pp.111-118
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• 1995

This paper presents the modified fuzzy-compensated PID(FCPID) control, which involves adding the compensator to an existing PID controller, to improve the performances of the systems. Compared to a conventional PID control and a fuzzy logic control(FLC), the proposed control scheme has superior performance. Experimental results of an actual implementation of the modified PC-based FCPID controller on the DC servo-motor demonstrate considerable improve- ment of the performance of the existing FCPID control by monitoring the scaling factor. They show faster responses and smaller overshoots than the conventional FCPID control scheme for the various reference inputs and the robustness to the loads.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.2
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• pp.403-410
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• 1999

A conventional PID controller has poor performance when it applied to systems with unknown deadzones. To solve this problem, this paper proposes PID controller using two layered-fuzzy logic. The structure of controller is reconstructed with fuzzy compensator and fuzzy tuner on the conventional PID controller. Our proposed control scheme shows superior transient and steady-state performance compared to conventional PID controller. The scheme is robust to variations in deadzone nonlinearities as well as the steady-state gain of the plant. The performance of the developed controller is verified through simulation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.701-708
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• 2017

This paper proposes an optimum design method using Teaching-Learning-based optimization for the fuzzy PID controller of Magnetic levitation rail-guided vehicle. Since an attraction-type levitation system is intrinsically unstable, it is difficult to completely satisfy the desired performance through the conventional control methods. In the paper, a fuzzy PID controller with fixed parameters is applied and then the optimum parameters of fuzzy PID controller are selected by Teaching-Learning optimization. For the fitness function of Teaching-Learning optimization, the performance index of PID controller is used. To verify the performances of the proposed method, we use a Maglev model and compare the proposed method with the performance of PID controller. The simulation results show that the proposed method is more effective than conventional PID controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.1
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• pp.22-30
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• 2001

This paper describes a neural network based fuzzy PID control scheme. The PID controller is being widely used in industrial applications. However, it is difficult to determine the appropriated PID gains in nonlinear systems and systems with long time delay and so on. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based self tuning fuzzy PID controller of which output gains were adjusted automatically. The tuning parameters of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods. Then they were adjusted by using proposed neural network learning algorithm. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The experiment on the magnetic levitation system, which is known to be heavily nonlinear, showed the proposed controller's excellent performance.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.3
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• pp.30-37
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• 2004

This thesis develops a fuzzy-PID control algorithm for control the filament winding tension. It is developed by applying classical PID control technique to a fuzzy logic controller. It is composed of a fuzzy-PI controller and a fuzzy-D controller. The fuzzy-PI controller uses error and integrated error as inputs, and the fuzzy-D controller uses derivative of error as input. The fuzzy-PI controller uses Takagi-Sugeno fuzzy inference system, and the fuzzy-D controller uses Mamdani fuzzy inference system. The fuzzy rule base for the fuzzy-PI controller is designed using 19 rules, and the fuzzy rule base for the fuzzy-D controller is designed using 5 rules. A test-bed is set-up for verifying the effectiveness of the developing control algorithm in control the filament winding tension. It is composed of a mandrel, a carriage, a force sensor, a driving roller, nip rollers, a creel, and a real-time control system. Nip rollers apply a vertical force to a filament, and the driving roller drives it. The real-time control system is developed by using MATLAB/xPC Target. First, experiments for showing the inherent problems of an open-loop control scheme in a filament winding are performed. Then, experiments for showing the robustness of the developing fuzzy-PID control algorithm are performed under various working conditions occurring in a filament winding such as mandrel rotating speed change, carriage traversing, spool radius change, and reference input change.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.1
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• pp.1-10
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• 2003

This paper is concerned with the fuzzy control scheme and PID controller for the vibration suppression control of a cantilever beam equipped with a laser sensor and an electromagnetic actuator. The PID controller is being widely used in industrial applications. However, it is difficult to determine the appropriate PID gains in nonlinear systems and systems with time variant characteristic and so on. In this paper, we design the fuzzy based PID controller of which output gains are adjusted automatically and the designed controller is applied to active vibration control of a cantilever beam using electromagnetic actuator with strong nonlinearity. The tuning PID parameters of proposed controller are determined by using Fuzzy algorithm. Effectiveness and performance of the designed controller are verified by both simulation and experiment results. Experimental results demonstrate that better control performance can be achieved in comparison with the PID cotroller.

• Journal of Advanced Marine Engineering and Technology
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• v.26 no.6
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• pp.638-645
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• 2002

This paper presents a scheme for integrating PID control, gain scheduling and emerging techniques in the field of artificial intelligence, such as fuzzy logic and genetic algorithms for the speed control of a marine diesel engine. At first, local PID controllers are designed based on a local model obtained at each speed mode, whose parameters are optimally tuned using a real-coded genetic algorithm. Then, fuzzy "if-then" rules combine the local controllers as a consequence part to implement fuzzy gain scheduling. To demonstrate the performance of the proposed fuzzy PID controller on overall operating conditions, a set of simulation works on B'||'&'||'W's 4L80MC diesel engine are carried out.t.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.2
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• pp.55-61
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• 2000

In this paper, we proposed a new PID tuning algorithm by the fuzzy set theory to improve the performance of the PID controller. The new tuning algorithm for the PID controller has the initial value of parameter Kc, $\tau$I, $\tau$D by the Ziegler-Nichols formula using the ultimate gain and ultimate period from a relay tuning experiment. We get error and error change of plant output correspond to the initial value and new proportion gain(Kc) and integral time($\tau$I) from fuzzy tunner. This fuzzy tuning algorithm for PID controller considerably reduced overshoot and rise time compare to any other PID controller tuning algorithms. In real parametric uncertainty systems, the PID controller with Fuzzy auto-tuning give appreciable improvement in the performance. The significant properties of this algorithm is shown by simulation In this paper, we proposed a new PID algorithm by the fuzzy set theory to improve the performance of the PID controller.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.1
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• pp.63-66
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• 1995

PID controller is being used in many servo control system. However, when a control system has variable load, it is difficult to guarantee the accurate control of the system. In the way of solving this problem, in this paper, a auto-tuning method of PID controller parameter using fuzzy rule in variable load is presented. The parameter of PID controller are decided by fuzzy rule according to load variation. The accurate control function of fuzzy auto-tuning is demonstrated by simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1066-1070
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• 2003

Recently technologies have created new principle and theory but the PID control system remains its popularity as the PID controller contains simple structure, including maintenance and parameter adjustment being so simple. Thus, this paper proposes auto tune PID by fuzzy logic controller based on FPGA which to achieve real time and small size circuit board. The digital PID controller design to consist of analog to digital converter which use chip TDA8763AM/3 (10 bit high-speed low power ADC), digital to analog converter which use two chip DAC08 (8 bit digital to analog converters) and fuzzy logic tune digital PID processor embedded on chip FPGA XC2S50-5tq-144. The digital PID processor was designed by fundamental PID equation which architectures including multiplier, adder, subtracter and some other logic gate. The fuzzy logic tune digital PID was designed by look up table (LUT) method which data storage into ROM refer from trial and error process. The digital PID processor verified behavior by the application program ModelSimXE. The result of simulation when input is units step and vary controller gain ($K_p$, $K_i$ and $K_d$) are similarity with theory of PID and maximum execution time is 150 ns/action at frequency are 30 MHz. The fuzzy logic tune digital PID controller based on FPGA was verified by control model of level control system which can control level into model are correctly and rapidly. Finally, this design use small size circuit board and very faster than computer and microcontroller.