• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1-4
• /
• 2004

The dual-mode strategy has been adopted in many constrained MPC methods. The size of stabilizable regions of states of MPC methods depends on the size of underlying feasible and positively invariant set and number of control moves. These results, however, could be conservative because the definition of positive invariance does not allow temporal leave of states from the set, In this paper, a concept of periodic invariance is introduced in which states are allowed to leave a set temporarily but return into the set in finite steps. The periodic invariance can defined with respect to sets of different state feedback gains. These facts make it possible for the periodically invariant sets to considerably larger than ordinary invariant sets. The periodic invariance can be defined for systems with polyhedral model uncertainties. We derive a MPC method based on these periodically invariant sets. Some numerical examples are given to show that the use of periodic invariance yields considerably larger stabilizable sets than the case of using ordinary invariance.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.20 no.5
• /
• pp.448-455
• /
• 2015

A model predictive control (MPC) method without individual PWM has been recently researched to simplify and improve the control flexibility of a multilevel inverter. However, the input power of each H-bridge cell and the switching frequency of switching devices are unbalanced because of the use of a restricted switching state in the MPC method. This paper proposes a control method for balancing the switching patterns and cell power supplied from each isolated dc source of a cascaded H-bridge inverter. The supplied dc power from isolated dc sources of each H-bridge cells is balanced with the proposed cell balancing method. In addition, the switching frequency of each switching device of the CHB inverter becomes equal. A simulation and experimental results are presented with nine-level and five-level three-phase CHB inverter to validate the proposed balancing method.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1435-1437
• /
• 2003

In this paper, we proposed static output feedback model predictive control for Wiener models. We adopted polytopic uncertainty description of Wiener Model Predictive Control (WMPC) algorithms for considering output nonlinearities. Robust stability conditions have been presented under which the closed loop stability of static output feedback MPC is guaranteed. The proposed control law is determined from the static output feedback WMPC based on the current estimated state with explicit satisfaction of input constraints.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.3
• /
• pp.1212-1222
• /
• 2018

This paper presents a model predictive control (MPC) method to reduce the common-mode voltage (CMV) for inverters connected in parallel, which increase the capacity of energy storage systems (ESSs). The proposed method is based on subdivided voltage vectors, and the resulting algorithm can be applied to control the inverters. Furthermore, we use more voltage vectors than the conventional MPC algorithm; consequently, the quality of grid currents is improved. Several methods were proposed in order to reduce the CMV appearing during operation and its adverse effects. However, those methods have shown to increase the total harmonic distortion of the grid currents. Our method, however, aims to both avoid this drawback and effectively reduce the CMV. By employing phase difference in the carrier signals to control each inverter, we successfully reduced the CMV for inverters connected in parallel, thus outperforming similar methods. In fact, the validity of the proposed method was verified by simulations and experimental results.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.4
• /
• pp.1484-1494
• /
• 2017

We present a two-stage inverter with high step-up conversion ratio engaging modified finite-set Model Predictive Control (MPC) for utility-integrated photovoltaic (PV) applications. The anticipated arrangement is fit for low power PV uses, the calculated efficiency at 150 W input power and 19 times boosting ratio was around 94%. The suggested high-gain dc-dc converter based on Cockcroft-Walton multiplier constitutes the first-stage of the offered structure, due to its high step-up ability. It can boost the input voltage up to 20 times. The 3S current-source inverter constitutes the second-stage. The 3S current-source inverter hires three semiconductor switches, in which one is functioning at high-frequency and the others are operating at fundamental-frequency. The high-switching pulses are varied in the procedure of unidirectional sine-wave to engender a current coordinated with the utility-voltage. The unidirectional current is shaped into alternating current by the synchronized push-pull configuration. The MPC process are intended to control the scheme and achieve the subsequent tasks, take out the Maximum Power (MP) from the PV, step-up the PV voltage, and introduces low current with low Total Harmonic Distortion (THD) and with unity power factor with the grid voltage.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.313-316
• /
• 1997

Two different issues, design of reduced-order robust model predictive control and input signal design for identification of a MIMO system, are addressed and design techniques based on singular value decomposition(SVD) of the pulse response circulant matrix(PRCM) are proposed. For this, we investigate the properties of the PRCM, which is a periodic approximation of a linear discrete-time system, and show its SVD represents the directional as well as the frequency decomposition of the system. Usefulness of the PRCM and effectiveness of the proposed design techniques are demonstrated through numerical examples.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.1
• /
• pp.19-26
• /
• 2005

This paper is concerned with the design of a predictive PID controller which has similar features to the model-based predictive controller. A PID type control structure is defined, which includes prediction of the outputs and the recalculation of new set points using the future set point data. The optimal values of the PID gains are precalculated using the values of gains calculated from an unconstrained generalized predictive control algorithm. Simulation studies demonstrate the performance of the proposed controller and the results are compared with the conventional PID and fuzzy control algorithms.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.3
• /
• pp.199-209
• /
• 2015

This paper presents a model predictive control (MPC) approach to control the steering angle in an autonomous vehicle. In designing a highly automated driving control algorithm, one of the research issues is to cope with probable risky situations for enhancement of safety. While human drivers maneuver the vehicle, they determine the appropriate steering angle and acceleration based on the predictable trajectories of surrounding vehicles. Likewise, it is required that the automated driving control algorithm should determine the desired steering angle and acceleration with the consideration of not only the current states of surrounding vehicles but also their predictable behaviors. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, we define a safe driving envelope with the consideration of probable risky behaviors among the predicted probable behaviors of surrounding vehicles over a finite prediction horizon. For the control of the vehicle while satisfying the safe driving envelope and system constraints over a finite prediction horizon, a MPC approach is used in this research. At each time step, MPC based controller computes the desired steering angle to keep the subject vehicle in the safe driving envelope over a finite prediction horizon. Simulation and experimental tests show the effectiveness of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• 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.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.5
• /
• pp.562-567
• /
• 2006

Model Predictive Control(MPC) system uses the current input which minimizes the difference between the desired output and the estimated output in the receding horizon scheme. In many cases (for example, system with constraints or nonlinear system), however, it is not easy to find the optimal solution to the above problem. In this paper, we show that real number coding genetic algorithm can be used to solve the optimal problem for MPC effectively. Also, we show by simulation that the real coding algorithm is mote natural and advantageous than the digital coding one.