Publications by authors named "Huanqing Wang"

28 Publications

  • Page 1 of 1

Fuzzy adaptive finite-time output feedback control of stochastic nonlinear systems.

ISA Trans 2021 Jun 25. Epub 2021 Jun 25.

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada. Electronic address:

An adaptive finite-time approach to the feedback control of stochastic nonlinear systems is presented. The fuzzy logic system (FLS) and a state observer are used to estimate the uncertain function and unmeasured state of the controlled system, respectively. A dynamic surface control (DSC) scheme is employed to deal with the "computational explosion" problem, which is inherent in traditional backstepping methods since the repetitive calculation of the derivatives of virtual control signals is avoided. A new output feedback controller is developed to guarantee that all the signals of the controlled system are bounded within a finite time range and the tracking deviation can converge to an arbitrarily small residual set within finite time. Simulations confirm the analytical and theoretical results of the presented algorithm.
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http://dx.doi.org/10.1016/j.isatra.2021.06.029DOI Listing
June 2021

Event-Triggered Adaptive NN Tracking Control for MIMO Nonlinear Discrete-Time Systems.

IEEE Trans Neural Netw Learn Syst 2021 Jun 15;PP. Epub 2021 Jun 15.

This article concentrates on the design of a novel event-based adaptive neural network (NN) control algorithm for a class of multiple-input-multiple-output (MIMO) nonlinear discrete-time systems. A controller is designed through a novel recursive design procedure, under which the dependence on virtual controls is avoided and only system states are needed. The numbers of the event-triggered conditions and parameters updated online in each subsystem reduce to only one, which largely reduces the computation burden and simplifies the algorithm realization. In this case, radial basis function NNs (RBFNNs) are employed to approximate the control input. The semiglobal uniformly ultimate boundedness (SGUUB) of all the signals in the closed-loop system is guaranteed by the Lyapunov difference approach. The effectiveness of the proposed algorithm is validated by a simulation example.
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http://dx.doi.org/10.1109/TNNLS.2021.3084965DOI Listing
June 2021

Command Filter-Based Adaptive Neural Control Design for Nonstrict-Feedback Nonlinear Systems With Multiple Actuator Constraints.

IEEE Trans Cybern 2021 Jun 2;PP. Epub 2021 Jun 2.

This article proposes an adaptive neural-network command-filtered tracking control scheme of nonlinear systems with multiple actuator constraints. An equivalent transformation method is introduced to address the impediment from actuator nonlinearity. By utilizing the command filter method, the explosion of complexity problem is addressed. With the help of neural-network approximation, an adaptive neural-network tracking backstepping control strategy via the command filter technique and the backstepping design algorithm is proposed. Based on this scheme, the boundedness of all variables is guaranteed and the output tracking error fluctuates near the origin within a small bounded area. Simulations testify the availability of the designed control strategy.
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http://dx.doi.org/10.1109/TCYB.2021.3079129DOI Listing
June 2021

Time-/Event-Triggered Adaptive Neural Asymptotic Tracking Control for Nonlinear Systems With Full-State Constraints and Application to a Single-Link Robot.

IEEE Trans Neural Netw Learn Syst 2021 Jun 2;PP. Epub 2021 Jun 2.

This study proposes the time-/event-triggered adaptive neural control strategies for the asymptotic tracking problem of a class of uncertain nonlinear systems with full-state constraints. First, we design a time-triggered strategy. The effect caused by the residuals of the estimation via radial basis function (RBF) neural networks (NNs), and the reasonable upper bounds on the first derivative of the reference signal and the derivative of each virtual control, can be eliminated by designing appropriate adaptive laws and utilizing the basic properties of RBF NNs. Moreover, the construction of the barrier Lyapunov functions (BLFs) in this work ensures the compliance of the full-state constraints and also holds the asymptotic output tracking performance. Then, based on the time-triggered strategy, we further design a relative threshold event-triggered strategy. The proposed event-triggered adaptive neural controller can solve the main control objective of this work, that is: 1) the full-state constraint requirements of the system are not violated and 2) the output signal asymptotically tracks the reference signal. Compared with the traditional method, the event-triggered strategy can improve the utilization of communication channels and resources and has greater practical significance. Finally, an example of single-link robot under the proposed two strategies illustrates the validity of the constructed controllers.
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http://dx.doi.org/10.1109/TNNLS.2021.3082994DOI Listing
June 2021

Binocular DIC system for 3D correlation measurements.

Appl Opt 2021 May;60(14):4101-4108

A novel, to the best of our knowledge, mirror-assisted binocular stereo digital image correlation (DIC) system is proposed for the reconstruction of the overall contour, thickness, and strain measurement of the object. First, the angle between the two plane mirrors is adjusted until two virtual images and two real images can be formed in the mirrors. Then, the adjustable speckle size and definition characteristics of the projection speckle technology are fully utilized to realize the precise measurement of the mirror plane. Finally, a 3D contour reconstruction experiment and a dynamic stretching experiment are conducted to verify the proposed method. Experimental results show that the proposed method can achieve a 360° omnidirectional deformation measurement, and the 3D reconstruction of the object with complex contours has a relatively ideal reconstruction effect. According to the virtual image, the thickness of the conventional specimen can be completed easily, and the coordinates of the front and rear surfaces need not be subtracted. The dynamic strain can be calculated separately from the front and rear surfaces of the standard specimen and can be realized in the dynamic tensile experiment. Compared with the existing binocular DIC system, the proposed method can provide more valid data with guaranteed excellent results. It provides a better implementation method for omnidirectional measurement, thickness, and stress-strain calculation of the object.
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http://dx.doi.org/10.1364/AO.423269DOI Listing
May 2021

Adaptive Control and Application for Nonlinear Systems With Input Nonlinearities and Unknown Virtual Control Coefficients.

IEEE Trans Cybern 2021 Mar 4;PP. Epub 2021 Mar 4.

This article is devoted to an adaptive tracking control problem for nonlinear systems with input deadzone and saturation, whose virtual control coefficients include the known and unknown terms. A novel smooth function is first introduced to approximate the input nonlinearities. By utilizing an auxiliary variable and the Nussbaum gain technique, an improved real control signal is constructed to handle the uncertainties of the virtual control coefficients and input nonlinearities. Furthermore, an adaptive tracking controller is constructed and applied to the attitude control of a quadrotor, which guarantees the boundedness of all the signals in the resulting closed-loop system. Finally, both stability analysis and simulation results validate the effectiveness of the developed control strategy.
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http://dx.doi.org/10.1109/TCYB.2021.3054373DOI Listing
March 2021

Finite-Time-Prescribed Performance-Based Adaptive Fuzzy Control for Strict-Feedback Nonlinear Systems With Dynamic Uncertainty and Actuator Faults.

IEEE Trans Cybern 2021 Jan 15;PP. Epub 2021 Jan 15.

In this article, finite-time-prescribed performance-based adaptive fuzzy control is considered for a class of strict-feedback systems in the presence of actuator faults and dynamic disturbances. To deal with the difficulties associated with the actuator faults and external disturbance, an adaptive fuzzy fault-tolerant control strategy is introduced. Different from the existing controller design methods, a modified performance function, which is called the finite-time performance function (FTPF), is presented. It is proved that the presented controller can ensure all the signals of the closed-loop system are bounded and the tracking error converges to a predetermined region in finite time. The effectiveness of the presented control scheme is verified through the simulation results.
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http://dx.doi.org/10.1109/TCYB.2020.3046316DOI Listing
January 2021

Method to eliminate thermal disturbance errors in digital image correlation measurement based on projection speckle.

Appl Opt 2020 Nov;59(33):10474-10482

Digital image correlation (DIC) technology is an optical measurement method of material strain displacement based on visible light illumination. With the increasing application of DIC technology in the field of high-temperature measurement, however, the effect of thermal disturbance on measurement accuracy becomes more and more serious. To solve this problem, a method to eliminate thermal disturbance in material measurements based on projection speckle is proposed. First, the gray surface information of two different colors is assigned to the surface of the test piece by artificial splashing and projector projection. The pictures are collected using a color camera, and the channels are separated for each frame of the picture. After that, the displacement field recorded by different channels can be obtained by DIC calculation so the thermal disturbance error can be separated from the real displacement and eliminated. Subsequently, an experimental system is built. Finally, the corrected results are compared with the true displacement value of the stage. The results show that the two sets of values are highly consistent, which verifies the feasibility and accuracy of the proposed method.
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http://dx.doi.org/10.1364/AO.405333DOI Listing
November 2020

Adaptive Neural Output-Feedback Controller Design of Switched Nonlower Triangular Nonlinear Systems With Time Delays.

IEEE Trans Neural Netw Learn Syst 2020 10 11;31(10):4084-4093. Epub 2019 Dec 11.

In this article, we study the issue of adaptive neural output-feedback controller design for a class of uncertain switched time-delay nonlinear systems with nonlower triangular structure. The prominent contribution of this article is that the delay-dependent stability criterion of nonswitched nonlinear systems is successfully extended to that of switched nonlower triangular nonlinear systems. The design algorithm is listed as follows. First, a switched state observer is designed such that the error dynamic system can be generated. Second, neural networks, adaptive backstepping technique, and variable separation method are, respectively, applied to construct a common controller for all subsystems, in which the Lyapunov-Krasovskii functionals are deliberately constructed such that the average dwell-time scheme can be employed to guarantee the stability and performance of the closed-loop system, despite the existence of time delays. Third, the stability analysis process confirms in detail that all the variables of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation study is given to show the validity of the proposed control approach.
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http://dx.doi.org/10.1109/TNNLS.2019.2952108DOI Listing
October 2020

Event-triggered adaptive tracking control for uncertain nonlinear systems based on a new funnel function.

ISA Trans 2020 Apr 23;99:130-138. Epub 2019 Sep 23.

School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, PR China. Electronic address:

This paper focuses on the problem of event-triggered funnel control for strict-feedback nonlinear systems with unknown parameters. For the first time, an adjustable funnel function is proposed, whose parameters can be adjusted online according to the change of tracking error. Furthermore, based on event-triggered control, an adaptive event-triggered funnel controller is constructed, which guarantees that all the signals in the closed-loop system are bounded. Besides, the output tracking error is further optimized and always falls within an adjustable funnel which has a faster convergence. Meanwhile, the Zeno behavior also is avoided. Simulation results demonstrate the effectiveness of the developed controller.
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http://dx.doi.org/10.1016/j.isatra.2019.09.015DOI Listing
April 2020

Adaptive Neural Network Prescribed Performance Bounded- H Tracking Control for a Class of Stochastic Nonlinear Systems.

IEEE Trans Neural Netw Learn Syst 2020 Jun 9;31(6):2140-2152. Epub 2019 Aug 9.

This paper aims to give a design strategy on the prescribed performance H tracking control problem for a class of strict-feedback stochastic nonlinear systems based on the backstepping technique. Generally, by using the backstepping design method, the stochastic nonlinear systems can only be made to be bounded in probability and it is difficult to achieve the H performance criterion due to the positive constant term appeared in the stability analysis. Thus, a novel concept with regard to the bounded- H performance is proposed in this paper to overcome the design difficulty. By using the new concept and the adaptive neural network technique as well as Gronwall inequality, an adaptive neural network prescribed performance bounded- H tracking controller is designed. Therein, neural networks are used to approximate the unknown packaged nonlinear functions. The assumption that the approximation errors of neural networks are square-integrable in some literature is eliminated. The designed controller guarantees that all the signals in the closed-loop stochastic nonlinear systems are bounded in probability, the tracking error is constrained into an adjustable neighborhood of the origin with the prescribed performance bounds, and the controlled system has a given H disturbance attenuation performance for external disturbances. Finally, the simulation results are provided to illustrate the effectiveness and feasibility of the proposed approach.
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http://dx.doi.org/10.1109/TNNLS.2019.2928594DOI Listing
June 2020

Adaptive Neural Output-Feedback Decentralized Control for Large-Scale Nonlinear Systems With Stochastic Disturbances.

IEEE Trans Neural Netw Learn Syst 2020 03 1;31(3):972-983. Epub 2019 Jul 1.

This paper addresses the problem of adaptive neural output-feedback decentralized control for a class of strongly interconnected nonlinear systems suffering stochastic disturbances. An state observer is designed to approximate the unmeasurable state signals. Using the approximation capability of radial basis function neural networks (NNs) and employing classic adaptive control strategy, an observer-based adaptive backstepping decentralized controller is developed. In the control design process, NNs are applied to model the uncertain nonlinear functions, and adaptive control and backstepping are combined to construct the controller. The developed control scheme can guarantee that all signals in the closed-loop systems are semiglobally uniformly ultimately bounded in fourth-moment. The simulation results demonstrate the effectiveness of the presented control scheme.
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http://dx.doi.org/10.1109/TNNLS.2019.2912082DOI Listing
March 2020

Adaptive Fuzzy Finite-Time Control of Nonlinear Systems With Actuator Faults.

IEEE Trans Cybern 2020 May 8;50(5):1786-1797. Epub 2019 May 8.

This paper addresses the trajectory tracking control problem of a class of nonstrict-feedback nonlinear systems with the actuator faults. The functional relationship in the affine form between the nonlinear functions with whole state and error variables is established by using the structure consistency of intermediate control signals and the variable-partition technique. The fuzzy control and adaptive backstepping schemes are applied to construct an improved fault-tolerant controller without requiring the specific knowledge of control gains and actuator faults, including both stuck constant value and loss of effectiveness. The proposed fault-tolerant controller ensures that all signals in the closed-loop system are semiglobally practically finite-time stable and the tracking error remains in a small neighborhood of the origin after a finite period of time. The developed control method is verified through two numerical examples.
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http://dx.doi.org/10.1109/TCYB.2019.2902868DOI Listing
May 2020

Editorial: Neural & Bio-inspired Processing and Robot Control.

Front Neurorobot 2018 8;12:72. Epub 2018 Nov 8.

Department of Systems and Computer Engineering, Carleton University Ottawa, Ottawa, ON, Canada.

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http://dx.doi.org/10.3389/fnbot.2018.00072DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6236023PMC
November 2018

Robust Fuzzy Adaptive Tracking Control for Nonaffine Stochastic Nonlinear Switching Systems.

IEEE Trans Cybern 2018 Aug 21;48(8):2462-2471. Epub 2017 Dec 21.

This paper is concerned with the trajectory tracking control problem of a class of nonaffine stochastic nonlinear switched systems with the nonlower triangular form under arbitrary switching. Fuzzy systems are employed to tackle the problem from packaged unknown nonlinearities, and the backstepping and robust adaptive control techniques are applied to design the controller by adopting the structural characteristics of fuzzy systems and the common Lyapunov function approach. By using Lyapunov stability theory, the semiglobally uniformly ultimate boundness in the fourth-moment of all closed-loop signals is guaranteed, and the system output is ensured to converge to a small neighborhood of the given trajectory. The main advantages of this paper lie in the fact that both the completely nonaffine form and nonlower triangular structure are taken into account for the controlled systems, and the increasing property of whole state functions is removed by using the structural characteristics of fuzzy systems. The developed control method is verified through a numerical example.
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http://dx.doi.org/10.1109/TCYB.2017.2740841DOI Listing
August 2018

The effect of shame on anger at others: awareness of the emotion-causing events matters.

Cogn Emot 2019 06 22;33(4):696-708. Epub 2018 Jun 22.

a State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research , Beijing Normal University , Beijing , People's Republic of China.

Numerous studies have found that shame increases individuals' anger at others. However, according to recent theories about the social function of shame and anger at others, it is possible that shame controls individuals' anger at others in specific conditions. We replicated previous findings that shame increased individuals' anger at others' unfairness, when others were not aware of the individual's experience of shameful events. We also found for the first time that shame controlled or even decreased individuals' anger at others' unfairness, when others were aware of the individual's experience of shameful events. The results were consistent when shame was induced by either a recall paradigm or an imagination paradigm, and in either the ultimatum game or the dictator game. This suggests that shame strategically controls individuals' anger at others to demonstrate that they are willing to benefit others, when facing the risk of social exclusion. Our findings highlight the interpersonal function of shame and deepen the understanding of the relationship between shame and anger at others.
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http://dx.doi.org/10.1080/02699931.2018.1489782DOI Listing
June 2019

Full-circle range and microradian resolution angle measurement using the orthogonal mirror self-mixing interferometry.

Opt Express 2018 Apr;26(8):10371-10381

The self-mixing technique based on the traditional reflecting mirror has been demonstrated with great merit for angle sensing applications. In order to solve the problems of the narrow measurement angle range and low resolution in traditional angle measurement method, we proposed an angle measurement system using orthogonal mirror self-mixing interferometry combine an orthogonal mirror with designed mechanical linkage. It overcomes the shortcomings of traditional angle measurement methods and realized the angle measurement with microradian resolution in a full-circle range of 0 rad to 2π rad. In the experiment, the measurement resolution can reach to 5.27 µrad and the absolute error can lower to ± 0.011µrad, which satisfies the requirements of most high accuracy angle measurement.
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http://dx.doi.org/10.1364/OE.26.010371DOI Listing
April 2018

Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics.

IEEE Trans Neural Netw Learn Syst 2018 08 29;29(8):3658-3668. Epub 2017 Aug 29.

This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.
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http://dx.doi.org/10.1109/TNNLS.2017.2716947DOI Listing
August 2018

A Novel Recurrent Neural Network for Manipulator Control With Improved Noise Tolerance.

IEEE Trans Neural Netw Learn Syst 2018 05 11;29(5):1908-1918. Epub 2017 Apr 11.

In this paper, we propose a novel recurrent neural network to resolve the redundancy of manipulators for efficient kinematic control in the presence of noises in a polynomial type. Leveraging the high-order derivative properties of polynomial noises, a deliberately devised neural network is proposed to eliminate the impact of noises and recover the accurate tracking of desired trajectories in workspace. Rigorous analysis shows that the proposed neural law stabilizes the system dynamics and the position tracking error converges to zero in the presence of noises. Extensive simulations verify the theoretical results. Numerical comparisons show that existing dual neural solutions lose stability when exposed to large constant noises or time-varying noises. In contrast, the proposed approach works well and has a low tracking error comparable to noise-free situations.
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http://dx.doi.org/10.1109/TNNLS.2017.2672989DOI Listing
May 2018

Observer-Based Fuzzy Adaptive Output-Feedback Control of Stochastic Nonlinear Multiple Time-Delay Systems.

IEEE Trans Cybern 2017 Sep 21;47(9):2568-2578. Epub 2017 Feb 21.

This paper is concerned with the observer-based fuzzy output-feedback control for stochastic nonlinear multiple time-delay systems. On the basis of the consistent form of virtual input signals and increasing characteristics of the system upper bound functions, a variable splitting technique is employed to surmount the difficulty occurred in the nonlower-triangular form. In the controller design procedure, a state observer is first designed, and then an adaptive fuzzy output-feedback control method is presented by combining backstepping design together with fuzzy systems' universal approximation capability. The proposed adaptive controller guarantees the semi-global boundedness of closed-loop system trajectories in terms of fourth-moment. Two simulation examples are displayed to demonstrate the feasibility of the suggested controller.
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http://dx.doi.org/10.1109/TCYB.2017.2655501DOI Listing
September 2017

Adaptive Neural Tracking Control for a Class of Nonlinear Systems With Dynamic Uncertainties.

IEEE Trans Cybern 2017 Oct 22;47(10):3075-3087. Epub 2016 Sep 22.

This paper considers the problem of adaptive neural control of nonlower triangular nonlinear systems with unmodeled dynamics and dynamic disturbances. The design difficulties appeared in the unmodeled dynamics and nonlower triangular form are handled with a dynamic signal and a variable partition technique for the nonlinear functions of all state variables, respectively. It is shown that the proposed controller is able to ensure the semi-global boundedness of all signals of the resulting closed-loop system. Furthermore, the system output is ensured to converge to a small domain of the given trajectories. The main advantage about this research is that a neural networks-based tracking control method is developed for uncertain nonlinear systems with unmodeled dynamics and nonlower triangular form. Simulation results demonstrate the feasibility of the newly presented design techniques.
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http://dx.doi.org/10.1109/TCYB.2016.2607166DOI Listing
October 2017

Adaptive Neural Synchronization Control for Bilateral Teleoperation Systems With Time Delay and Backlash-Like Hysteresis.

IEEE Trans Cybern 2017 Oct 10;47(10):3018-3026. Epub 2017 Jan 10.

This paper considers the master and slave synchronization control for bilateral teleoperation systems with time delay and backlash-like hysteresis. Based on radial basis functions neural networks' approximation capabilities, two improved adaptive neural control approaches are developed. By Lyapunov stability analysis, the position and velocity tracking errors are guaranteed to converge to a small neighborhood of the origin. The contributions of this paper can be summarized as follows: 1) by using the matrix norm established using the weight vector of neural networks as the estimated parameters, two novel control schemes are developed and 2) the hysteresis inverse is not required in the proposed controllers. The simulations are performed, and the results show the effectiveness of the proposed method.
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http://dx.doi.org/10.1109/TCYB.2016.2644656DOI Listing
October 2017

Backstepping-Based Lyapunov Function Construction Using Approximate Dynamic Programming and Sum of Square Techniques.

IEEE Trans Cybern 2017 Oct 20;47(10):3393-3403. Epub 2016 Jun 20.

In this paper, backstepping for a class of block strict-feedback nonlinear systems is considered. Since the input function could be zero for each backstepping step, the backstepping technique cannot be applied directly. Based on the assumption that nonlinear systems are polynomials, for each backstepping step, Lypunov function can be constructed in a polynomial form by sum of square (SOS) technique. The virtual control can be obtained by the Sontag feedback formula, which is equivalent to an optimal control-the solution of a Hamilton-Jacobi-Bellman equation. Thus, approximate dynamic programming (ADP) could be used to estimate value functions (Lyapunov functions) instead of SOS. Through backstepping technique, the control Lyapunov function (CLF) of the full system is constructed finally making use of the strict-feedback structure and a stabilizable controller can be obtained through the constructed CLF. The contributions of the proposed method are twofold. On one hand, introducing ADP into backstepping can broaden the application of the backstepping technique. A class of block strict-feedback systems can be dealt by the proposed method and the requirement of nonzero input function for each backstepping step can be relaxed. On the other hand, backstepping with surface dynamic control actually reduces the computation complexity of ADP through constructing one part of the CLF by solving semidefinite programming using SOS. Simulation results verify contributions of the proposed method.
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http://dx.doi.org/10.1109/TCYB.2016.2574747DOI Listing
October 2017

Robust Adaptive Neural Tracking Control for a Class of Stochastic Nonlinear Interconnected Systems.

IEEE Trans Neural Netw Learn Syst 2016 Mar 24;27(3):510-23. Epub 2015 Mar 24.

In this paper, an adaptive neural decentralized control approach is proposed for a class of multiple input and multiple output uncertain stochastic nonlinear strong interconnected systems. Radial basis function neural networks are used to approximate the packaged unknown nonlinearities, and backstepping technique is utilized to construct an adaptive neural decentralized controller. The proposed control scheme can guarantee that all signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded in the sense of fourth moment, and the tracking errors eventually converge to a small neighborhood around the origin. The main feature of this paper is that the proposed approach is capable of controlling the stochastic systems with strong interconnected nonlinearities both in the drift and diffusion terms that are the functions of all states of the overall system. Simulation results are used to illustrate the effectiveness of the suggested approach.
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http://dx.doi.org/10.1109/TNNLS.2015.2412035DOI Listing
March 2016

Neural-Based Adaptive Output-Feedback Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems.

IEEE Trans Cybern 2015 Sep 28;45(9):1977-87. Epub 2014 Oct 28.

In this paper, we consider the problem of observer-based adaptive neural output-feedback control for a class of stochastic nonlinear systems with nonstrict-feedback structure. To overcome the design difficulty from the nonstrict-feedback structure, a variable separation approach is introduced by using the monotonically increasing property of system bounding functions. On the basis of the state observer, and by combining the adaptive backstepping technique with radial basis function neural networks' universal approximation capability, an adaptive neural output feedback control algorithm is presented. It is shown that the proposed controller can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in the sense of mean quartic value. Simulation results are provided to show the effectiveness of the proposed control scheme.
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http://dx.doi.org/10.1109/TCYB.2014.2363073DOI Listing
September 2015

Adaptive neural tracking control for a class of nonstrict-feedback stochastic nonlinear systems with unknown backlash-like hysteresis.

IEEE Trans Neural Netw Learn Syst 2014 May;25(5):947-58

This paper considers the problem of adaptive neural control of stochastic nonlinear systems in nonstrict-feedback form with unknown backlash-like hysteresis nonlinearities. To overcome the design difficulty of nonstrict-feedback structure, variable separation technique is used to decompose the unknown functions of all state variables into a sum of smooth functions of each error dynamic. By combining radial basis function neural networks' universal approximation capability with an adaptive backstepping technique, an adaptive neural control algorithm is proposed. It is shown that the proposed controller guarantees that all the signals in the closed-loop system are four-moment semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in the sense of mean quartic value. Simulation results further show the effectiveness of the presented control scheme.
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http://dx.doi.org/10.1109/TNNLS.2013.2283879DOI Listing
May 2014

Robust adaptive fuzzy tracking control for pure-feedback stochastic nonlinear systems with input constraints.

IEEE Trans Cybern 2013 Dec;43(6):2093-104

This paper is concerned with the problem of adaptive fuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with input saturation. To overcome the design difficulty from nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is first introduced to approximate the saturation function; then, an adaptive fuzzy tracking controller based on the mean-value theorem is constructed by using backstepping technique. The proposed adaptive fuzzy controller guarantees that all signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighborhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme.
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http://dx.doi.org/10.1109/TCYB.2013.2240296DOI Listing
December 2013

Enantioselective determination of the insecticide indoxacarb in cucumber and tomato by chiral liquid chromatography-tandem mass spectrometry.

Chirality 2013 Jun;25(6):350-4

Institute of Plant Protection, Chinese Academy of Agricultural Sciences, State Key Laboratory for Biology of Plant Diseases and Insect Pests, Beijing, PR China.

A convenient and precise chiral method was developed and validated for measuring indoxacarb enantiomers in cucumber and tomato using liquid chromatography-tandem mass spectrometry (LC-MS/MS) with a reversed-phase Chiralpak AD-RH column. The target analytes were extracted by acetonitrile and then purified by solid phase extraction (SPE) using NH2 /Carb combined-cartridge. Parameters including the matrix effect, linearity, precision, accuracy, and stability were used. Then the proposed method was successfully applied to investigate the possible enantioselective degradation of rac-indoxacarb in cucumber and tomato under open conditions. The results indicated that the degradation of indoxacarb enantiomers followed first-order kinetics in cucumber and tomato. The half-lives of (+)-S-indoxacarb in cucumber and tomato were 3.0 and 5.9 days, respectively; while the (-)-R-indoxacarb were 7.3 and 12.2 days, respectively. The data of the half-lives showed that (+)-S-indoxacarb was preferentially degraded in cucumber and tomato. Moreover, indoxacarb degraded faster in cucumber than in tomato.
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http://dx.doi.org/10.1002/chir.22165DOI Listing
June 2013
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