Yun Zhang

GuangDong University of Technology, Shengcheng, Guangdong, China

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Publications (74)85.45 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Hand tremors may cause some blemishes in precision and stability of a minimally invasive surgery (MIS). To track the tremor signals accurately, there are two main problems left to be settled. First, it is not practical to collect the sample data of tremor in large scale in practical applications. To deal with the hand tremors, a learning method based on small samples sizes and high dimensional input space is needed. Second, the hand tremors have time-varying characteristics. This fact is neglected by traditional learning methods, which could lead to imprecision and instability of a MIS. In this work, a time-sequence-based fuzzy support vector machine adaptive filter (TSF-SVMAF) for tremor cancelling is proposed. The proposed method is based on support vector machine and time series. It is suitable for solving the problem that the inputs are time-varying and the samples are small-scale. To cancel the time-varying hand tremors, different learning-weight-functions are designed for tremor signals with different frequencies. From the simulation results, compared with the existing methods such as back propagation (BP), weighted-frequency Fourier combiner (WFLC) and bandlimited multiple Fourier linear combiner (BMFLC), the proposed method has better performance when learning the time-varying hand tremors with small sample sizes.
    International Journal of Systems Science 04/2015; 46(6). DOI:10.1080/00207721.2013.821718 · 1.58 Impact Factor
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    ABSTRACT: This paper addresses the problem of adaptive neural output-feedback control for a class of special nonlinear systems with the hysteretic output mechanism and the unmeasured states. A modified Bouc-Wen model is first employed to capture the output hysteresis phenomenon in the design procedure. For its fusion with the neural networks and the Nussbaum-type function, two key lemmas are established using some extended properties of this model. To avoid the bad system performance caused by the output nonlinearity, a barrier Lyapunov function technique is introduced to guarantee the prescribed constraint of the tracking error. In addition, a robust filtering method is designed to cancel the restriction that all the system states require to be measured. Based on the Lyapunov synthesis, a new neural adaptive controller is constructed to guarantee the prescribed convergence of the tracking error and the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system. Simulations are implemented to evaluate the performance of the proposed neural control algorithm in this paper.
    IEEE transactions on neural networks and learning systems 04/2015; DOI:10.1109/TNNLS.2015.2420661 · 4.37 Impact Factor
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    ABSTRACT: This paper presents a novel adaptive controller for controlling an autonomous helicopter with unknown inertial matrix to asymptotically track the desired trajectory. To identify the unknown inertial matrix included in the attitude dynamic model, this paper proposes a new structural identifier that differs from those previously proposed in that it additionally contains a neural networks (NNs) mechanism and a robust adaptive mechanism, respectively. Using the NNs to compensate the unknown aerodynamic forces online and the robust adaptive mechanism to cancel the combination of the overlarge NNs compensation error and the external disturbances, the new robust neural identifier exhibits a better identification performance in the complex flight environment. Moreover, an optimized algorithm is included in the NNs mechanism to alleviate the burdensome online computation. By the strict Lyapunov argument, the asymptotic convergence of the inertial matrix identification error, position tracking error, and attitude tracking error to arbitrarily small neighborhood of the origin is proved. The simulation and implementation results are provided to evaluate the performance of the proposed controller.
    IEEE transactions on neural networks and learning systems 03/2015; DOI:10.1109/TNNLS.2015.2406812 · 4.37 Impact Factor
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    ABSTRACT: In this paper, a fuzzy adaptive approach for stochastic strict-feedback nonlinear systems with quantized input signal is developed. Compared with the existing research on quantized input problem, the existing works focus on quantized stabilization, while this paper considers the quantized tracking problem, which recovers stabilization as a special case. In addition, uncertain nonlinearity and the unknown stochastic disturbances are simultaneously considered in the quantized feedback control systems. By putting forward a new nonlinear decomposition of the quantized input, the relationship between the control signal and the quantized signal is established, as a result, the major technique difficulty arising from the piece-wise quantized input is overcome. Based on fuzzy logic systems' universal approximation capability, a novel fuzzy adaptive tracking controller is constructed via backstepping technique. The proposed controller guarantees that the tracking error converges to a neighborhood of the origin in the sense of probability and all the signals in the closed-loop system remain bounded in probability. Finally, an example illustrates the effectiveness of the proposed control approach.
    03/2015; DOI:10.1109/TCYB.2015.2405616
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    ABSTRACT: This paper focuses on a problem of adaptive control for a class of nonlinear strict-feedback systems with a fuzzy dead zone and immeasurable states. By using the adaptive backstepping technique, an adaptive fuzzy output-feedback controller is constructed. The proposed control method requires only one adaptive law for an nth-order system. Compared with the conventional deterministic dead-zone models in previous articles, the main advantage of this paper is that the proposed dead-zone model is uncertain and fuzzy. By defuzzifying for fuzzy dead zone ~Γ(u) and employing an integrated design, an integrated fuzzy controller is constructed. It is proved that, even though the dead-zone input ~Γ(u) is fuzzy, the integrated fuzzy controller can make the closed-loop system semiglobally uniformly ultimately bounded and the tracking error converge to a small neighborhood of the origin. Finally, simulation results are provided to show the effectiveness of the proposed approach.
    IEEE Transactions on Fuzzy Systems 02/2015; 23(1):193-204. DOI:10.1109/TFUZZ.2014.2310491 · 6.31 Impact Factor
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    ABSTRACT: This paper presents an adaptive control approach to bridge the gap between unknown time-varying actuator nonlinearities and identical control directions in the area of robotic systems. Technically, a novel Nussbaum gain and its properties are first proposed to pave the way for the formulation of a more advanced tool. Subsequently, a newly developed theorem is summarized and integrated into the stability analysis such that all control units automatically and simultaneously estimate the unknown actuator nonlinearities and directions. Finally, it is rigorously proved that robotic systems asymptotically converge to the desired trajectories.
    Nonlinear Dynamics 01/2015; DOI:10.1007/s11071-015-2068-3 · 2.42 Impact Factor
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    ABSTRACT: The control problem of the dual arm robot rigidly grasping an object with unknown nonsymmetric deadzone input is investigated in this paper. Due to the factor that the deadzone nonlinearity is widespread in the actuators, a smooth inverse adaptive deadzone is incorporated to minimize the effect of the deadzone nonlinearity in the dual arm robot system to guarantee a high accuracy tracking. Since these type of robots are usually applied in complex environments, a multi-input multi-output fuzzy logic unit is adopted to approximate the manipulator’s dynamics to achieve a accuracy tracking performances. Moreover, a decentralized robust fuzzy adaptive control scheme is constructed to make the motion and internal forces track a reference trajectories in the presence of parameters uncertainty and external disturbance. By using the Lyapunov method, the stability of the signals in the closed-loop system is proved. Simulation result demonstrates that the proposed controller is effective to the dual arm robot system with deadzone nonlinearity.
    Nonlinear Dynamics 01/2015; DOI:10.1007/s11071-015-2070-9 · 2.42 Impact Factor
  • Shuqiong Xu, Zhi Liu, Yun Zhang
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    ABSTRACT: Support vector machines are the popular machine learning techniques. Its variant least squares support vector regression (LS-SVR) is effective for image denoising. However, the fitting of the samples contaminated by noises in the training phase will result in the fact that LS-SVR cannot work well when noise level is too far from it or noise density is high. Type-2 fuzzy sets and systems have been shown to be a more promising method to manifest the uncertainties. Various noises would be taken as uncertainties in scene images. By integrating the design of learning weights with type-2 fuzzy sets, a systematic design methodology of interval type-2 fuzzy density weighted support vector regression (IT2FDW-SVR) model for scene denoising is presented to address the problem of sample uncertainty in scene images. A novel strategy is used to design the learning weights, which is similar to the selection of human experience. To handle the uncertainty of sample density, interval type-2 fuzzy logic system (IT2FLS) is employed to deduce the fuzzy learning weights (IT2FDW) in the IT2FDW-SVR, which is an extension of the previously weighted SVR. Extensive experimental results demonstrate that the proposed method can achieve better performances in terms of both objective and subjective evaluations than those state-of-the-art denoising techniques.
    Soft Computing 01/2015; DOI:10.1007/s00500-015-1598-4 · 1.30 Impact Factor
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    ABSTRACT: This paper investigates the fusion of unknown direction hysteresis model with adaptive neural control techniques in face of time-delayed continuous time nonlinear systems without strict-feedback form. Compared with previous works on the hysteresis phenomenon, the direction of the modified Bouc-Wen hysteresis model investigated in the literature is unknown. To reduce the computation burden in adaptation mechanism, an optimized adaptation method is successfully applied to the control design. Based on the Lyapunov-Krasovskii method, two neural-network-based adaptive control algorithms are constructed to guarantee that all the system states and adaptive parameters remain bounded, and the tracking error converges to an adjustable neighborhood of the origin. In final, some numerical examples are provided to validate the effectiveness of the proposed control methods.
    IEEE transactions on neural networks and learning systems 12/2014; 25(12):2129-40. DOI:10.1109/TNNLS.2014.2305717 · 4.37 Impact Factor
  • Nonlinear Dynamics 12/2014; 78(4):2331-2340. DOI:10.1007/s11071-014-1570-3 · 2.42 Impact Factor
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    ABSTRACT: In this paper, a coordinated fuzzy control is developed for robotic arms with actuator hysteresis and motion constraint. To accurately compensate the hysteresis phenomena from the electromechanical devices, the modeling of actuator hysteresis is first integrated into the dynamics of multiple arms system. Then, the adaptive control scheme is introduced to reduce the harmful effects from unknown nonlinearities. Subsequently, the issue of the motion constraint is taken into account to facilitate the application in the condition of potential collisions. Furthermore, the stability analysis is carried out to guarantees the motion and internal forces in the robotic arms converge to the desired values. Simultaneously, the predetermined motion boundary is ensured to be never violated. Finally, comparative results are presented to illustrate the proposed scheme’s effectiveness.
    Information Sciences 11/2014; DOI:10.1016/j.ins.2014.10.061 · 3.89 Impact Factor
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    ABSTRACT: This paper presents adaptive neural tracking control for a class of uncertain multiinput-multioutput (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine pure-feedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularity-free adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded. Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this paper.
    IEEE transactions on neural networks and learning systems 11/2014; 25(11):2017-2029. DOI:10.1109/TNNLS.2014.2302856 · 4.37 Impact Factor
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    ABSTRACT: This paper focuses on an input-to-state practical stability (ISpS) problem of nonlinear systems which possess unmodeled dynamics in the presence of unstructured uncertainties and dynamic disturbances. The dynamic disturbances depend on the states and the measured output of the system, and its assumption conditions are relaxed compared with the common restrictions. Based on an input-driven filter, fuzzy logic systems are directly used to approximate the unknown and desired control signals instead of the unknown nonlinear functions, and an integrated backstepping technique is used to design an adaptive output-feedback controller that ensures robustness with respect to unknown parameters and uncertain nonlinearities. This paper, by applying the ISpS theory and the generalized small-gain approach, shows that the proposed adaptive fuzzy controller guarantees the closed-loop system being semi-globally uniformly ultimately bounded. A main advantage of the proposed controller is that it contains only three adaptive parameters that need to be updated online, no matter how many states there are in the systems. Finally, the effectiveness of the proposed approach is illustrated by two simulation examples.
    10/2014; 44(10):1714-1725. DOI:10.1109/TCYB.2013.2292702
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    ABSTRACT: A three-domain fuzzy wavelet network filter (3DFWNF) is proposed to filter the physiological tremor in robotic assisted microsurgical procedures, which bases on the three-domain fuzzy wavelet neural network (3DFWN) for estimating the modulated signals with multiple frequency components. The fuzzy domain is added in the 3DFWN to handle the fuzzy uncertainties of the tremor signals. The adaptive parameters of the network are adjusted by using a novel particle swarm optimization (PSO) algorithm in the training process, namely fuzzy PSO (FPSO). FPSO adopts fuzzy sets described by Gaussian membership function to define the position and velocity of particles, thus all arithmetic operators in the position and velocity updating rules used in the original PSO are replaced by the operators and procedures defined on fuzzy sets. Without the necessity for gradients, the FPSO coordinates the exploration and exploitation capabilities of particles, ensures quick convergence and a preferable global search. The proposed filter is compared with the existing RBF neural network and fuzzy wavelet neural networks. Experiments are carried in different situations, experimental results show superiority on tremor suppression of the newly filter. The effectiveness and accuracy of the FPSO algorithm are also verified.
    Knowledge-Based Systems 08/2014; 66. DOI:10.1016/j.knosys.2014.03.025 · 3.06 Impact Factor
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    ABSTRACT: In this paper, a robust adaptive fuzzy control approach is proposed for a class of nonlinear systems in strict-feedback form with the unknown time-varying saturation input. To deal with the time-varying saturation problem, a novel controller separation approach is proposed in the literature to separate the desired control signal from the practical constrained control input. Furthermore, an optimized adaptation method is applied to the dynamic surface control design to reduce the number of adaptive parameters. By utilizing the Lyapunov synthesis, the fuzzy logic system technique and the Nussbaum function technique, an adaptive fuzzy control algorithm is constructed to guarantee that all the signals in the closed-loop control system remain semiglobally uniformly ultimately bounded, and the tracking error is driven to an adjustable neighborhood of the origin. Finally, some numerical examples are provided to validate the effectiveness of the proposed control scheme in the literature.
    Asian Journal of Control 08/2014; 17(3). DOI:10.1002/asjc.921 · 1.41 Impact Factor
  • Zhi Liu, Ci Chen, Yun Zhang, C L P Chen
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    ABSTRACT: To achieve an excellent dual-arm coordination of the humanoid robot, it is essential to deal with the nonlinearities existing in the system dynamics. The literatures so far on the humanoid robot control have a common assumption that the problem of output hysteresis could be ignored. However, in the practical applications, the output hysteresis is widely spread; and its existing limits the motion/force performances of the robotic system. In this paper, an adaptive neural control scheme, which takes the unknown output hysteresis and computational efficiency into account, is presented and investigated. In the controller design, the prior knowledge of system dynamics is assumed to be unknown. The motion error is guaranteed to converge to a small neighborhood of the origin by Lyapunov's stability theory. Simultaneously, the internal force is kept bounded and its error can be made arbitrarily small.
    06/2014; 45(3). DOI:10.1109/TCYB.2014.2329931
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    ABSTRACT: This paper is concerned with fuzzy-model based stabilization of nonlinear networked control systems (NCSs) with time-varying transmission delays and transmission intervals based on a random-delay approach. The real-time distribution of input delays resulting from transmission delays and intervals is modeled as a dependent and nonidentically distributed process. Then a randomly switched Takagi-Sugeno fuzzy system with multiple input-delay subsystems is proposed to model the nonlinear NCSs. Based on an improved Lyapunov-Krasovskii method, which takes into account the real-time distribution of input delays in estimating cross-product integral terms, new sufficient conditions are deriveed for the exponential stability of the overall systems. The resulting controller design method is equivalent to a nonlinear convex optimization problem with LMI constraints. Numerical examples are presented to substantiate the effectiveness of our results.
    2014 26th Chinese Control And Decision Conference (CCDC); 05/2014
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    ABSTRACT: An interval type-2 fuzzy weighted support vector machine (IT2FW-SVM) is proposed to address the problem of high energy consumption for biped walking robots. Different from the traditional machine learning method of ‘copy learning’, the proposed IT2FW-SVM obtains lower energy cost and larger zero moment point (ZMP) stability margin using a novel strategy of ‘selective learning’, which is similar to human selections based on experience. To handle the uncertainty of the experience, the learning weights in the IT2FW-SVM are deduced using an interval type-2 fuzzy logic system (IT2FLS), which is an extension of the previous weighted SVM. Simulation studies show that the existing biped walking which generates the original walking samples is improved remarkably in terms of both energy efficiency and biped dynamic balance using the proposed IT2FW-SVM.
    Applied Intelligence 04/2014; 40(3). DOI:10.1007/s10489-013-0472-2 · 1.85 Impact Factor
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    ABSTRACT: This paper proposed an Interval Type-2 Fuzzy Kernel based Support Vector Machine (IT2FK-SVM) for scene classification of humanoid robot. Type-2 fuzzy sets have been shown to be a more promising method to manifest the uncertainties. Kernel design is a key component for many kernel-based methods. By integrating the kernel design with type-2 fuzzy sets, a systematic design methodology of IT2FK-SVM classification for scene images is presented to improve robustness and selectivity in the humanoid robot vision, which involves feature extraction, dimensionality reduction and classifier learning. Firstly, scene images are represented as high dimensional vector extracted from intensity, edge and orientation feature maps by biological-vision feature extraction method. Furthermore, a novel three-domain Fuzzy Kernel-based Principal Component Analysis (3DFK-PCA) method is proposed to select the prominent variables from the high-dimensional scene image representation. Finally, an IT2FM SVM classifier is developed for the comprehensive learning of scene images in complex environment. Different noisy, different view angle, and variations in lighting condition can be taken as the uncertainties in scene images. Compare to the traditional SVM classifier with RBF kernel, MLP kernel, and the Weighted Kernel (WK), respectively, the proposed method performs much better than conventional WK method due to its integration of IT2FK, and WK method performs better than the single kernel methods (SVM classifier with RBF kernel or MLP kernel). IT2FK-SVM is able to deal with uncertainties when scene images are corrupted by various noises and captured by different view angles. The proposed IT2FK-SVM method yields over $92~\% $ 92 % classification rates for all cases. Moreover, it even achieves $98~\% $ 98 % classification rate on the newly built dataset with common light case.
    Soft Computing 03/2014; 18(3). DOI:10.1007/s00500-013-1080-0 · 1.30 Impact Factor
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    ABSTRACT: In this paper, a fuzzy adaptive dynamic surface control is developed for a class of nonlinear systems with fuzzy dead zone and dynamic uncertainties. The nonlinear systems addressed in this paper are assumed to possess the unmodeled dynamics, dynamical disturbances and unknown nonlinear functions. By using a new scheme, the assumption on the dynamic uncertainties is relaxed, but the control law is simpler than the ones in the existing papers. Moreover, the dead zone input of the nonlinear systems is fuzzy, and by defuzzifying for fuzzy dead zone \(\tilde{\Gamma }(u)\) and adopting an integrated design, a new fuzzy controller is constructed. It is shown that, despite fuzzy dead zone input \(\tilde{\Gamma }(u)\) , the proposed integrated fuzzy controller can guarantee the desired tracking performance. Finally, simulation example demonstrates the effectiveness of the proposed scheme.
    Nonlinear Dynamics 02/2014; 79(3):1693-1709. DOI:10.1007/s11071-014-1768-4 · 2.42 Impact Factor