• 제어로봇시스템학회논문지
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• 제13권3호
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• pp.268-272
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• 2007

These days many scientists make studies of feedback control system for stability on non-linear state and for the maneuver of flying objects. These feedback control systems have to satisfy trajectory condition and angular conditions, that is to say, controllability and stability simultaneously to achieve mission. In this paper, a design methods using model based control system which consists of spiral predictive model, Q-function included into generalized-work function is shown. It is made a clear that the proposed algorithm using SPM maneuvers for controllability and stability at the same time is successful in attaining our purpose. The feature of the proposed algorithm is illustrated by simulation results. As a conclusion, the proposed algorithm is useful for the control of moving objects.

• International Journal of Control, Automation, and Systems
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• 제4권2호
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• pp.139-145
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• 2006

A multi-variable Golden-Section adaptive controller is proposed for the tracking control of robotic manipulators with unknown dynamics. With a small sample time, the unknown dynamics of the robotic manipulator are denoted equivalently by a characteristic model of a 2-order multivariable time-varying difference equation. The coefficients of the characteristic model change slowly with time and some of their valuable characteristic relationships emerge. Based on the characteristic model, an adaptive algorithm with a simple form for the control of robotic manipulators is presented, which combines the multi-variable Golden-Section adaptive control law with the weighted least squares estimation method. Moreover, a compensation neural network law is incorporated into the designed controller to reduce the influence of the coefficients estimation error on the control performance. The results of the simulations indicate that the developed control scheme is effective in robotic manipulator control.

• Journal of Power Electronics
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• 제18권5호
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• pp.1398-1408
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• 2018

In order to further improve the steady-state control performance of model predictive torque control (MPTC), a double-vector-based model predictive torque control without a weighting factor is proposed in this paper. The extended voltage vectors synthesized by two basic voltage vectors are used to increase the number of feasible voltage vectors. Therefore, the control precision of the torque and the stator flux along with the steady-state performance can be improved. To avoid testing all of the feasible voltage vectors, the solution of deadbeat torque control is calculated to predict the reference voltage vector. Thus, the candidate voltage vectors, which need to be evaluated by a cost function, can be reduced based on the sector position of the predicted reference voltage vector. Furthermore, a cost function, which only includes a reference voltage tracking error, is designed to eliminate the weighting factor. Moreover, two voltage vectors are applied during one control period, and their durations are calculated based on the principle of reference voltage tracking error minimization. Finally, the proposed method is tested by simulations and experiments.

• 생물환경조절학회지
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• 제23권3호
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• pp.181-191
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• 2014

Modern commercial greenhouse requires the use of advanced climate control system to improve crop production and to reduce energy consumption. As an alternative to classical sensor-based control method, this paper introduces a model-based control method that consists of two models: the predictive model and the evaluation model. As a first step, this paper presents straightforward models to predict the effect of natural ventilation in a greenhouse according to meteorological factors, such as outdoor air temperature, soil temperature, solar radiation and mean wind speed, and structural factor, opening rate of roof ventilators. A multiple regression analysis was conducted to develop the predictive models on the basis of data obtained by computational fluid dynamics (CFD) simulations. The output of the models are air temperature drops due to ventilation at 9 sub-volumes in the greenhouse and individual volumetric ventilation rate through 6 roof ventilators, and showed a good agreement with the CFD-computed results. The resulting predictive models have an advantage of ensuring quick and reasonable predictions and thereby can be used as a part of a real-time model-based control system for a naturally ventilated greenhouse to predict the implications of alternative control operation.

• Structural Engineering and Mechanics
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• 제26권4호
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• pp.377-392
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• 2007

A discrete sliding mode control (SMC) method based on hybrid model of neural network and nominal model is proposed to reduce the vibration of flexible structures, which is a robust active controller developed by using a sliding manifold approach. Since the thick boundary layer will reduce the virtue of SMC, the multilayer feed-forward neural network is adopted to model the uncertainty part. The neural network is trained by Levenberg-Marquardt backpropagation. The design objective of the sliding mode surface is based on the quadratic optimal cost function. In course of running, the input signal of SMC come from the hybrid model of the nominal model and the neural network. The simulation shows that the proposed control scheme is very effective for large uncertainty systems.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1146-1151
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• 2005

This paper deals with the control problem of a natural circulation water tube boiler with constraint conditions. Some linearized models for the water tube boiler are proposed around some operating points, and the model based predictive control law is adopted to control the plant accounting for constraints. In this controller, the Kalman filter is used for the state estimation, and the controller is designed based on the linearized model. The control performance of the designed controller is exemplified via some nonlinear simulations around the operation point, which show it works well.

• 제어로봇시스템학회논문지
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• 제10권5호
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• pp.385-394
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• 2004

This paper proposes the generalized structure of a model-based disturbance rejection controller called a Robust Internal-loop Compensator (RIC). The framework consists of the RIC in the internal-loop to eliminate disturbances and a feedback controller in the external-loop to achieve nominal control performance. As the main contribution of this paper, we redefine the design problem of the RIC as a regulation control problem, then show that this new definition with the RIC structure provides more design flexibility and less implementation constraints. This is verified through a comparative structural analysis with Disturbance Observer (DOB) and Adaptive Robust Control (ARC). Two design examples of the RIC are given, along with practical issues that should be considered in the design procedure. The proposed framework is demonstrated by simulations of a rotary-type motor and experiments with a linear-type motor system.

• Journal of Mechanical Science and Technology
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• 제19권7호
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• pp.1414-1423
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• 2005

This paper develops and evaluates several strategies for structural vibration control with the objective of attenuating broadband noise inside a rectangular enclosure. The strategies evaluated include model-independent collocated control, model-based feedback control and a new 'modal-estimate' feedback strategy. Collocated control requires no knowledge of model parameters and enjoys the advantage of robustness. However, effective broadband noise attenuation with colocated control requires a large number of sensor-actuator pairs. Model-based con-trollers, on the other hand, can be theoretically effective even with the use of a single actuator. However, they suffer from a lack of robustness and are unsuitable from a practical point of view for broadband structural vibration applications where the dynamic models are of large order and poorly known. A new control strategy is developed based on attenuating a few structural vibration modes that have the best coupling with the enclosure acoustics. Broadband attenuation of these important modes can be achieved using a single actuator, a limited number of accelerometers and limited knowledge of a few modal functions. Simulation results are presented to demonstrate the effectiveness of the developed strategy.

• 제어로봇시스템학회논문지
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• 제22권5호
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• pp.339-345
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• 2016

This paper presents a robust speed control of an autonomous vehicle using a disturbance observer. For the purpose, the transfer function of speed dynamics of an autonomous vehicle is identified using step response data. Based on the identified transfer function, model based PID (Proportional-Integral-Derivative) control is designed. In order to design a robust control against load variations on the vehicle, a disturbance observer (DOB) based control is devised. The performance of the designed DOB based control is demonstrated by real experiments.

• International Journal of Control, Automation, and Systems
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• 제6권1호
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• pp.24-34
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• 2008

A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.