• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2189-2196
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• 2016

This paper proposes a new finite control set-model predictive control (FCS-MPC) method for induction motors. In the method, the reference state that satisfies the given torque and rotor flux requirements is derived. Cost indices for the FCS-MPC are defined using the state tracking error, and a linear matrix inequality is formulated to obtain a proper weighting matrix for the state tracking error. The on-line procedure of the proposed FCS-MPC comprises of two steps: select the output voltage vector of the two level inverter minimizing the cost index and compute the optimal modulation factor of the minimizing output voltage vector in order to reduce the state tracking error and torque ripple. The steady state tracking error is removed by using an integrator to adjust the reference state. The simulation and experimental results demonstrated that the proposed FCS-MPC shows good torque, rotor flux control performances at different rotating speeds.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.801-806
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• 2015

In this paper, we propose cascade-form velocity controller for a permanent magnet synchronous motor (PMSM). The proposed controller consists of a sliding-mode controller (SMC) for the inner current control loop and a model-predictive controller (MPC) for the outer velocity control loop. With SMC, we can ensure that the current tracking error always converges to zero in finite time. The SMC is designed to track the desired currents. Additionally, with MPC, we can obtain the optimal velocity control input which minimizes the cost function. Constraint conditions for input and input variation are included in the MPC design. The simulation results are included to validate the performance of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1901-1904
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• 2003

MPC or model predictive control is representative of control methods which are able to handle physical constraints. Closed-loop stability can therefore be ensured only locally in the presence of constraints of this type. However, if the system is neutrally stable, and if the constraints are imposed only on the input, global aymptotic stability can be obtained; until recently, use of infinite horizons was thought to be inevitable in this case. A globally stabilizing finite-horizon MPC has lately been suggested for neutrally stable continuous-time systems using a non-quadratic terminal cost which consists of cubic as well as quadratic functions of the state. The idea originates from the so-called small gain control, where the global stability is proven using a non-quadratic Lyapunov function. The newly developed finite-horizon MPC employs the same form of Lyapunov function as the terminal cost, thereby leading to global asymptotic stability. A discrete-time version of this finite-horizon MPC is presented here. The proposed MPC algorithm is also coded using an SQP (Sequential Quadratic Programming) algorithm, and simulation results are given to show the effectiveness of the method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.165-170
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• 2017

This paper presents an event-triggered model predictive controller for adaptive cruise control system with sampled and quantized-data. Unlike existing works, a longitudinal continuous-time model is used for the predictive control of the system. To efficiently utilize network resources, event-trigger scheme is employed, which allows limited sensor and actuator signal satisfying the condition that the measurement of errors is over the ratio of a trigger level. The proposed control gain is obtained by solving a convex problem satisfying several linear matrix inequalities at every sampling times. Simulation results are given to show the effectiveness of the proposed design method.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.451-454
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• 1998

This paper proposes a new method of Model Predictive Control (MPC) of nonlinear process by us-ing the measured Volterra kernels as the nonlinear model. A nonlinear dynamical process is usually de-scribed as Volterra kernel representation, In the authors' method, a pseudo-random M-sequence is ar plied to the nonlinear process, and its output is measured. Taking the crosscorrelation between the input and output, we obtain the Volterra kernels up to 3rd order which represent the nonlinear characteristics of the process. By using the measured Volterra kernels, we can construct the nonlinear model for MPC. In applying Model Predictive Control to a nonlinear process, the most important thing is, in general, what kind of nonlinear model should be used. The authors used the measured Volterra kernels of up to 3rd order as the process model. The authors have carried out computer simulations and compared the simulation results for the linear model, the nonlinear model up to 2nd Volterra kernel, and the nonlinear model up to 3rd order Vol-terra kernel. The results of computer simulation show that the use of Valterra kernels of up to 3rd order is most effective for Model Predictive Control of nonlinear dynamical processes.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1443-1448
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• 2003

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impounded by linear models due to the lack of a similarly accepted nonlinear modelling or data based technique. The authors have recently developed a new method for obtaining Volterra kernels of up to third order by use of pseudorandom M-sequence. By use of this method, nonparametric NMPC is derived in discrete-time using multi-dimensional convolution between plant data and Volterra kernel measurements. This approach is applied to an industrial polymerisation process using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.413-419
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• 1998

A novel order-reduction technique for model predictive control(MPC) is proposed based on the singular value decomposition(SVD) of a pulse response circulant matrix(PRCM) of a concerned system. It is first investigated that the PRCM (in the limit) contains a complete information of the frequency response of a system and its SVD decomposes the information into the respective principal directions at each frequency. This enables us to isolate the significant modes of the system and to devise the proposed order-reduction technique. Though the primary purpose of the proposed technique is to diminish the required computation in MPC, the clear frequency decomposition of the SVD of the PRCM also enables us to improve the robustness through selective excitation of frequency modes. Performance of the proposed technique is illustrated through two numerical examples.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.11
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• pp.106-111
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• 2014

Currently, the commonly used method for PWM(Pulse Width Modulation) Inverter of the Solar Power Plant. However, the limit of the developing performance to the non-linear and switch devices of the Inverter. Therefore, we propose a model predictive control techniques applied to Laguerre functions. LMPC(Laguerre functions model predictive control) reduces the number of computations made and so online implementation becomes possible where traditional MPC would have fail. In this paper, we comment on the appropriate scope and functions degree of the LMPC inverter control. The simulation results from MATLAB are also provided.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.6
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• pp.730-736
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• 2016

Recent research suggests the various applications of Model Predictive Control on vehicle systems. In numerous cases, nonlinear tire models such as the Magic Formula, which are highly complex and are more detailed than necessary, are used. This paper presents a nonlinear tire model that excludes the region of negative slope but expresses the nonlinear properties of tire well enough for tracking the lane of a racing course. The proposed inverse tire model can also be used to calculate the slip angle from the tire force. Thus, the model can be utilized to design the Model Predictive Controller.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.49-52
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• 1999

A subspace-based identification method of the Wiener model, consisting of a state-space linear block and a polynomial static nonlinearity at the output, is used to retrieve from discrete sample data the accurate information about the nonlinear dynamics. Wiener model may be incorporated into model predictive control (MPC) schemes in a unique way which effectively removes the nonlinearity from the control problem, preserving many of the favorable properties of linear MPC. The control performance is evaluated with simulation studies where the original first-principles model for a continuous MMA polymerization reactor is used as the true process while the identified Wiener model is used for the control purpose. On the basis of the simulation results, it is demonstrated that, despite the existence of unmeasured disturbance, the controller performed quite satisfactorily for the control of polymer qualities with constraints.