• Journal of the Korean Society for Precision Engineering
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• v.11 no.6
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• pp.57-64
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• 1994

This paper proposes a fuzzy tuning PID controller for straight line type inverted pendulum. The conventional PID controller which is used widely in industrial field has fatal drawback on determining control gains for practical system. The proposed controller tunes the gains automatically based on fuzzy urle derived from the experience of expert operator. The results of simulation and experiment show the efficiency of the proposed control method comparing with conventional PID control method in terms of rising time, overshoot, and overall errors.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.809-811
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• 1999

The three-mode PID controller is widely used in plant because control algorithm is simple and tuning method is general. But the conventional PID controller can not have the both purpose of setpoint tracking and disturbance rejection at the same time. In this paper, we studied the application 2DOF PID controller to pneumatic control valve through simulation and experiments. Results are Presented a satisfactory response

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.323-330
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• 2004

In this paper, we propose a design method of PID controller using an improved Tabu Search. Tabu Search is improved by neighbor solution creation using Gaussian random distribution and generalized Hermite Biehler Theorem for stable bounds. The range of admissible proportional gains are determined first in closed form. Next the optimal PID gains are selected by improved Tabu Search. The results of Computer simulations represent that the proposed Tabu Search algorithm shows a fast convergence speed and a good control performance.

• Journal of Biomedical Engineering Research
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• v.8 no.2
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• pp.245-254
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• 1987

A tuning method for the conventional PID controller in a two-input two-output multivariable system presented. The system is assumed to be no disturbances and modelling errors. Simulation studies show that controller can provide excellent tuning for highly interacting or unfavorable processes.

• International Journal of Automotive Technology
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• v.4 no.4
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• pp.181-191
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• 2003

Since the nonlinearity and uncertainties which inherently exist in vehicle system need to be considered in active suspension control law design, this paper proposes a new control strategy for active vehicle suspension systems by using a combined control scheme, i.e., respectively using a genetic algorithm (GA) based self-tuning PID controller and a fuzzy logic controller in two loops. In the control scheme, the PID controller is used to minimize vehicle body vertical acceleration, the fuzzy logic controller is to minimize pitch acceleration and meanwhile to attenuate vehicle body vertical acceleration further by tuning weighting factors. In order to improve the adaptability to the changes of plant parameters, based on the defined objectives, a genetic algorithm is introduced to tune the parameters of PID controller, the scaling factors, the gain values and the membership functions of fuzzy logic controller on-line. Taking a four degree-of-freedom nonlinear vehicle model as example, the proposed control scheme is applied and the simulations are carried out in different road disturbance input conditions. Simulation results show that the present control scheme is very effective in reducing peak values of vehicle body accelerations, especially within the most sensitive frequency range of human response, and in attenuating the excessive dynamic tire load to enhance road holding performance. The stability and adaptability are also showed even when the system is subject to severe road conditions, such as a pothole, an obstacle or a step input. Compared with conventional passive suspensions and the active vehicle suspension systems by using, e.g., linear fuzzy logic control, the combined PID and fuzzy control without parameters self-tuning, the new proposed control system with GA-based self-learning ability can improve vehicle ride comfort performance significantly and offer better system robustness.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.330-336
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• 2014

Many industrial processes such as continuous stirred tank reactors(CSTRs), desalination plant, distillation columns, pH neutralization processes and so on exhibit highly nonlinear characteristic and time-varying behavior during operation. The control of such processes has been challenging to control engineers. Hence, a variety of forms of PID controllers and their tuning rules for industrial processes have been developed to guarantee the best performance. In this paper, a scheme that designs the practical PID controller with an anti-windup strategy incorporating with an evolutionary algorithm(EA) is presented for the concentration control of a nonisothermal CSTR. EA is used to tune the parameters of the overall PID control process with anti-windup by minimizing the integral of absolute error(IAE). Simulation works for reference tracking and disturbance rejecting performances and robustness to parameter changes show the feasibility of using the proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.1
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• pp.180-186
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• 2001

PID control is widely used in industrial areas, but it is not easy to tune PID gains for an optimal control. The proposed learning method is to tune PID gains using the gradient approach. We use two estimation functions in this method : one is an error function for tuning of PID gains, and the other is a performance measuring function for a completion of learning. This paper shows that optimal PID controllers can be acquired when this learning method is applied to 10 systems with different natural frequencies and damping ratios.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1027-1037
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• 2017

Penetration of renewable energy sources makes the modern interconnected power systems to have more intelligence and flexibility in the control. Hence, it is essential to maintain the system frequency and tie-line power exchange at nominal values using Load Frequency Control (LFC) for efficient, economic and reliable operation of power systems. In this paper, intelligent tuning of the Proportional Integral Derivative (PID) controller for LFC in an interconnected power system is considered as a main objective. The chosen problem is formulated as an optimization problem and the optimal gain parameters of PID controllers are computed with three innovative swarm intelligent algorithms named Particle Swarm Optimization (PSO), Bacterial Foraging Optimization Algorithm (BFOA) and hybrid Bacterial Foraging Particle Swarm Optimization (BFPSO) and a comparative study is made between them. A new objective function designed with necessary time domain specifications using weighted sum approach is also offered in this report and compared with conventional objective functions. All the simulation results clearly reveal that, the hybrid BFPSO tuned PID controller with proposed objective function has better control performances over other optimization methodologies.

• Journal of Korean Port Research
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• v.14 no.2
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• pp.187-197
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• 2000

In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied to the driving control of the autonomous guided vehicle(AGV). When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged by the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined through this off-line manner, these parameters are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough initially, the weighting parameters are adjusted more accurately through the on-line fine tuning. The experiment for the control of steering and speed of AGV is performed. The results show that the proposed controller provides better performances than other conventional controllers.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.239-239
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• 2000

This paper present experimental results on temperature trajectory tracking. The benefits of precalculated feedforward input together with PID feedback control are demonstrated by experimental results. To find the feedforward input, the plant (autoregresiive) model is first identified and convex optimization procedure is applied. PID controller is then implemented based on Ziegler-Nickels tuning rule to reduce effects of disturbances and modeling errors. Experimental results show an improvement in slope tracking performance over the fully PID controller.