• Title/Summary/Keyword: MIMO nonlinear systems

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Block Diagonal Decomposition Using Uniform Channel Decomposition for Multicell MIMO Broadcast Channels (다중 셀 MIMO 하향채널에서의 UCD를 이용한 블록 대각 분해)

  • Park, Yu-han;Park, Daeyoung
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.40 no.12
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    • pp.2331-2342
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    • 2015
  • In this paper, we design non-linear transmitter and receiver using a uniform channel decomposition (UCD) to take account of inter-cell interference in multi-cell downlink systems. The designed UCD scheme brings forth the same SINR for all sub-channels in each cell. It provides great convenience to modulation/coding process and achieves the maximum diversity gain. The simulation results confirm that it exhibits a lower BER than the conventional method.

Adaptive model predictive control using ARMA models (ARMA 모델을 이용한 적응 모델예측제어에 관한 연구)

  • 이종구;김석준;박선원
    • 제어로봇시스템학회:학술대회논문집
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    • 1993.10a
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    • pp.754-759
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    • 1993
  • An adaptive model predictive control (AMPC) strategy using auto-regression moving-average (ARMA) models is presented. The characteristic features of this methodology are the small computer memory requirement, high computational speed, robustness, and easy handling of nonlinear and time varying MIMO systems. Since the process dynamic behaviors are expressed by ARMA models, the model parameter adaptation is simple and fast to converge. The recursive least square (RLS) method with exponential forgetting is used to trace the process model parameters assuming the process is slowly time varying. The control performance of the AMPC is verified by both comparative simulation and experimental studies on distillation column control.

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A Direct Adaptive Fuzzy Control of Nonlinear Systems with Application to Robot Manipulator Tracking Control

  • Cho, Young-Wan;Seo, Ki-Sung;Lee, Hee-Jin
    • International Journal of Control, Automation, and Systems
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    • v.5 no.6
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    • pp.630-642
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    • 2007
  • In this paper, we propose a direct model reference adaptive fuzzy control (MRAFC) for MIMO nonlinear systems whose structure is represented by the Takagi-Sugeno fuzzy model. The adaptive law of the MRAFC estimates the approximation error of the fuzzy logic system so that it provides asymptotic tracking of the reference signal for the systems with uncertain or slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal. To verify the validity and effectiveness of the MRAFC scheme, the suggested analysis and design techniques are applied to the tracking control of robot manipulator and simulation studies are carried out. In the control design, the MRAFC is combined with feedforward PD control to make the actual joint trajectories of the robot manipulator with system uncertainties track the desired reference joint position trajectories asymptotically stably.

Multi-Input Multi-Output Nonlinear Autopilot Design for Ship-to-Ship Missiles

  • Im Ki-Hong;Chwa Dong-Kyoung;Choi Jin-Young
    • International Journal of Control, Automation, and Systems
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    • v.4 no.2
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    • pp.255-270
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    • 2006
  • In this paper, a design method of nonlinear autopilot for ship-to-ship missiles is proposed. Ship-to-ship missiles have strongly coupled dynamics through roll, yaw, and pitch channel in comparison with general STT type missiles. Thus it becomes difficult to employ previous control design method directly since we should find three different solutions for each control fin deflection and should verify the stability for more complicated dynamics. In this study, we first propose a control loop structure for roll, yaw, and pitch autopilot which can determine the required angles of all three control fins. For yaw and pitch autopilot design, missile model is reduced to a minimum phase model by applying a singular perturbation like technique to the yaw and pitch dynamics. Based on this model, a multi-input multi-output (MIMO) nonlinear autopilot is designed. And the stability is analyzed considering roll influences on dynamic couplings of yaw and pitch channel as well as the aerodynamic couplings. Some additional issues on the autopilot implementation for these coupled missile dynamics are discussed. Lastly, 6-DOF (degree of freedom) numerical simulation results are presented to verify the proposed method.

A NOVEL MULTI-INPUT MULTI-OUTPUT FUZZY CONTROLLER

  • Huaguang, Zhang;Bien, Zeungnam;Yinguo, Piao
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.06a
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    • pp.194-198
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    • 1998
  • A novel fuzzy basis function vector- based adaptive control approach for Multi-input and Multi-output(MIMO) system is presented in this paper, in which the nonlinear plants is first linearised, the fuzzy basis function vector is then introduced to adaptively learn the upper bound of the system uncertainty vector, and its output is used as the paramenters of the compensator in the sense that both the robustness and the asymptotic error convergence can be obtained for the closed loop nonlinear control system.

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A Study on Design of Neuro- Fuzzy Controller for Attitude Control of Helicopter (헬리콥터 자세제어를 위한 뉴로 퍼지 제어기의 설계에 관한 연구)

  • Choi, Yong-Sun;Lim, Tae-Woo;Jang, Gung-Won;Ahn, Tae-Chon
    • Proceedings of the KIEE Conference
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    • 2001.07d
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    • pp.2283-2285
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    • 2001
  • This paper proposed to a neural network based fuzzy control (neuro-fuzzy control) technique for attitude control of helicopter with strongly dynamic nonlinearities and derived a helicopter aerodynamic torque equation of helicopter and the force balance equation. A neuro-fuzzy system is a feedforward network that employs a back-propagation algorithm for learning purpose. A neuro-fuzzy system is used to identify nonlinear dynamic systems. Hence, this paper presents methods for the design of a neural network(NN) based fuzzy controller(that is, neuro-fuzzy control) for a helicopter of nonlinear MIMO systems. The proposed neuro-fuzzy control determined to a input-output membership function in fuzzy control and neural networks constructed to improve through learning of input-output membership functions determined in fuzzy control.

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Design of Sliding Hyperplanes in Nonlinear Variable Structure Systems with Uncertainties (불확실성을 갖는 비선형 가변구조시스템의 슬라이딩 초평면 설계)

  • 박동원;최승복;김재문
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.18 no.8
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    • pp.1985-1996
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    • 1994
  • A new design method of sliding hyperplanes is proposed in the synthesis of a variable structure controller for robust tracking of general nonlinear multi-input-output(MIMO) uncertain systems of relative degree higher than two. Input/ output(I/O) linearzation is firstly undertaken by employing the concept of relative degree and minimum phase followed by the construction of sliding mode controllers. Sliding hyperplanes are then derived from the inherent properties of companion matrix and ideal sliding mode characterized in I/O linearized system. Subsequently, the gradient magnitudes of the sling hyperplanes are determined in an optimal manner by considering a quadratic performance index to be evaluated at two phases; a reaching phase and a sliding phase. The proposed design methodology is relatively straightforward and systematic compared with conventional strategies such as geometric approach or pole assignment technique. A nonlinear governor and exciter control problem for a power system is adopted herein in order to demonstrate the design efficiency and also favorable and robust control performances.

An Adaptive Joint Precoding for Multi-user MIMO Systems (다중 사용자 MIMO 시스템을 위한 적응적 결합 프리코딩)

  • Park, Ju Yong;Hanif, Mohammad Abu;Song, Sang Seob;Lee, Moon Ho
    • Journal of the Institute of Electronics and Information Engineers
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    • v.51 no.12
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    • pp.3-11
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    • 2014
  • Multiple antennas can provide huge capacity gains when the transmitter knows the channel state information (CSI). Precoding is a technique that exploits CSI at the transmitter side. In this paper, an adaptive precoding scheme is proposed, called a hybrid multiple-input multiple-output (MIMO) precoding (HMP). HMP is a combination of linear and nonlinear precoding. The number of transmit antennas less than or equal to four is as same as the conventional antenna selection scheme. Therefore, the HMP scheme uses more than four transmit antennas. The good channel means that the channels must be selected to maximize the channel capacity among the given channels, and the rest channels are called bad channel. In HMP scheme, we use the nonlinear precoding in the good channels and the linear precoding in the bad channels. The well-known Tomlinson-Harashima precoding (THP) is considered as nonlinear precoding. The system throughput and MSE (minimum square error) are shown for the performance of HMP scheme compared to the conventional schemes which are BD (block diagonalization), antenna selection and THP.

Development of Control Algorithm for Ship Berthing and Unberthing Systems Using a Joystick (조이스틱을 이용한 선박의 입출항 및 접이안 시스템의 제어 알고리즘 개발)

  • Hong, Seong-Kuk;Jung, Yun-Ha;Kim, Sun-Young;Won, Moon-Cheol
    • Journal of Navigation and Port Research
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    • v.31 no.5 s.121
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    • pp.325-332
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    • 2007
  • This study develops a control algorithm on berthing/unberthing system using a joystick for ships with thrusters and a rudder. A nonlinear mathematical model for low speed maneuvering of typical container ships is used to develop a MIMO(multi-input multi-output) nonlinear control algorithm for velocity feedback joystick control. Also a virtual HILS(hardware in the loop simulation) software program for berthing/unberthing is developed to test the performance of the nonlinear and a PID control algorithm. The program is developed using LabWindow/CVI, and a user can see current position and desired trajectory of ship in a monitor, then he can control forward and yaw velocities of a ship using a joystick. The simulation results show that the nonlinear mfd the PID controller have superior performance over a simple open loop joystick control algorithm.

Model predictive control combined with iterative learning control for nonlinear batch processes

  • Lee, Kwang-Soon;Kim, Won-Cheol;Lee, Jay H.
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10a
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    • pp.299-302
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    • 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.

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