• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.10
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• pp.1-11
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• 1996

In this paper, a robust receding horizon control algorithm, which can be employed for a wide class of nonlinear systems with control and state constraints, modeling errors, and disturbances, is considered. In a neighborhood of the origin, a linear feedback controlelr for the linearized system is applied. Outside this neighborhood, a receding horizon control is applied. Robust stability is proved considering the time taken to solve an optimal control problem so that the proposed algorithm can be applied as an on-line controller.

• Wind and Structures
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• v.2 no.3
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• pp.189-200
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• 1999

In this paper, a new algorithm for active control design of structures is proposed and investigated. The algorithm preserves the decoupling property of the modal vibration equation and eliminates the spillover problem, which is the main shortcoming in the independent modal space control(IMSC) algorithm. With linear quadratic regulator(LQR) control law, the analytical solution of algebraic Riccati equation and the optimal actuator control force are obtained, and the control design procedure is significantly simplified. A numerical example for the control design of a tall building subjected to wind loads demonstrates the effectiveness of the proposed algorithm in reducing the acceleration and displacement responses of tall buildings under wind actions.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.175-186
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• 2021

We have studied the speed regulation of the permanent magnet synchronous motor (PMSM) servo system in this paper. To optimize the PMSM servo system's speed-control performance with disturbances, a non-linear speed-control technique using a back-propagation neural network (BP-NN) algorithm forthe controller design of the PMSM speed loop is introduced. To solve the slow convergence speed and easy to fall into the local minimum problem of BP-NN, we develope an improved BP-NN control algorithm by limiting the range of neural network outputs of the proportional coefficient Kp, integral coefficient Ki of the controller, and add adaptive gain factor β, that is the internal gain correction ratio. Compared with the conventional PI control method, our improved BP-NN control algorithm makes the settling time faster without static error, overshoot or oscillation. Simulation comparisons have been made for our improved BP-NN control method and the conventional PI control method to verify the proposed method's effectiveness.

• Smart Structures and Systems
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• v.10 no.1
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• pp.15-31
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• 2012

This study proposes an active control method to suppress beam-rotating machinery system vibrations. The present control method is a combination of the fuzzy input estimation method (FIEM) and linear quadratic Gaussian problem (LQG) algorithms. The FIEM can estimate the unknown input and optimal states by measuring the dynamic displacement, the optimal estimated states into the feedback control; thereby obtaining the optimal control force for a random linear system. Active vibration control of a beam-rotating machinery system is performed to verify the feasibility and effectiveness of the proposed algorithm. The simulation results demonstrate that the proposed method can suppress vibrations in a beam-machine system more efficiently than the conventional LQG method.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.622-627
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• 2014

This paper proposes a new method which improves recognition rate on the gait recognition system using wavelet transform, linear discriminant analysis and genetic algorithm. We use wavelet transform to obtain the four sub-bands from the gait energy image. In order to extract feature data from sub-bands, we use linear discriminant analysis. Distance values between training data and four sub-band data are calculated and four weights which are calculated by genetic algorithm is assigned at each sub-band distance. Based on a new fusion distance value, we conducted recognition experiments using k-nearest neighbors algorithm. Experimental results show that the proposed weight fusion method has higher recognition rate than conventional method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.325-329
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• 2003

This paper discuss the method of the system's efficient control using a Intelligent hybrid algorithm in nonlinear dynamics systems. Existing neural network and genetic algorithm for the control of non-linear systems work well in static states. but it be not particularly good in changeable states and must re-learn for the control of the system in the changed state. This time spend a lot of time. For the solution of this problem we suggest the intelligent hybrid self-tuning controller. it includes neural network, genetic algorithm and immune system. it is based on neural network, and immune system and genetic algorithm are added against a changed factor. We will call a change factor an antigen. When an antigen broke out, immune system come into action and genetic algorithm search an antibody. So the system is controled more stably and rapidly. Moreover, The Genetic algorithm use the memory address of the immune bank as a genetic factor. So it brings an advantage which the realization of a hardware easy.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.44-53
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• 2008

An interacting multiple model (IMM) estimation algorithm based on the mixing of the predicted state estimates is proposed in this paper for a right continuous jump-linear system model different from the left-continuous system model used to develop the existing IMM algorithm. The difference lies in the modeling of the mode switching time. Performance of the proposed algorithm is compared numerically with that of the existing IMM algorithm for noisy system identification. Based on the numerical analysis, the proposed algorithm is applied to target tracking with a large sampling period for performance comparison with the existing IMM.

• International Journal of Precision Engineering and Manufacturing
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• v.9 no.3
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• pp.40-45
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• 2008

This paper presents a linear positioning system and its control algorithm design with nano accuracy/resolution. The basic linear stage structure is driven by an ultrasonic motor and its displacement feedback is detected by a LDGI (Laser Diffraction Grating Interferometer), which can achieve nanometer resolution. Due to the friction driving property of the ultrasonic motor, the driving situation differs in various ranges along the travel. Experiments have been carried out in order to observe and realize the phenomena of the three main driving modes: AC mode (for mm motion), Gate mode (for ${\mu}m$ motion), and DC mode (for nm motion). A proposed FCMAC (Fuzzy Cerebella Model Articulation Controller) control algorithm is implemented for manipulating and predicting the velocity variation during the motion of each mode respectively. The PCbased integral positioning system is built up with a NI DAQ Device by a BCB (Borland $C^{++}$ Builder) program to accomplish the purpose of an intelligent nanopositioning control.

• Journal of Power System Engineering
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• v.12 no.1
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• pp.72-79
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• 2008

This paper designs a looper-tension controller for mass-flow stabilization in hot strip finishing mills. By Newton's 2nd law and Hooke's law, nonlinear dynamic equations on the looper-tension system are firstly derived, and linearized by a linearization algorithm using a Taylor's series expansion. Moreover, a tension calculation model is obtained from the nonlinear dynamic equations which is called as a soft sensor of strip tension between two neighboring stands. Next, a looper-tension servo controller is designed by an ILQ(Inverse Linear Quadratic optimal control) algorithm, and it is combined with a minimal disturbance observer which to attenuate speed disturbances by AGC and operator interventions, etc.. Finally, it is shown from by a computer simulation that the proposed ILQ controller with a disturbance observer is very effective in stabilizing the strip mass-flow under some disturbances, moreover it has a good command following performance.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.8
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• pp.616-622
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• 2003

This paper deals with the problem of estimating the asymptotic stability region(ASR) of uncertain variable structure systems with bounded controllers. Using linear matrix inequalities(LMIs) we estimate the ASR and show the exponential stability of the closed-loop control system in the estimated ASR. We give a simple LMI-based algorithm to get estimates of the ASR. We also give a synthesis algorithm to design a switching surface which will make the estimated ASR big. Finally, we give numerical examples in order to show that our method can give better results than the previous ones for a certain class of uncertain variable structure systems with bounded controllers.