Qinmin Yang

Zhejiang University, Hang-hsien, Zhejiang Sheng, China

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Publications (51)72.45 Total impact

  • Qinmin Yang · Jianhua Zhu · Xiangguo Xu · Jiangang Lu
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    ABSTRACT: Conventional chilled water based air conditioning systems use low temperature chilled water to remove both sensible load and latent load in conditioned space, and reheating devices are usually installed to warm the overcooled air, which leads to energy waste. Alternatively, this paper proposes a neural network (NN) model based predictive control strategy for simultaneous control of indoor air temperature and humidity by varying the speeds of compressor and supply air fan in a chilled water based air conditioning system. Firstly, a NN model has been developed to model the system dynamics, linking the variations of indoor air temperature and humidity with the variations of compressor speed and supply air fan speed. Subsequently, the NN model is experimentally validated and used as a predictor. Based on the NN model, a neural network predictive controller is proposed to control the indoor air temperature and humidity simultaneously. The experimental results demonstrate the effectiveness of the proposed scheme compared with conventional PID controllers. Moreover, it has been proven that it is practical to simultaneously control indoor air temperature and humidity by varying the compressor speed and the supply air fan speed without adding any other devices to the chilled water based air conditioning systems.
    No preview · Article · Nov 2015 · Energy and Buildings
  • Jianhua Zhu · Qinmin Yang · Jiangang Lu · Binhui Zheng · Chuan Yan
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    ABSTRACT: In this paper, an adaptive neural network (NN)-based supply air temperature controller is proposed for an air handling unit (AHU) in heating, ventilation and air conditioning (HVAC) systems. The heat exchange dynamics within an AHU is complicated and almost impossible to model exactly. Moreover, it is subject to multiple external disturbance variables. To accommodate such uncertainties, a direct adaptive controller based on a two-layer NN is introduced to maintain the desired supply air temperature under varying operating conditions. To verify the performance of the proposed scheme, extensive experiments have been conducted on a pilot HVAC system. The experimental results substantiate that our method outperforms a conventional proportional-integral-derivative controller in terms of promptness to changing working conditions and robustness to external disturbances.
    No preview · Article · Oct 2015 · Transactions of the Institute of Measurement and Control
  • Qinmin Yang · Shuzhi Sam Ge · Youxian Sun
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    ABSTRACT: In this paper, a novel adaptive fault tolerant controller design is proposed for a class of nonlinear unknown systems with multiple actuators. The controller consists of an adaptive learning-based control law, a Nussbaum gain, and a switching function scheme. The adaptive control law is implemented by a two-layer neural network to accommodate the unknown system dynamics. Without the requirement of additional fault detection mechanism, the switching function is designed to automatically locate and turn off the unknown faulty actuators by observing a control performance index. The asymptotic stability of the system output in the presence of actuator failures is rigidly proved through standard Lyapunov approach, while the other signals of the closed-loop system are guaranteed to be bounded. The theoretical result is substantiated by simulation on a two-tank system.
    No preview · Article · Oct 2015 · Automatica
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    ABSTRACT: This paper presents a novel state-feedback control scheme for the tracking control of a class of multi-input multioutput continuous-time nonlinear systems with unknown dynamics and bounded disturbances. First, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback multiplied with an adaptive gain is introduced. The NN in the control law learns the system dynamics in an online manner, while the NN residual reconstruction errors and the bounded disturbances are overcome by the error sign signal. Since both of the NN output and the error sign signal are included in the integral, the continuity of the control input is ensured. The controller structure and the NN weight update law are novel in contrast with the previous effort, and the semiglobal asymptotic tracking performance is still guaranteed by using the Lyapunov analysis. In addition, the NN weights and all other signals are proved to be bounded simultaneously. The proposed approach also relaxes the need for the upper bounds of certain terms, which are usually required in the previous designs. Finally, the theoretical results are substantiated with simulations.
    No preview · Article · Sep 2015 · IEEE transactions on neural networks and learning systems
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    Wenchao Meng · Qinmin Yang · Youxian Sun

    Full-text · Dataset · Aug 2015
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    ABSTRACT: Today, innovation is a key word for many universities, as it constitutes an important part of most universities’ public and scientific outreach task. Many universities are striving to increase the number of innovations generated at the university. A common method is to provide various support for research projects e.g.; providing researchers with information about international patent rights (IPR), offering administrative or financial help concerning patent applications, giving entrepreneurship and start-up support, etc. However, fostering innovators and entrepreneurs can start already in undergraduate/graduate courses, i.e. long before a student potentially reaches the research level. We believe that key factors for success in this matter are diversity and freedom. A course that strives to promote innovation capability must allow for students with different backgrounds and different curricula to meet and work together, and must allow for students to freely use their current knowledge within new contexts. This is generally not a setting provided in traditional undergraduate/graduate courses. This article describes the execution and outcome of an graduate course “international Market-Driven Engineering (iMDE)” in which diversity and freedom are key factors. The course is international and multi-disciplinary in terms of students, teachers and subjects. Graduate students with prior knowledge in automatic control constitute one important part of the course population. We believe that the diversity amongst the students, and their freedom when it comes to both innovation process and product, provides a promising platform in which seeds of ideas can grow into conceptual prototypes that build a solid foundation for full-scale innovations. On of the iMDE-projects, the Elderly Accessible Chair, or EA Chair, with its automated scanning and automatic seat-provider functionality, is one concrete example of this.
    Full-text · Conference Paper · Aug 2015
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    ABSTRACT: Smaller and more agile space assets, such as picoscale satellites, have become one of the promising focuses for space missions because of the benefits in cost reduction, enhanced flexibility, survivability, and reliability. A simple magnetic torque coil-based attitude control system is proposed here for a one unit picoscale satellite test bed using an L1 adaptive controller. A pointing accuracy of 2 deg is achieved in both a one-axis ground test bed and three-axis control simulation.
    No preview · Article · Apr 2015 · IEEE Transactions on Aerospace and Electronic Systems
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    Wenchao Meng · Qinmin Yang · Jennie Si · Youxian Sun
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    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.
    Full-text · Article · Feb 2015 · Cybernetics, IEEE Transactions on
  • Zaiyue Yang · Lihao Sun · Jiming Chen · Qinmin Yang · Xi Chen · Kai Xing
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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.
    No preview · Article · Nov 2014 · IEEE Transactions on Power Systems
  • 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.
    No preview · Article · Sep 2014 · IEEE transactions on neural networks and learning systems
  • Wenchao Meng · Qinmin Yang · Youxian Sun
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    ABSTRACT: In this paper, a reinforcement learning based adaptive critic controller is proposed for the power capture control of variable-speed wind energy conversion systems (WECSs). The control objective is to optimize the power capture from wind by tracking the maximum power curve and minimize a predefined long-term cost function in the mean time. By minimizing the long-term cost function, both the power capture and the life time of mechanical part of a wind turbine are considered as opposed to most of existing literatures. The developed controller consists of an action network and a critic network. The critic network is introduced to evaluate the performance of the action network, and learn the cost-to-go function in an online manner. The estimate of cost-to-go function is then transmitted to the action network. The action network is utilized to provide the optimal generator torque rate with the help of the estimate of cost-to-go function. Here, a two-layer neural network structure is employed for both the action and critic network. Finally, the performance of the proposed controller is evaluated on a 1.5MW three-blade wind turbine in simulating environment.
    No preview · Article · Sep 2014
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    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.
    Full-text · Article · Jul 2014 · IEEE transactions on neural networks and learning systems
  • Yi Deng · Xiaobo Yu · Qinmin Yang · Jiangang Lu
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    ABSTRACT: Ad hoc networks have been paid more attention recently in underground mines. Due to its flexibility and quick deployment, ad hoc networks are widely used in emergency occasions such as mine collapses when communication infrastructure is destroyed. However, underground mines are harsh environment where corrosive water, dust and toxic and explosive gases are commonly present. And the characteristic of confined space in underground mines will lead to diffraction, attenuation, multipath, scattering and fading phenomena that could interfere with radio signals. In this paper, we will focus on studying performance of underground mines' ad hoc network in a VoIP context by means of simulating VoIP over ad hoc network. We compare the performance of three different codecs, G.711, G.723, and G.729. The results demonstrate that G.723 will obtain highest Mean Opinion Score and is suitable for encoding voice and transmitting through the ad hoc networks.
    No preview · Conference Paper · Jul 2014
  • Jie You · Qinmin Yang · Jiangang Lu · Youxian Sun
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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.
    No preview · Article · Jul 2014 · Chinese Journal of Chemical Engineering
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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.
    Full-text · Article · May 2014 · Automatica
  • Jianhua Zhu · Qinmin Yang · Xiangguo Xu · Jiangang Lu
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    ABSTRACT: In this paper, a linear parameter varying (LPV) model-based predictive control method is proposed to regulate the chilled water temperature of centralized heating, ventilating and air conditioning (HVAC) systems. First, a LPV model structure is proposed to identify the dynamics of the refrigerant loop under varying cooling load. By defining the scheduling variable to be the chilled water mass flow rate, the working conditions, under which the HVAC system is operating, can be quantified. Thereafter, a certain number of operating points across the whole operation range can be determined and the corresponding local linear models are built. Subsequently, a global LPV model is attained by interpolating all local models along with transition process data. With the aid of the model, a LPV-based model predictive control mechanism is implemented to control the compressor frequency in order to maintain the chilled water temperature in the presence of varying cooling load and external disturbance. Experimental tests on a pilot HVAC system demonstrate that the proposed control scheme can accurately capture the nonlinearity of the process and deliver satisfactory performance to maintain a desired chilled water temperature.
    No preview · Article · Apr 2014 · Building Service Engineering
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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.
    No preview · Article · Jan 2014 · ISA Transactions
  • Long Xu · Jiangang Lu · Qinmin Yang · Jinshui Chen · Yingzi Shi

    No preview · Article · Jan 2014 · Journal of Computers
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    ABSTRACT: This paper is concerned with the identification of linear parameter varying (LPV) systems by utilizing a multimodel structure. To improve the approximation capability of the LPV model, asymmetric Gaussian weighting functions are introduced and compared with commonly used symmetric Gaussian functions. By this mean, locations of operating points can be selected freely. It has been demonstrated through simulations with a high purity distillation column that the identified models provide more satisfactory approximation. Moreover, an experiment is performed on real HVAC (heating, ventilation, and air-conditioning) to further validate the effectiveness of the proposed approach.
    Preview · Article · Dec 2013 · Journal of Applied Mathematics
  • Hongguang Zhang · Qinmin Yang · Jiangang Lu
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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.
    No preview · Article · Nov 2013 · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy

Publication Stats

242 Citations
72.45 Total Impact Points

Institutions

  • 2011-2015
    • Zhejiang University
      • Department of Control Science and Engineering
      Hang-hsien, Zhejiang Sheng, China
  • 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