Qinmin Yang

Zhejiang University, Hang-hsien, Zhejiang Sheng, China

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Publications (42)48.02 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we present a novel tracking controller for a class of uncertain nonaffine systems with time-varying asymmetric output constraints. Firstly, the original nonaffine constrained (in the sense of the output signal) control system is transformed into a output-feedback control problem of an unconstrained affine system in normal form. As a result, stabilization of the transformed system is sufficient to ensure constraint satisfaction. It is subsequently shown that the output tracking is achieved without violation of the predefined asymmetric time-varying output constraints. Therefore, we are capable of quantifying the system performance bounds as functions of time on both transient and steady-state stages. Furthermore, the transformed system is linear with respect to a new input signal and the traditional backstepping scheme is avoided, which makes the synthesis extremely simplified. All the signals in the closed-loop system are proved to be semi-globally, uniformly, and ultimately bounded via Lyapunov synthesis. Finally, the simulation results are presented to illustrate the performance of the proposed controller.
    02/2015; DOI:10.1109/TCYB.2015.2394797
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    ABSTRACT: This paper investigates the optimal charging strategy for a plug-in electric taxi (PET) to maximize its operating profit by choosing proper charging slots, subject to uncertain electricity prices and time-varying incomes. As PET consumes more electricity and possesses different charging behaviors from the widely studied private electric vehicles, this problem deserves special treatment. First, in order to tackle the uncertain electricity prices, a simple thresholding method is proposed to determine the optimal charging slot, where the thresholds are computed via a backward induction algorithm. Then, the properties that reveal the insights of the algorithm are presented. Then, several practical factors are included in algorithm design to approach a more realistic solution, such as an accurate battery model, the additional power consumption of driving PET to charging station, and the battery loss during charging and discharging processes. Numerical results show that the proposed algorithm is able to improve the profit and significantly reduce the expense.
    IEEE Transactions on Power Systems 11/2014; 29(6):3058-3068. DOI:10.1109/TPWRS.2014.2311120 · 3.53 Impact Factor
  • Zaiyue Yang, Qinmin Yang, Youxian Sun
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    ABSTRACT: This brief considers the asymptotic tracking problem for a class of high-order nonaffine nonlinear dynamical systems with nonsmooth actuator nonlinearities. A novel transformation approach is proposed, which is able to systematically transfer the original nonaffine nonlinear system into an equivalent affine one. Then, to deal with the unknown dynamics and unknown control coefficient contained in the affine system, online approximator and Nussbaum gain techniques are utilized in the controller design. It is proven rigorously that asymptotic convergence of the tracking error and ultimate uniform boundedness of all the other signals can be guaranteed by the proposed control method. The control feasibility is further verified by numerical simulations.
    IEEE transactions on neural networks and learning systems 09/2014; DOI:10.1109/TNNLS.2014.2354533 · 4.37 Impact Factor
  • Wenchao Meng, Qinmin Yang, Youxian Sun
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    ABSTRACT: In this paper, adaptive neural control is investigated for a class of unknown multiple-input multiple-output nonlinear systems with time-varying asymmetric output constraints. To ensure constraint satisfaction, we employ a system transformation technique to transform the original constrained (in the sense of the output restrictions) system into an equivalent unconstrained one, whose stability is sufficient to solve the output constraint problem. It is shown that output tracking is achieved without violation of the output constraint. More specifically, we can shape the system performance arbitrarily on transient and steady-state stages with the output evolving in predefined time-varying boundaries all the time. A single neural network, whose weights are tuned online, is used in our design to approximate the unknown functions in the system dynamics, while the singularity problem of the control coefficient matrix is avoided without assumption on the prior knowledge of control input's bound. All the signals in the closed-loop system are proved to be semiglobally uniformly ultimately bounded via Lyapunov synthesis. Finally, the merits of the proposed controller are verified in the simulation environment.
    IEEE transactions on neural networks and learning systems 07/2014; 26(5). DOI:10.1109/TNNLS.2014.2333878 · 4.37 Impact Factor
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    ABSTRACT: In the field of nonlinear system identification, the linear parameter varying (LPV) model identification approach has attracted more and more attention from academia and industry. In this paper, the asymmetric Gaussian weighting function is introduced into nonlinear identification of the multi-model LPV structure. Choosing uneven operating points for local linear models over the scheduling variable, the accuracy and flexibility of the multi-model LPV model can be improved. Simulation results of a CSTR benchmark process demonstrate that the new LPV model using asymmetric Gaussian weights is more accurate and more flexible than the existing LPV models commonly using Gaussian weights or linear weights.
    Chinese Journal of Chemical Engineering 07/2014; DOI:10.1016/j.cjche.2014.05.002 · 0.87 Impact Factor
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    ABSTRACT: This brief investigates the adaptive neural network (NN) control of a class of high-order nonaffine nonlinear systems with completely unknown dynamics. Since the control terms appear within the unknown nonlinearity, traditional control schemes and stability analysis are usually rendered extremely complicated. Our main contribution includes a novel system transformation that converts the nonaffine system into an affine system through a combination of a low-pass filter and state transformation. As a result, the state-feedback control of the nonaffine system can be viewed as the output-feedback control of an affine system in normal form. The transformed system becomes linear with respect to the new input while the traditional backstepping approach is not needed thus allowing the synthesis to be extremely simplified. It is theoretically proven that all the signals in the closed-loop system are uniformly ultimately bounded (UUB). Simulation results are provided to demonstrate the performance of the developed controller.
    Automatica 05/2014; 50(5). DOI:10.1016/j.automatica.2014.03.013 · 3.13 Impact Factor
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    ABSTRACT: In this paper, a miniature methanol fuel processor and its controller design is introduced for onboard hydrogen production. The hydrogen is generated via autothermal reforming of methanol. The control scheme consists of a hydrogen flow rate controller and a reforming temperature controller. To deal with uncertain system dynamics and external disturbance, an adaptive sliding mode control algorithm is adopted as the hydrogen flow rate controller for regulating hydrogen flow rate by manipulating methanol flow rate. Additionally, a high-gain observer is implemented to estimate the unmeasurable system state. The stability of closed-loop system is guaranteed by standard Lyapunov analysis. Furthermore, a variable ratio control law is employed as the reforming temperature controller to achieve steady reforming temperature by adjusting the reforming air flow rate. Finally, the effectiveness of the entire system is testified by experimental means.
    ISA Transactions 01/2014; 53(5). DOI:10.1016/j.isatra.2013.12.015 · 2.26 Impact Factor
  • 01/2014; 9(1). DOI:10.4304/jcp.9.1.228-234
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    ABSTRACT: In this study, near-infrared (NIR) spectroscopy is applied for rapid and objective classification of 5 different brands of washing powder. Chemometric calibrations including partial least square discriminant analysis (PLS-DA), back propagation neural network (BP-NN) and least square support vector machine (LS-SVM) are investigated and compared to achieve an optimal result. Firstly, principal component analysis (PCA) is conducted to visualize the difference among washing powder samples of different brands and principal components (PCs) are extracted as inputs of BP-NN and LS-SVM models. The number of PCs and parameters of such models are optimized via cross validation. In experimental studies, a total of 225 spectra of washing powder samples (45 samples for each brand) were used to build models and 75 spectra of washing powder samples (15 samples for each brand) were used as the validation set to evaluate the performance of developed models. As for the comparison of the three investigated models, both BP-NN model and LS-SVM model successfully classified all samples in validation set according to their brands. However, the PLS-DA model failed to achieve 100% of classification accuracy. The results obtained in this investigation demonstrate that NIR spectroscopy combined with chemometric calibrations including BP-NN and LS-SVM can be successfully utilized to classify the brands of washing powder.
    Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy 11/2013; 120. DOI:10.1016/j.saa.2013.11.057 · 2.13 Impact Factor
  • Huhui Tian, Jiangang Lu, Qinmin Yang
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    ABSTRACT: In this paper, a self-tuning fuzzy logic controller (STFLC) is proposed to maintain the evaporator superheat via an electronic expansion valve (EEV) in a vapor compression chiller system. The output scaling factor (SF) of the STFLC is tuned in an on-line manner by fuzzy rules according to the current condition of the process. To verify the performance of the proposed control scheme, real-time experiments have been conducted on a pilot vapor compression chiller system. The results demonstrate that the controller design outperforms its conventional counterpart in keeping the evaporator superheat at a desired value even when the refrigeration load is changed over a wide range.
    Proceedings of the 2013 Sixth International Symposium on Computational Intelligence and Design - Volume 01; 10/2013
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    ABSTRACT: This paper deals with the power capture control of variable-speed wind energy conversion systems. The control objective is to optimize the capture of wind energy by tracking the desired power output. Arbitrary steady-state performance is achieved in the sense that the tracking error is guaranteed to converge to any predefined small set. In addition, to maximize the wind energy capture, transient performance is enhanced such that the convergence rate can be larger than an arbitrary value, which further limits the maximum overshoot. First, an adaptive controller is designed for the case where known aerodynamic torque is assumed. Then, by utilizing an online approximator to estimate the uncertain aerodynamics, the need for the exact knowledge of the aerodynamic torque is waived to imitate the practical experience. With the aid of a novel output error transformation technique, both of the proposed controllers are capable of shaping the system performance arbitrarily on transient and steady-state stages. Meanwhile, it is also proved that all the signals in the closed-loop system are bounded via Lyapunov synthesis. Finally, the feasibility of the proposed controllers is demonstrated on an 1.5-MW three-blade wind turbine using the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code developed by the National Renewable Energy Laboratory.
    IEEE Transactions on Energy Conversion 09/2013; 28(3):716-725. DOI:10.1109/TEC.2013.2273357 · 3.35 Impact Factor
  • Jianhua Zhu, Qinmin Yang, Jiangang Lu
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    ABSTRACT: This paper proposes a model predictive controller (MPC) to regulate the chilled water temperature of centralized Heating, Ventilation and Air Conditioning (HVAC) systems. Firstly, a linear parameter varying (LPV) model is built to represent the dynamics of the heat transfer between the chilled water and the refrigerant under different working conditions. Then, local linear models are identified at three chosen representative working conditions via local tests. A global LPV model is thereafter obtained by interpolating all local models along with transition process data. With the aid of the model, a MPC mechanism is implemented to adjust the compressor frequency in order to maintain the outlet chilled water temperature to a prescribed constant even in the presence of varying cooling load and external disturbance. Experimental tests on an actual HV AC system demonstrate the effectiveness and efficiency of the proposed scheme.
    2013 IEEE Electrical Power & Energy Conference (EPEC); 08/2013
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    ABSTRACT: In this paper, a novel nonlinear adaptive torque controller is proposed for variable-speed wind energy conversion systems to track the maximum power curve. Firstly, the controller is designed for the ideal case where known system parameters are assumed. Then, in the presence of uncertain internal system dynamics, the need for the exact knowledge of the system model is waived by using adaptive technologies. Furthermore, the chattering phenomenon in the generator torque which can result in high mechanical stress is avoided by adopting a modified robust term. Compared with existing methods, no accurate measurement of wind speed is required for the controller design. The control goal is achieved in the sense that the tracking error is guaranteed to converge to an arbitrarily small set. It is theoretically proved that all the signals in the closed-loop system are bounded via Lyapunov synthesis. Finally, the effectiveness and the merit of our proposed controller is shown by simulation on a 1.5MW three-blade wind turbine.
    American Control Conference (ACC), 2013; 06/2013
  • Jie Luo, Chengyu Cao, Qinmin Yang
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    ABSTRACT: In this article, an extension of the L1 adaptive control design is introduced for a class of non-affine Multi-Input Multi-Output nonlinear systems with unknown dynamics and unmeasured states. The system dynamics is represented in the normal form with the bounded-input-bounded-output internal dynamics. At first, a stable virtual reference counterpart is constructed. Thereafter, a piece-wise continuous adaptive law is introduced to the actual system along with a low-pass filtered control signal that allows for achieving arbitrarily close tracking of the input and the output signals of the reference system. Rigorous mathematical proof is provided, and the theoretical results are verified with the simulation.
    International Journal of Control 02/2013; 86(2). DOI:10.1080/00207179.2012.731727 · 1.14 Impact Factor
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    ABSTRACT: In this paper, a hybrid no-reference blockiness metric along with its calculation method is presented as a quantitative distortion measure of blocking artifacts for H.264 advanced video compression standard. Firstly, blocking artifacts are obtained by using Sobel operator for each block within a frame. Spatial masking and temporal masking effects are incorporated into the proposed metric as well. Moreover, quantization parameters from the compression domains are extracted and taken into account in the metric to interpret the fact that the blocking artifacts are usually introduced during compressing process. Experimental results demonstrate that the proposed algorithm can measure the blocking artifacts accurately and is sensitive to bit rate as well.
    Control and Automation (ICCA), 2013 10th IEEE International Conference on; 01/2013
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    ABSTRACT: This paper focuses on control of hydrogen production via autothermal reforming of methanol. Owing to uncertainty of model parameters, a sliding mode controller is designed for regulating hydrogen yield by manipulating methanol flow and water flow. Theoretical analysis and simulations demonstrate its adaptability to shifts of model parameters. In addition, a variable ratio controller is proposed in order to achieve a steady reforming temperature, in which the reforming air is employed as manipulated variable. Finally, the effectiveness of this control strategy is verified through experiments.
    Control and Automation (ICCA), 2013 10th IEEE International Conference on; 01/2013
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    ABSTRACT: In this paper, an empirical model based on least square support vector machine (LSSVM) method to predict the output air conditions in a packed tower liquid desiccant dehumidifier is developed. By analysis of the coupled heat and mass transfer between the process air and desiccant solution, six variables are used as the inputs of the LSSVM model, namely: desiccant solution and air flow rates, desiccant solution and air inlet temperature, desiccant concentration, and air relative humidity. Meanwhile, outlet air temperature and relative humidity related with the performance of the dehumidifier are considered as the outputs of the LSSVM model. Compared with the existing theoretical models, the present one is very simple, yet accuracy, and does not need complex theoretical analysis. The experimental results illustrate the effectiveness of the proposed model on performance predicting in a packed tower liquid desiccant dehumidifier. This developed model is expected to have widely applications in performance evaluation, operational monitoring, fault detection and diagnosis.
    Control and Automation (ICCA), 2013 10th IEEE International Conference on; 01/2013
  • Journal of Applied Mathematics 01/2013; 2013:1-12. DOI:10.1155/2013/840628 · 0.72 Impact Factor
  • Jie You, Qinmin Yang, Jiangang Lu
    09/2012; 10(3). DOI:10.12928/telkomnika.v10i3.315
  • Qinmin Yang, Bingnan Liu, Zhiwen Yu
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    ABSTRACT: This paper introduces a fault tolerant controller design for nonlinear unknown systems with multiple actuators and bounded disturbance. The controller consists of an adaptive learning-based control law and a switching function mechanism. The adaptive control law is implemented by a two-layer neural network and the switching function is designed to automatically search for the correct switching vector to turn off the unknown faulty actuator if there is any. The stability of the system output under the occurrence of actuator failure is proved through standard Lyapunov approach, while the other signals are guaranteed to be bounded. The theoretical result is substantiated by a simulation example with a continuous stirred tank reactor.
    2012 International Conference on Machine Learning and Cybernetics (ICMLC); 07/2012

Publication Stats

145 Citations
48.02 Total Impact Points

Institutions

  • 2011–2014
    • Zhejiang University
      • Department of Control Science and Engineering
      Hang-hsien, Zhejiang Sheng, China
    • The Hong Kong Polytechnic University
      • Department of Computing
      Hong Kong, Hong Kong
  • 2010
    • University of Connecticut
      • Department of Mechanical Engineering
      Storrs, Connecticut, United States
  • 2008–2009
    • Missouri University of Science and Technology
      • Department of Electrical Engineering
      Missouri, United States
  • 2005–2008
    • University of Missouri
      • Department of Electrical and Computer Engineering
      Columbia, Missouri, United States