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IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-pacific)

作者： Du, Xinxin Tan, Kok Kiong Natl Univ Singapore Dept Elect & Cornputer Engn Singapore 117548 Singapore

ISBN: (纸本)9781509012725

To apply model predictive control (MPC) to autonomous vehicle, certain compromise has to be made. To avoid this, we propose a nonlinear MPC (NMPC) controller, controlling vehicle velocity and steering simultaneously. The optimization solver is based on genetic algorithms, which provides a flexible structure to design the cost function and constraints in a more accurate and straightforward way. On-field tests howed the NMPC can control the vehicle to follow the road accurately and consistently. Moreover, passengers' safety and comfort were well taken care of as both the vehicle movement and steering accelerations are confined explicitly in the N MPC constraints.

关键词： Nonlinear model predictive control genetic algorithms multi-input multi-output system path following

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Modeling of Elastic Robotic Arm using a Soft-Computing Algorithm 8

Modeling of Elastic Robotic Arm using a Soft-Computing Algor...

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8th Vienna International Conference on Mathematical Modelling

作者： Tamimi, Hammam Soeffker, Dirk Univ Duisburg Essen Chair Dynam & Control Duisburg Germany

This paper proposes the use of the least square support vector machine (LS-SVM) algorithm to model an elastic robotic arm. Dynamic system modeling is important as the first step in obtaining a suitable controller for any system. Acquiring an accurate model of elastic robotic based on input-output measurements using the LS-SVM algorithm requires less knowledge about the physical-laws of the system. The LS-SVM algorithm achieves global, unique solution, and requires less training time compared with other soft computing algorithms. In this paper, a successful use of the LS-SVM algorithm to model the elastic robotic arm as multi-input multi-output system is demonstrated. The simulation results illustrate the efficiency and high performance of the proposed approach.

关键词： system identification multi-input multi-output system soft-computing algorithm support vector machine multi-step ahead perdition

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Modeling of Elastic Robotic Arm using a Soft-Computing Algorithm

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IFAC-PapersOnLine 2015年第1期48卷 655-656页

作者： Hammam Tamimi Dirk Söffker Chair of Dynamics and Control University of Duisburg-Essen Duisburg Germany

关键词： system identification multi-input multi-output system soft-computing algorithm support vector machine multi-step ahead perdition

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multivariable fuzzy adaptive predictive functional control for pass temperature balance of the ethylene cracking furnace

Multivariable fuzzy adaptive predictive functional control f...

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第26届中国过程控制会议（CPCC2015）

作者： Huiyuan Shi Chengli Su Ping Li Jiangtao Cao Guocai Zhong Jianping Liang School of Information and Control Engineering Liaoning Shihua University Petro China Sichuan Petrochemical Company Limited

This paper presents a multivariable fuzzy adaptive predictive functional control(MFAPFC) algorithm based on Takagi-Sugeno(T-S) model for multi-input multi-output(MIMO) system. Firstly, the structure parameters of T-S fuzzy model are confirmed, and the model consequent parameters are identified online using the recursive weighting least square method in order to obtain the precise predictive model and offset for the effect on system performance under model mismatch. Secondly, the nonlinear system is linearized by the identified T-S models and then transformed into time-varying state space model. Finally, the linear predictive functional control is adopted to compute the control law, in which the predictive output error between process output and model predictive output is optimized by improved error compensation, a novel adjusting factor in the performance index is introduced to improve the robustness of system and the future control variable is expressed by the step method. Application results on the pass temperature balance of the ethylene cracking furnace show the proposed control strategy is of strong tracking ability and robustness.

关键词： Adaptive predictive functional control T-S model multi-input multi-output system Pass temperature balance Ethylene cracking furnace Paper Instruction CPCC

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Contribution of state modelling in efficient MAP symbol-by-symbol demodulation schemes for CPM-MIMO systems

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SIGNAL PROCESSING 2014年第partb期96卷 420-432页

作者： Boujemaa, R. Amara Marcos, S. Tunis El Manar Univ Natl Engn Sch Tunis Signals & Syst Unit Tunis Tunisia Carthage Univ Ctr Urbain Nord Natl Inst Appl Sci & Technol Carthage Tunisia CNRS Supelec Signals & Syst Lab Gif Sur Yvette France

In [1], a state space model was derived for the demodulation of Continuous Phase Modulation (CPM) signals, based on which the demodulation problem was solved through the symbol-by-symbol Bayesian estimation built around the MAP Symbol-by-symbol Detector (MAPSD). In this paper, a new state space model considered in the augmented state composed of the symbol and the phase state is proposed and the corresponding modified MAPSD demodulation scheme is presented. The main contribution of the paper however consists in deriving optimal and suboptimal symbol-by-symbol MAP detection schemes for MIMO systems operating with CPM signals. For this, a state model description of the corresponding demodulation problem is introduced based on which two CPM-MIMO Bayesian demodulators are proposed. The first one uses a Zero Forcing (ZF) pre-processing block to separate the different CPM signals followed by a bank of MAPSD based CPM demodulators. The second demodulator consists in a joint decision feedback (DF) CPM-MIMO MAPSD detector. Simulations confirm the good performance in term of BER of both proposed structures. Particularly, high BER's performance of the partially joint CPM-MIMO-MAPSD/DF is recorded and an emphasis is made on the implementation simplicity of this new detector with no constraint on the modulation index or the alphabet size. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Continuous phase modulation Maximum a posteriori Bayesian estimation/detection State space model Optimal Bayes filtering multi-input multi-output system

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Development of a Genetic Algorithm based Nonlinear Model Predictive Scheme

Development of a Genetic Algorithm based Nonlinear Model Pre...

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Asian Control Conference

作者： Xinxin Du Kok Kiong Tan Kyaw Ko Ko Htet Department of Electrical and Computer Engineering National University of Singapore

ISBN: (纸本)9781479978632

Model predictive controller (MPC) has demonstrated its competency in controlling autonomous vehicles. But to apply the current MPC-based schemes, certain compromise or approximations have to be made in order to fit the optimization solvers, e.g. linearizing the nonlinear vehicle model. To eliminate the gaps, in this paper, we propose a nonlinear MPC controller which controls the vehicle velocity and steering simultaneously. The optimization solver is based on genetic algorithms (GA), which provides a flexible structure to design the cost function and constraints in a more accurate, meaningful and direct way. The simulation results showed that the vehicle under the control of the proposed nonlinear MPC is able to follow the road center line accurately and consistently, even at sharp corners. The average distance deviation from the road center is 18.4cm and moving direction deviation from the road tangent is 0.041rad. Moreover, the simulation results also showed that passengers' safety and comfort can be well taken care of under the proposed MPC scheme as both the vehicle movement acceleration and steering acceleration are well confined within a safety range. The promising results from the simulation indicate that the proposed GA based nonlinear MPC can be a suitable solution to autonomous vehicle control.

关键词： Nonlinear model predictive control Genetic algorithms Nonlinear optimization multi-input multi-output system Autonomous vehicle Path following

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Optimum Coverage/Capacity in MIMO Indoor Channel with Minimum Transmitters

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WIRELESS PERSONAL COMMUNICATIONS 2013年第3期71卷 1989-2001页

作者： Talepour, Zeinab Ahmadi-Shokouh, Javad Tavakoli, Saeed Univ Sistan & Baluchestan Sch Elect & Comp Engn Zahedan Iran

In this paper, we consider a transmitter location finding problem for the best coverage and capacity in an indoor multi-input multi-output (MIMO) channel. Hence, a multi-objective optimization problem is solved in order to achieve the above targets. A weighted combination of Receive Signal Strength (RSS) and channel capacity is defined as fitness function for this optimization problem. The proposed optimization method contains a jointly designed Ray Tracing Engine (RTE) and Genetic Algorithm (GA). The algorithm is applied on a sample environment which is fully simulated with RTE. The results show that the proposed method perfectly finds the best locations of the minimum number of MIMO transmitters at different weights for RSS and channel capacity.

关键词： multi-input multi-output system Ray-tracing Genetic algorithm Received signal strength Channel capacity Wireless local area network

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FEEDFORWARD NEURAL NETWORK AND FEEDBACK LINEARIZATION CONTROL DESIGN OF BILSAT-1 SATELLITE system

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2012年第10A期8卷 6921-6943页

作者： Chien, Ting-Li WuFeng Univ Dept Elect Engn Minhsiung 62153 Chiayi County Taiwan

The paper presents a novel feedforward neural network and feedback linearization control of BILSAT-1 satellite system for the almost disturbance decoupling performance. The proposed controller guarantees exponentially global uniform ultimate bounded stability and the almost disturbance decoupling performance without using any learning or adaptive algorithms. The proposed approach provides the architecture of the neural network and the weights among the layers in order to guarantee stability of the system. Moreover, the new approach renders the system to be stable with the almost disturbance decoupling property at each step of selecting weights to enhance the performance if the proposed sufficient conditions are maintained. One example, which cannot be solved by the first paper on the almost disturbance decoupling problem, is proposed in this paper to exploit the fact that the tracking and the almost disturbance decoupling performances are easily achieved by the proposed approach.

关键词： BILSAT-1 satellite system Feedforward neural network Almost disturbance decoupling multi-input multi-output system Feedback linearization approach Composite Lyapunov approach

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Efficient Pilot Patterns and Channel Estimations for MIMO-OFDM systems

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IEEE TRANSACTIONS ON BROADCASTING 2012年第4期58卷 648-653页

作者： Baek, Jong-Seob Seo, Jong-Soo Yonsei Univ Dept Elect & Elect Engn Seoul 120749 South Korea

In this paper, we present the efficient pilot patterns and channel estimations for a high-rate multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system. In particular, we propose the scattered pilot (SP), edge pilot (EP), and continuous pilot (CP) by exploiting a basic orthogonal property of a unitary matrix. From the orthogonal property, we then propose the efficient channel frequency response (CFR) and channel impulse response (CIR) estimations. Finally, we evaluate the efficiency of the proposed schemes by comparing with a multi-input single-output OFDM (MISO-OFDM) system.

关键词： Channel estimation multi-input multi-output system orthogonal frequency division multiplexing pilot pattern

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A self-tuning fuzzy controller for a class of multi-input multi-output nonlinear systems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2011年第2期24卷 238-250页

作者： Xu, Chengying Shin, Yung C. Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA Univ Cent Florida Dept Mech Mat & Aerosp Engn Orlando FL 32816 USA

This paper presents a systematic design procedure of a multivariable fuzzy controller for a general multi-input multi-output (MIMO) nonlinear system with an input-output monotonic relationship or a piecewise monotonic relationship for each input-output pair. Firstly, the system is modeled as a Fuzzy Basis Function Network (FBFN) and its Relative Gain Array (RGA) is calculated based on the obtained fuzzy model. The proposed multivariable fuzzy controller is constructed with two orthogonal fuzzy control engines. The horizontal fuzzy control engine for each system input-output pair has a hierarchical structure to update the control parameters online and compensate for unknown system variations. The perpendicular fuzzy control engine is designed based on the system RGA to eliminate the multivariable interaction effect. The resultant closed-loop fuzzy control system is proved to be passive stable as long as the augmented open-loop system is input-output passive. Two sets of simulation examples demonstrate that the proposed fuzzy control strategy can be a promising way in controlling multivariable nonlinear systems with unknown system uncertainties and time-varying parameters. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Fuzzy control Self-tuning systems multi-input multi-output system Relative gain array Non-linear systems

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