• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
• /
• pp.299-302
• /
• 1996

A control algorithm is proposed for nonlinear multi-input multi-output(MIMO) batch processes by combining quadratic iterative learning control(Q-ILC) with model predictive control(MPC). Both controls are designed based on output feedback and Kalman filter is incorporated for state estimation. Novelty of the proposed algorithm lies in the facts that, unlike feedback-only control, unknown sustained disturbances which are repeated over batches can be completely rejected and asymptotically perfect tracking is possible for zero random disturbance case even with uncertain process model.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.62.3-62
• /
• 2001

The predictive control is one of the nonlinear three-axis rotation methods. The desired trace of a satellite is pre-determined, and the control inputs are designed so that the satellite follows the ´predictive´ trace. The predictive control has been adapted to the research for the three-axis attitude control. In that case, the control variables are the quaternion represented the angular rates and attitude angles of the body about the three-axes. The objective of this paper is to propose to design a predictive controller for the three-axis attitude control under external disturbances. In order to do that, this paper proposes how to construct a predictive control law including disturbances and to discern them. The basic algorithm of the existent predictive control is partially modified, and the presumption and modeling of disturbances are performed ...

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
• /
• pp.183-186
• /
• 2002

In this paper, a predictive control method using wavelet neural network for chaotic nonlinear systems is presented. In our method, we use the adjusting method of the parameter for the training a wavelet neural network. The control signals are directly obtained by minimizing the difference between a reference signal and the output of a wavelet neural network. To verify the efficiency of our method, we apply it to the Duffing and the Henon system, which are a representative continuous and discrete time chaotic nonlinear system respectively.

• 한국항해항만학회:학술대회논문집
• /
• 한국항해항만학회 2002년도 춘계학술대회논문집
• /
• pp.219-227
• /
• 2002

In this paper, we designed neural network predictive PID controller to control sway happened in transfer of trolley for automatic travel control system. We include dynamic character of nonlinear system, and mathematical expression veny simple used neural network. When various establishment location and surrounding disturbance were approved based on mathematical modelling of crane, controller designed to become effective control location error and vibration angle of two control variables that simultaneously can predictive control. Neural network predictive PID controller produced parameter of PID controller using neural network self-tuner. Neural network self-tuner's input used crane's output and neural network predictive output. Neural network self-tuner using error back propagation algorithm. We analyzed control performance comparison through computer simulation when applied disturbance about sway of location and angle in transfer of crane. The results show that the proposed neural network predictive PID controller has better performances than general PID controller, neural network PID controller.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
• /
• pp.329-332
• /
• 1997

This paper deals with the attitude control problem of nonlinear MIMO propeller setup. Multivariable GPC[Generalized Predictive Control] is adopted as the main controller, and it is implemented by TMS320C31 in the current paper. The main object of control is to move the propellers to wanted positions. System identification is performed to configure the system. Performance of the multivariable predictive controller implemented is shown via some experiments, which shows the controller meets the adequate control purpose.

• 한국항해항만학회지
• /
• 제47권2호
• /
• pp.66-74
• /
• 2023

With the increase of interest in developing Maritime Autonomous Surface Ships (MASS), an optimal ship route planning is gradually gaining popularity as one of the important subsystems for autonomy of modern marine vessels. In the present paper, an optimal ship route planning model for MASS is proposed using a nonlinear MPC approach together with a nonlinear MMG model. Results drawn from this study demonstrated that the optimization problem for the ship route was successfully solved with satisfaction of the nonlinear dynamics of the ship and all constraints for the state and manipulated variables using the nonlinear MPC approach. Given that a route generation system capable of accounting for nonlinear dynamics of the ship and equality/inequality constraints is essential for achieving fully autonomous navigation at sea, it is expected that this paper will contribute to the field of autonomous vehicles by demonstrating the performance of the proposed optimal ship route planning model.

• 제어로봇시스템학회논문지
• /
• 제6권4호
• /
• pp.254-260
• /
• 2000

This paper deals with the development of fault tolerant control for a nonlinear boiler system with noise and disturbance. The MCMBPC(Multivariable Constrained Model Based Predictive Control) is adopted for the control of the specific boiler turbin model. The fault detection and diagnosis are accomplished with the Kalman filter and two bias estimators. Once a fault is detected, two Bias estimators are driven to estimate the fault and to discriminate Process fault and sensor fault. In this paper, a fault tolerant control scheme combining MCMBPC with a fault compensation method based on the bias estimator is proposed. The proposed scheme has been applied to the nonlinear boiler system and shown a satisfactory performance through some simulations.

• 제어로봇시스템학회논문지
• /
• 제3권3호
• /
• pp.272-279
• /
• 1997

In this paper, a fuzzy generalized predictive control (FGPC) for non-linear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input-output data without identifying a plant model. A parameter estimation algorithm is used for identifying the fuzzy predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus may possess inherent good properties of the GPC. Computer simulations show that the performance of the FGPC is satisfactory.

• 한국군사과학기술학회지
• /
• 제27권4호
• /
• pp.495-506
• /
• 2024

In this study, we propose the terrain following algorithm using model predictive control and nonlinear disturbance observer-based backstepping sliding mode controller for an aircraft system. Terrain following is important for military missions because it helps the aircraft avoid detection by the enemy radar. The model predictive control is used to replace the generating trajectory and guidance with the flight path angle constraint. In addition, the aircraft is affected to the parameter uncertainty and unknown disturbance such as wind near the mountainous terrain. Therefore, we suggest the nonlinear disturbance-based backstepping sliding mode control method for the aircraft that has highly nonlinearity to enhance flight path angle tracking performance. Through the numerical simulation, the proposed method showed the better tracking performance than the traditional backstepping method. Furthermore, the proposed method presented the terrain following maneuver maintaining the desired altitude.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2004년도 ICCAS
• /
• pp.1975-1980
• /
• 2004

A parallel active power filter is designed and implemented to compensate for undesired current harmonics generated by a nonlinear load. The filter works based on PWM strategy and control signal is generated using a model predictive controller. To evaluate the achievements, a PI controller is also designed and implemented. Experimental results indicate about 50% increase in the efficiency over PI controller.