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Ding Y.,SYSU CMU Shunde International Joint Research Institute | Li X.,Sun Yat Sen University | Zhang D.,Sun Yat Sen University | Tan H.,Key Laboratory of Autonomous Systems and Networked Control | Zhang Y.,Sun Yat Sen University
2016 3rd International Conference on Systems and Informatics, ICSAI 2016 | Year: 2016

In this paper, discontinuous signum-Activated ZD (Zhang dynamics) controllers are proposed as an attempt firstly to handle the agitator tank. For the agitator tank, there are two control variables, which are accurate concentration and moderate liquid level. In other words, the proposed discontinuous signum-Activated ZD controllers are designed to make the two variables of the agitator tank converge to desired trajectories. Moreover, signum function, as a kind of discontinuous activated function, has easier circuit implementation and thus is introduced as an activation function for handling agitator tank as an attempt. Besides, different tracking trajectories are used, and corresponding simulation results are shown in this paper. The simulation results, compared with the ZD controllers using the linear activation function, further verify the relative effectiveness of the discontinuous signum-Activated ZD controllers. In addition, the simulation results also simultaneously show that the discontinuous signum-Activated ZD controllers of the agitator tank may oscillate under certain frequencies. © 2016 IEEE.


Zhang Y.,Sun Yat Sen University | Zhang Y.,SYSU CMU Shunde International Joint Research Institute | Zhang Y.,Key Laboratory of Autonomous Systems and Networked Control | Qiu B.,Sun Yat Sen University | And 9 more authors.
Nonlinear Dynamics | Year: 2017

The pendulum control of the inverted-pendulum-on-a-cart (IPC) system is one of the most important issues in nonlinear control theory and has been widely investigated. Nevertheless, the control of pendulum tracking and swinging up has often been addressed separately. In this paper, by combining the zeroing dynamics and the conventional gradient dynamics, two concise zeroing-gradient (ZG) controllers (termed, z2g0 controller and z2g1 controller, respectively) are constructed for the IPC system. Importantly, the proposed z2g1 controller not only realizes the simultaneous control of pendulum swinging up and pendulum angle tracking, but also solves the singularity problem elegantly without using any switching strategy. Besides, the ZG method is compared with the optimal control method and the backstepping method. The theoretical analyses about the convergence performance of z2g0 and z2g1 controllers are further presented. Moreover, the boundedness of both control input u and its derivative (Formula presented.) of the z2g1 controller is proved. Three illustrative examples are carried out to demonstrate the tracking performance of z2g0 and z2g1 controllers for the pendulum tracking control. In particular, the efficacy and superiority of z2g1 controller for the control of pendulum tracking (including swinging up) of the IPC system in conquering the singularity problem are substantiated by comparative results. Furthermore, this paper investigates the robustness of the proposed ZG controllers (as well as the ZG design method) in the situations of time delay and disturbance. © 2017 Springer Science+Business Media Dordrecht


Yin M.,Guangdong University of Technology | Yin M.,Key Laboratory of Autonomous Systems and Networked Control | Gao J.,Charles Sturt University | Lin Z.,Peking University | Lin Z.,Shanghai JiaoTong University
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2016

Low-rank representation (LRR) has recently attracted a great deal of attention due to its pleasing efficacy in exploring low-dimensional subspace structures embedded in data. For a given set of observed data corrupted with sparse errors, LRR aims at learning a lowest-rank representation of all data jointly. LRR has broad applications in pattern recognition, computer vision and signal processing. In the real world, data often reside on low-dimensional manifolds embedded in a high-dimensional ambient space. However, the LRR method does not take into account the non-linear geometric structures within data, thus the locality and similarity information among data may be missing in the learning process. To improve LRR in this regard, we propose a general Laplacian regularized low-rank representation framework for data representation where a hypergraph Laplacian regularizer can be readily introduced into, i.e., a Non-negative Sparse Hyper-Laplacian regularized LRR model (NSHLRR). By taking advantage of the graph regularizer, our proposed method not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information in data. The extensive experimental results on image clustering, semi-supervised image classification and dimensionality reduction tasks demonstrate the effectiveness of the proposed method. © 1979-2012 IEEE.


Zhang Y.,Sun Yat Sen University | Zhai K.,Sun Yat Sen University | Chen D.,SYSU CMU Shunde International Joint Research Institute | Jin L.,SYSU CMU Shunde International Joint Research Institute | Hu C.,Key Laboratory of Autonomous Systems and Networked Control
Mathematics and Computers in Simulation | Year: 2015

Zhang-gradient (ZG) method is a combination of Zhang dynamics (ZD) and gradient dynamics (GD) methods which are two powerful methods for online time-varying problems solving. ZG controllers are designed using the ZG method to solve the tracking control problem of a certain system. In this paper, the design process of the ZG controllers with explicit as well as implicit tracking control of the double-integrator system is presented in detail. In addition, the corresponding computer simulations are conducted with different values of the design parameter λ to illustrate the effectiveness of ZG controllers. However, even though the ZG controllers are powerful, there is still a challenge in the simulation practice. Specifically, different settings of simulation options in MATLAB ordinary differential equation (ODE) solvers may lead to different simulation results (e.g.,failure and success). For better comparison, the successful and failed simulation results are both presented. The differences in simulation results remind us to pay more attention to MATLAB defaults and options when we conduct such simulations. © 2015 International Association for Mathematics and Computers in Simulation (IMACS).


Zhang Y.,Sun Yat Sen University | Zhang Y.,SYSU CMU Shunde International Joint Research Institute | Zhang Y.,Key Laboratory of Autonomous Systems and Networked Control | Qu L.,Sun Yat Sen University | And 5 more authors.
Soft Computing | Year: 2016

To solve complex problems such as multi-input function approximation by using neural networks and to overcome the inherent defects of traditional back-propagation neural networks, a single hidden-layer feed-forward sine-activated neural network, sine neural network (SNN), is proposed and investigated in this paper. Then, a double-stage weights and structure determination (DS-WASD) method, which is based on the weights direct determination method and the approximation theory of using linearly independent functions, is developed to train the proposed SNN. Such a DS-WASD method can efficiently and automatically obtain the relatively optimal SNN structure. Numerical results illustrate the validity and efficacy of the SNN model and the DS-WASD method. That is, the proposed SNN model equipped with the DS-WASD method has great performance of approximation on multi-input function data. © 2014, Springer-Verlag Berlin Heidelberg.


Qiu B.,Sun Yat Sen University | Qiu B.,SYSU CMU Shunde International Joint Research Institute | Qiu B.,Key Laboratory of Autonomous Systems and Networked Control | Zhang Y.,Sun Yat Sen University | And 4 more authors.
Advanced Robotics | Year: 2016

In this paper, by revisiting Ma et al.’s inspiring work (specifically, Ma equivalence, ME) and Zhang et al.’s inspiring work (specifically, Zhang equivalence, ZE), which both investigate the equivalence relationships of redundancy-resolution schemes at two different levels, but with different formulations, the general scheme formulations and equivalence analyses of ME and ZE are presented. Besides, being a case study, the ME and ZE of minimum velocity norm (MVN) type are investigated for the inverse-kinematics (IK) problem solving. Moreover, the link and difference between the MVN-type ME and ZE are analyzed, summarized and presented methodologically, systematically, and computationally in this paper. In order to numerically compare the ME and ZE of MVN type, a Rhodonea-path tracking task based on PUMA560 robot manipulator is tested and fulfilled by employing the original velocity-level MVN schemes and its equivalent acceleration-level MVN schemes of ME and ZE. The simulative and numerical results not only verify the effectiveness of the velocity-level and acceleration-level schemes of MVN-type ME and ZE, but also validate the reasonableness of such two proved equivalence relationships. More importantly, these results show quantitatively and comparatively the respective advantages and future applications of MVN-type ME and ZE for the IK problem solving. © 2016 Taylor & Francis and The Robotics Society of Japan


Mao M.,Sun Yat Sen University | Li J.,Sun Yat Sen University | Li J.,Key Laboratory of Autonomous Systems and Networked Control | Jin L.,Sun Yat Sen University | And 4 more authors.
Neurocomputing | Year: 2016

Inevitable noises and limited computational time are major issues for time-variant matrix inversion in practice. When designing a time-variant matrix inversion algorithm, it is highly demanded to suppress noises without violating the performance of real-time computation. However, most existing algorithms only consider a nominal system in the absence of noises, and may suffer from a great computational error when noises are taken into account. Some other algorithms assume that denoising has been conducted before computation, which may consume extra time and may not be suitable in practice. By considering the above situation, in this paper, an enhanced discrete-time Zhang neural network (EDTZNN) model is proposed, analyzed and investigated for time-variant matrix inversion. For comparison, an original discrete-time Zhang neural network (ODTZNN) model is presented. Note that the EDTZNN model is superior to ODTZNN model in suppressing various kinds of bias noises. Moreover, theoretical analyses show the convergence of the proposed EDTZNN model in the presence of various kinds of bias noises. In addition, numerical experiments including an application to robot motion planning are provided to substantiate the efficacy and superiority of the proposed EDTZNN model for time-variant matrix inversion. © 2016 Elsevier B.V.


Jin L.,Sun Yat Sen University | Jin L.,SYSU CMU Shunde International Joint Research Institute | Jin L.,Key Laboratory of Autonomous Systems and Networked Control | Zhang Y.,Sun Yat Sen University | And 6 more authors.
Neurocomputing | Year: 2016

The tracking-control problem of a special nonlinear system (i.e., the extension of a modified Lorenz chaotic system) with additive input or the mixture of additive and multiplicative inputs is considered in this paper. It is worth pointing out that, with the parameters fixed at some particular values, the modified Lorenz nonlinear system degrades to the modified Lorenz chaotic system. Note that, due to the existence of singularities at which the nonlinear system fails to have a well-defined relative degree, the input-output linearization method and the backstepping design technique cannot solve the tracking-control problem. By combining Zhang neural dynamics and gradient neural dynamics, a new effective controller-design method, termed Zhang-gradient (ZG) neural dynamics, is proposed for the tracking control of the modified Lorenz nonlinear system. With singularities conquered, this ZG neural dynamics is able to solve the tracking-control problem of the modified Lorenz nonlinear system via additive input or mixed inputs (i.e., the mixture of additive and multiplicative inputs). Both theoretical analyses and simulative verifications substantiate that the tracking controllers based on the ZG neural dynamics with additive input or mixed inputs not only achieve satisfactory tracking accuracy but also successfully conquer the singularities encountered during the tracking-control process. Moreover, the applications to the synchronization, stabilization and tracking control of other nonlinear systems further illustrate the effectiveness and advantages of the ZG neural dynamics. © 2016 Elsevier B.V.


Guo D.,Sun Yat Sen University | Guo D.,SYSU CMU Shunde International Joint Research Institute | Guo D.,Key Laboratory of Autonomous Systems and Networked Control | Zhang Y.,Sun Yat Sen University | And 11 more authors.
Neurocomputing | Year: 2015

Being two famous neural networks, the error back-propagation (BP) algorithm based neural networks (i.e., BP-type neural networks, BPNNs) and Hopfield-type neural networks (HNNs) have been proposed, developed, and investigated extensively for scientific research and engineering applications. They are different from each other in a great deal, in terms of network architecture, physical meaning and training pattern. In this paper of literature-review type, we present in a relatively complete and creative manner the common natures of learning between BP-type and Hopfield-type neural networks for solving various (mathematical) problems. Specifically, comparing the BPNN with the HNN for the same problem-solving task, e.g., matrix inversion as well as function approximation, we show that the BPNN weight-updating formula and the HNN state-transition equation turn out to be essentially the same. Such interesting phenomena promise that, given a neural-network model for a specific problem solving, its potential dual neural-network model can thus be developed. © 2015 Elsevier B.V.


Chen D.,Sun Yat Sen University | Chen D.,SYSU CMU Shunde International Joint Research Institute | Chen D.,Key Laboratory of Autonomous Systems and Networked Control | Zhang Y.,Sun Yat Sen University | And 2 more authors.
IET Control Theory and Applications | Year: 2016

In this study, a minimum jerk norm (MJN) scheme with an obstacle avoidance constraint is proposed and applied to a redundant robot arm, of which the joint jerks keep bounded for a human-friendly robot control. To achieve superior tracking performances of the redundant robot arm, the proposed jerk bounded MJN scheme is improved by the feedback control. More importantly, the effectiveness on obstacle avoidance of the proposed scheme is guaranteed by the variable-magnitude escape jerk theorem. Besides, for the purpose of implementation on the practical robot system, the corresponding discrete formulas with their theoretical analyses are presented. Then the proposed scheme is reformulated as a dynamical quadratic program which is solved by a piecewise-linear projection equation neural network. Furthermore, the path-tracking simulation and comparison substantiate the effectiveness and accuracy of such a scheme with the smooth and human-friendly joint variables applied to the obstacle avoidance of a six degrees of freedom jerk bounded robot arm. At last, the experimental application conducted on a practical redundant robot arm system further shows the physical realisability and the safety of the proposed scheme. © The Institution of Engineering and Technology 2016.

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