• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1212-1217
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• 2008

This paper studies the feasibility of using the nonlinear model predictive control as a formation flight control algorithm for unmanned aerial vehicles. The optimal control inputs for formation flight are calculated through the cost function which incorporates the relative positions of the individual vehicles to maintain a desired formation and also the inequality constraints on inputs and states using the Karush-Kuhn-Tucker conditions. In the nonlinear model predictive control setting, the optimal control inputs are implemented in a receding horizon manner, which is suitable for dealing with dynamic constraints. Numerical simulations are executed for the validation of the proposed scheme.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.7
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• pp.22-28
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• 1998

This paper includes AC servo motor speed control usig the predictive control strategy. Generally, AC servo motor control should have the fast response characteristics. For the issue, sliding mode control and PID control have been applied. However, the former has the speed ripple response due to the chattering and the latter requires the many trial efforts. Originally, the predictive control which has been used in process control area does not need the priori knowledge for the application system and it is easy to compute the optimal gain with the prediction. In this paper, the TMS320C31 DSP pocessor is used for AC motor control with fst dynamics and the tuning guid-line for the parameters of the predictive control algorithm is given in order to reduce the computation load. Also, the actuator saturationis implemented uisngthe QP(Quadratic Programming) method and the transient response is improved by the identified intertia coefficient when AC motor is drived at forward/reverse rotation.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.115-121
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• 2022

This paper proposes a distributed model predictive formation control algorithm for a group of unmanned ground vehicles (UGVs) with guaranteeing collision avoidance between UGVs. Generally, the model predictive control based formation control has a disadvantage in that it takes a long time to compute control inputs when considering collision avoidance between UGVs. In this paper, in order to overcome this problem, the formation control algorithm is implemented in a distributed manner so that it could be individually controlled. Also, a collision-avoidance method considering real-time is proposed. The proposed formation control algorithm is implemented based on robot operating system (ROS), open source-based middleware. Through the various simulation tests, it is confirmed that the formation control of five UGVs is successfully performed while avoiding collisions between UGVs.

• Smart Structures and Systems
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• v.11 no.6
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• pp.623-635
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• 2013

This paper investigates application of a control algorithm called model predictive sliding mode control (MPSMC) to active vibration suppression of a cantilevered aluminum beam. MPSMC is a relatively new control algorithm where model predictive control is employed to enhance sliding mode control by enforcing the system to reach the sliding surface in an optimal manner. In previous studies, it was shown that MPSMC can be applied to reduce hysteretic effects of piezoelectric actuators in dynamic displacement tracking applications. In the current study, a cantilevered beam with unknown mass distribution is selected as an experimental test bed in order to verify the robustness of MPSMC in active vibration control applications. Experimental results show that MPSMC can reduce vibration of an aluminum cantilevered beam at least by 29% regardless of modified mass distribution.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.606-613
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• 2003

As a technical method for controlling chaotic dynamics, this paper presents a predictive control for chaotic systems based on radial basis function networks(RBFNs). To control the chaotic systems, we employ an on-line identification unit and a nonlinear feedback controller, where the RBFN identifier is based on a suitable NARMA real-time modeling method and the controller is predictive control scheme. In our design method, the identifier and controller are most conveniently implemented using a gradient-descent procedure that represents a generalization of the least mean square(LMS) algorithm. Also, we introduce a projection matrix to determine the control input, which decreases the control performance function very rapidly. And the effectiveness and feasibility of the proposed control method is demonstrated with application to the continuous-time and discrete-time chaotic nonlinear system.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.171-175
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• 1992

In this paper, we derive the optimal input-constrained control law which minimizes predictive control objective function subject to input constraints. In order to obtain the closed form of control law, three suboptimal methods are proposed and evaluated by simulation.

• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.63-64
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• 2015

The control of IPMSM(Interior Permanent Magnet Synchronous Motor) for electric vehicle is important to track torque reference depended on accelerator. This paper executes IPMSM control applied the predictive current control which has good dynamic characteristic and, compare PI control with predictive current control to verify dynamic characteristic through simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.134-138
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• 1996

Refuse incineration plant operations involve many kinds of uncertain factors, such as the variable physical properties of refuse as fuel and the complexity of the burning phenomenon. That makes it very difficult apply conventional control methods to the combustion control of the refuse. In this paper, an adaptive fuzzy model predictive controller is proposed for the combustion control of the refuse. In this paper, an adaptive fuzzy model predictive controller is proposed for the combustion control of the refuse. And computer simulation was carried out to evaluate performance of the proposed controller.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.12 no.7
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• pp.680-687
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• 2000

The objective of this study is to improve the control performance of Ondol system which causes overheating and underheating with 2-position on/off control. For this, a predictive control that determines the suitable on/off positions using Artificial Neural Network(ANN) model was proposed Dynamic analyses using computer simulation show that the neural network used in the predictive control is adapted to each room whose loads variation and thermal characteristics are different. To examine the applicability of this predictive control with ANN it was compared with 2-position on/off control through experiments.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.286-286
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.