• 제목/요약/키워드: Predictive optimal control

검색결과 146건 처리시간 0.046초

Capacity Firming for Wind Generation using One-Step Model Predictive Control and Battery Energy Storage System

  • Robles, Micro Daryl;Kim, Jung-Su;Song, Hwachang
    • Journal of Electrical Engineering and Technology
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    • 제12권5호
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    • pp.2043-2050
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    • 2017
  • This paper presents two MPC (Model Predictive Control) based charging and discharging algorithms of BESS (Battery Energy Storage System) for capacity firming of wind generation. To deal with the intermittency of the output of wind generation, a single BESS is employed. The proposed algorithms not only make the output of combined systems of wind generation and BESS track the predefined reference, but also keep the SoC (State of Charge) of BESS within its physical limitation. Since the proposed algorithms are both presented in simple if-then statements which are the optimal solutions of related optimization problems, they are both easy to implement in a real-time system. Finally, simulations of the two strategies are done using a realistic wind farm library and a BESS model. The results on both simulations show that the proposed algorithms effectively achieve capacity firming while fulfilling all physical constraints.

외란 관측기를 이용한 모델 예견 기반의 전지형 크레인 자동조향 제어알고리즘 개발 (Development of an Automatic Steering-Control Algorithm based on the MPC with a Disturbance Observer for All-Terrain Cranes)

  • 오광석;서자호
    • 드라이브 ㆍ 컨트롤
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    • 제14권2호
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    • pp.9-15
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    • 2017
  • The steering systems of all-terrain cranes have been developed with various control strategies for the stability and drivability. To optimally control the input steering angle, an accurate mathematical model that represents the actual crane dynamics is required. The derivation of an accurate mathematical model to optimally control the steering angle, however, is difficult since the steering-control strategy generally varies with the magnitude of the crane's longitudinal velocity, and the postures of the crane's working parts vary while it is being driven. To address this problem, this paper proposes an automatic steering-control algorithm that is based on the MPC (model predictive control) with a disturbance observer for all-terrain cranes. The designed disturbance observer of this study was used to estimate the error between the base steering model and the actual crane. A model predictive controller was used for the computation of the optimal steering angle, along with the use of the base steering model with an estimated uncertainty. Performance evaluations of the designed control algorithms were conducted based on a curved-path scenario in the Matlab/Simulink environment. The performance-evaluation results show a sound reference-path-tracking performance despite the large uncertainties.

A novel multi-feature model predictive control framework for seismically excited high-rise buildings

  • Katebi, Javad;Rad, Afshin Bahrami;Zand, Javad Palizvan
    • Structural Engineering and Mechanics
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    • 제83권4호
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    • pp.537-549
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    • 2022
  • In this paper, a novel multi-feature model predictive control (MPC) framework with real-time and adaptive performances is proposed for intelligent structural control in which some drawbacks of the algorithm including, complex control rule and non-optimality, are alleviated. Hence, Linear Programming (LP) is utilized to simplify the resulted control rule. Afterward, the Whale Optimization Algorithm (WOA) is applied to the optimal and adaptive tuning of the LP weights independently at each time step. The stochastic control rule is also achieved using Kalman Filter (KF) to handle noisy measurements. The Extreme Learning Machine (ELM) is then adopted to develop a data-driven and real-time control algorithm. The efficiency of the developed algorithm is then demonstrated by numerical simulation of a twenty-story high-rise benchmark building subjected to earthquake excitations. The competency of the proposed method is proven from the aspects of optimality, stochasticity, and adaptivity compared to the KF-based MPC (KMPC) and constrained MPC (CMPC) algorithms in vibration suppression of building structures. The average value for performance indices in the near-field and far-field (El earthquakes demonstrates a reduction up to 38.3% and 32.5% compared with KMPC and CMPC, respectively.

Attitude Control of Planar Space Robot based on Self-Organizing Data Mining Algorithm

  • Kim, Young-Woo;Matsuda, Ryousuke;Narikiyo, Tatsuo;Kim, Jong-Hae
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2005년도 ICCAS
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    • pp.377-382
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    • 2005
  • This paper presents a new method for the attitude control of planar space robots. In order to control highly constrained non-linear system such as a 3D space robot, the analytical formulation for the system with complex dynamics and effective control methodology based on the formulation, are not always obtainable. In the proposed method, correspondingly, a non-analytical but effective self-organizing modeling method for controlling a highly constrained system is proposed based on a polynomial data mining algorithm. In order to control the attitude of a planar space robot, it is well known to require inputs characterized by a special pattern in time series with a non-deterministic length. In order to correspond to this type of control paradigm, we adopt the Model Predictive Control (MPC) scheme where the length of the non-deterministic horizon is determined based on implementation cost and control performance. The optimal solution to finding the size of the input pattern is found by a solving two-stage programming problem.

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Nash equilibrium-based geometric pattern formation control for nonholonomic mobile robots

  • Lee, Seung-Mok;Kim, Hanguen;Lee, Serin;Myung, Hyun
    • Advances in robotics research
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    • 제1권1호
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    • pp.41-59
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    • 2014
  • This paper deals with the problem of steering a group of mobile robots along a reference path while maintaining a desired geometric formation. To solve this problem, the overall formation is decomposed into numerous geometric patterns composed of pairs of robots, and the state of the geometric patterns is defined. A control algorithm for the problem is proposed based on the Nash equilibrium strategies incorporating receding horizon control (RHC), also known as model predictive control (MPC). Each robot calculates a control input over a finite prediction horizon and transmits this control input to its neighbor. Considering the motion of the other robots in the prediction horizon, each robot calculates the optimal control strategy to achieve its goals: tracking a reference path and maintaining a desired formation. The performance of the proposed algorithm is validated using numerical simulations.

슬라이딩 모드 및 모델 예측 직렬형 제어기를 이용한 영구자석형 동기전동기의 속도제어 (Velocity Control of Permanent Magnet Synchronous Motors using Model Predictive and Sliding Mode Cascade Controller)

  • 이일로;이영우;신동훈;정정주
    • 제어로봇시스템학회논문지
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    • 제21권9호
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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.

Model Predictive Torque Control of Surface Mounted Permanent Magnet Synchronous Motor Drives with Voltage Cost Functions

  • Zhang, Xiaoguang;Hou, Benshuai;He, Yikang;Gao, Dawei
    • Journal of Power Electronics
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    • 제18권5호
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    • pp.1369-1379
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    • 2018
  • In this paper, a model predictive torque control (MPTC) without the use of a weighting factor for surface mounted permanent-magnet synchronous machine (SPMSM) drive systems is presented. Firstly, the desired voltage vector is predicted in real time according to the principles of deadbeat torque and flux control. Then the sector of this desired voltage vector is determined. The complete enumeration for testing all of the feasible voltage vectors is avoided by testing only the candidate vectors contained in the sector. This means that only two voltage vectors in the sector need to be tested for selecting the optimal voltage vector in each control period. Thus, the calculation time can be reduced when compared with the conventional enumeration method. On the other hand, a novel cost function that only includes the dq-axis voltage errors between the desired voltage and candidate voltage is designed to eliminate the weighting factor used in the conventional MPTC. Thus, the control complexity caused by the tuning of the weighting factor is effectively decreased when compared with the conventional MPTC. Simulation and experimental investigation have been carried out to verify the proposed method.

Predictive Current Control for Multilevel Cascaded H-Bridge Inverters Based on a Deadbeat Solution

  • Qi, Chen;Tu, Pengfei;Wang, Peng;Zagrodnik, Michael
    • Journal of Power Electronics
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    • 제17권1호
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    • pp.76-87
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    • 2017
  • Finite-set predictive current control (FS-PCC) is advantageous for power converters due to its high dynamic performance and has received increasing interest in multilevel inverters. Among multilevel inverter topologies, the cascaded H-bridge (CHB) inverter is popular and mature in the industry. However, a main drawback of FS-PCC is its large computational burden, especially for the application of CHB inverters. In this paper, an FS-PCC method based on a deadbeat solution for three-phase zero-common-mode-voltage CHB inverters is proposed. In the proposed method, an inverse model of the load is utilized to calculate the reference voltage based on the reference current. In addition, a cost function is directly expressed in the terms of the voltage errors. An optimal control actuation is selected by minimizing the cost function. In the proposed method, only three instead of all of the control actuations are used for the calculations in one sampling period. This leads to a significant reduction in computations. The proposed method is tested on a three-phase 5-level CHB inverter. Simulation and experimental results show a very similar and comparable control performance from the proposed method compared with the traditional FS-PCC method which evaluates the cost function for all of the control actuations.

Anti-Sway Position Control of an Automated Transfer Crane Based on Neural Network Predictive PID Controller

  • Suh Jin-Ho;Lee Jin-Woo;Lee Young-Jin;Lee Kwon-Soon
    • Journal of Mechanical Science and Technology
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    • 제19권2호
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    • pp.505-519
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    • 2005
  • In this paper, we develop an anti-sway control in proposed techniques for an ATC system. The developed algorithm is to build the optimal path of container motion and to calculate an anti-collision path for collision avoidance in its movement to the finial coordinate. Moreover, in order to show the effectiveness in this research, we compared NNP PID controller to be tuning parameters of controller using NN with 2 DOF PID controller. The simulation and experimental results show that the proposed control scheme guarantees performances, trolley position, sway angle and settling time in NNP PID controller than other controller. As the results in this paper, the application of NNP PID controller is analyzed to have robustness about disturbance which is wind of fixed pattern in the yard. Accordingly, the proposed algorithm in this study can be readily used for industrial applications.

An Automatic Travel Control of a Container Crane using Neural Network Predictive PID Control Technique

  • Suh Jin-Ho;Lee Jin-Woo;Lee Young-Jin;Lee Kwon-Soon
    • International Journal of Precision Engineering and Manufacturing
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    • 제7권1호
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    • pp.35-41
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    • 2006
  • In this paper, we develop anti-sway control in proposed techniques for an ATC system. The developed algorithm is to build the optimal path of container motion and to calculate an anti-collision path for collision avoidance in its movement to the finial coordinate. Moreover, in order to show the effectiveness in this research, we compared NNP PID controller to be tuning parameters of controller using NN with 2-DOF PID controller. The experimental results jar an ATC simulator show that the proposed control scheme guarantees performances, trolley position, sway angle, and settling time in NNP PID controller than other controller. As a result, the application of NNP PID controller is analyzed to have robustness about disturbance which is wind of fixed pattern in the yard.