• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2299-2301
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• 2000

In this paper, the design of PID controller using Neural networks for the control of non-linear system is presented. First, non-linear system is identified using BPN(Backpropagation Network) algorithm. This identified model is connected to the PID controller and the parameters of PID controller are updated to the direction of reducing the difference between the identified model output and model reference output in arbitrary input signal. Therefore, identified model output tracks the model reference output in an acceptable error range and the parameters of controller are updated adaptively. The output of the system has a good performance in case of both noisy and noiseless model reference and we can control the system stable in off-line when the dynamics of the system is changed.

• Journal of Aerospace System Engineering
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• v.11 no.1
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• pp.14-21
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• 2017

A Linear parameterized Sigma-Pi neural network (SPNN) is applied to a tilt-duct unmanned aerial vehicle (UAV) which has a very large longitudinal stability ($C_{L{\alpha}}$). It is uncontrollable by a proportional, integral, derivative (PID) controller due to heavy stability. It is shown that the combined inner loop and outer loop of SPNN controllers could overcome the sluggish longitudinal dynamics using a method of dynamic inversion and pseudo-control to compensate for reference model error. The simulation results of the way point guidance are presented to evaluate the performance of SPNN in comparison to a PID controller.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.6
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• pp.633-641
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• 2015

Several complex processes that are employed in industries, such as shipping, power plants, and the petrochemical industry, involve time-varying behavior as well as strong nonlinear behavior during operation. The fixed-parameter proportional-integral-derivative (PID) controllers have difficulty in dealing with control problems that occur in such processes. In this paper, we propose a method of designing a nonlinear PID controller for industrial processes that exhibit a large number of nonlinearities and time-varying behavior. The gains of the nonlinear PID controller are characterized by a simple nonlinear function of the error and/or error rate depending on the process set-point and output. We tune the user-defined parameters using a genetic algorithm by minimizing the integral of time absolute error (ITAE) index. We verify the effectiveness of the proposed method by performing a comparison of the proposed method and two other nonlinear and adaptive methods that are employed for reference tracking, disturbance-rejection performances, and robustness to parameter changes on a continuously stirred tank reactor.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1233-1236
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• 1996

Attitude Control System for a Hydrofoil type catamaran in wave is designed using a Decentralized Adaptive Control technique which is announced already by authors. This automatic attitude control system is designed for its good seaworthiness and for robustness on the variation of center of gravity. The performance is compared with a PID controller and the results show that the Decentralized Adaptive controller has better stability on the variation of the center of gravity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.113-117
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• 2002

PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But it is difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose a nonlinear adaptive PR controller based on an Immune feedback mechanism and a gradient descent teaming. This algorithm has a simple structure and robustness to system parameters variation. To verify performances of the proposed nonlinear adaptive PID controller, the speed control of nonlinear DC motor Is peformed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1341-1343
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• 1996

This paper presents a design method for an on-line adaptive neural networks based intelligent controller. The proposed neural controller, assuming PID controller is initially presented, learns the equivalent behaviors of the existing PID controller initially and switches to take over the PID control system. Then, it executes on-line adaptation via evaluating its performance and minimizing user defined cost function constantly so that the optimal control can be achieved. The PID controller and the proposed neural controller are investigated and compared in computer simulation.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.3
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• pp.320-327
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• 1998

The purpose of this study is to design the intelligent speed control system for marine diesel engine by combining the Model Matching Method and the Nominal Model Tracking Method. Recently for the speed control of a diesel engine some methods using the advanced control techniques such as LQ control Fuzzy control or H$\infty$ control etc. have been reported. However most of speed controllers of a marine diesel engine developed are still using the PID control algorithm But the performance of a marine diesel engine depends highly on the parameter setting of the PID controllers. The authors proposed already a new method to tune efficiently the PID parameters by the Model Mathcing Method typically taking a marine diesel engine as a non-oscillatory second-order system. It was confirmed that the previously proposed method is superior to Ziegler & Nichols's method through simulations under the assumption that the parameters of a diesel engine are exactly known. But actually it is very difficult to find out the exact model of the diesel engine. Therefore when the model and the actual diesel engine are unmatched as an alternative to enhance the speed control characteristics this paper proposes a Model Refernce Adaptive Speed Control system of a diesel engine in which PID control system for the model of a diesel engine is adopted as the nominal model and a Fuzzy controller is adopted as the adaptive controller, And in the nominal model parameters of a diesel engine are adjusted using the Model Matching Method. it is confirmed that the proposed method gives better performance than the case of using only Model Matching Method through the analysis of the characteristics of indicial responses.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.220-227
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• 1999

The position control accuracy of a robot arm is significantly deteriorated when a long slender arm robot is operated at a high speed. In this case, the robot arm needs to be modeled as a flexible structure, not a rigid one, and its control system needs to be designed with its elastic modes taken into account. In this paper, the vibration control scheme of a one-link flexible manipulator using adaptive input shaper in conjunction with PID controller is presented. The robot consists of a flexible arm manufactured with a thin aluminium plate, an AC servo motor with a harmonic drive for speed reduction, an optical encoder and an accelerometer. On-line identification of the vibration mode is done using the pruned decimation-in-time FFT algorithm to estimate the parameter of the input shaper. Experimental results of the flexible manipulator with a PID controller and input shaper are provided to show the effectiveness of the advocated controllers.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.675-679
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• 1992

In industry, the speed control of single-phase induction motor in domestic use is generally controlled by a simple ON-OFF or PID control method. However, in this case, in order to have a good speed regulating characteristics, itself should be modified in accordance with the optimum PID factors which are varied each time operating speed changes. Shortening the development time and saving the cost which are needed to modify the controller is a major problem to be solved now in industry. In order to alleviate the above difficulties, it is proposed to apply adaptive control technique using MRFAC(Model Reference Following Adaptive Control) for the speed control of single-phase induction motor which has scarcely been studied. In this paper, the above speed control technique is achieved using MCS-96 one chip micro controller with a good speed control characteristics and it is expetted to open a wide application field in the speed control of single-phase induction motor in the future.

• Journal of Aerospace System Engineering
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• v.17 no.5
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• pp.34-41
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• 2023

This paper applies the icing effect and wing rock uncertainty to small unmanned aerial vehicles (UAVs), which have recently attracted attention. Attitude control simulations were performed using various control methods. First, the selected platform, the Skywalker X8 UAV with blended wing-body (BWB) configuration, was linearized for both its baseline form, and a form with applied icing effects. Subsequently, using MATLAB SimulinkⓇ, simulations were conducted for roll and pitch attitude control of the baseline configuration and the configuration with icing effects, employing disturbance observer-based PID control, model reference adaptive control, and model predictive control. Furthermore, the study introduced wing rock uncertainty simultaneously with icing effects on the configured model-a combination not previously explored in existing research-and conducted simulations. The performance of each control Method was compared and analyzed.