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Discrete time-variant nonlinear optimization and system solving via integral-type error function and twice ZND formula with noises suppressed

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SOFT COMPUTING 2018年第21期22卷 7129-7141页

作者： Shi, Yang Zhang, Yunong Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China SYSU CMU Shunde Int Joint Res Inst Foshan 528300 Peoples R China

In this paper, by using integral-type error function and twice zeroing neural-dynamics (or termed, Zhang neural-dynamics, ZND) formula, continuous-time advanced zeroing neural-dynamics (CT-AZND) model is proposed for solving the continuous time-variant nonlinear optimization problem. Furthermore, a discrete-time advanced zeroing neural-dynamics (DT-AZND) model is first proposed, analyzed, and investigated for solving the discrete time-variant nonlinear optimization (DTVNO) problem. Theoretical analyses show that the proposed DT-AZND model is convergent, and its steady-state residual error has an O(g(3)) pattern with g denoting the sampling gap. In addition, in the presence of various kinds of noises, the proposed DT-AZND model possesses advantaged performance. In detail, the proposed DT-AZND model converges toward the time-variant theoretical solution of the DTVNO problem with O(g(3)) residual error in the presence of an arbitrary constant noise and has excellent ability to suppress linear-form time-variant noise and bounded random noise. Illustrative numerical experiments further substantiate the efficacy and advantage of the proposed DT-AZND model for solving the DTVNO problem.

关键词： Discrete-time advanced zeroing neural-dynamics (DT-AZND) Discrete time-variant nonlinear optimization (DTVNO) Noise suppression Convergence Residual error

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An enhanced kernel learning data-driven method for multiple fault detection and identification in industrial systems

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INFORMATION SCIENCES 2022年 615卷 431-448页

作者： Sun, Chengyuan Ma, Hongjun Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China South China Univ Technol Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked ControlMinist Guangzhou 510641 Peoples R China

Traditional fault detection methods focus mainly on a single abnormal condition of the sys-tem. However, successive multiple faults are more common than a single fault in industrial systems. Hence, this paper proposes a novel algorithm for detecting and identifying mul-tiple faults associated with the quality indicators of the process. Considering the dynamic feature and measurement noise in the system, an enhanced kernel learning data-driven (EKLDD) algorithm is designed to improve the performance of modeling and multiple fault detection. In addition, a monitoring scheme is proposed to evaluate the quality status under every fault based on the fault line and the angle statistics. Lastly, a simulation case and a real-world case are presented to illustrate the feasibility and effectiveness of the pro-posed EKLDD method.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Dynamic behavior Quality-related Multiple fault detection Kernel learning

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Sine neural network (SNN) with double-stage weights and structure determination (DS-WASD)

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SOFT COMPUTING 2016年第1期20卷 211-221页

作者： Zhang, Yunong Qu, Lu Liu, Jinrong Guo, Dongsheng Li, Mingming Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China SYSU CMU Shunde Int Joint Res Inst Shunde 528300 Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China

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.

关键词： Sine neural network (SNN) Double-stage weights and structure determination (DS-WASD) Function approximation Linear independence

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Personalized Variable Gain control With Tremor Attenuation for Robot Teleoperation

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2018年第10期48卷 1759-1770页

作者： Yang, Chenguang Luo, Jing Pan, Yongping Liu, Zhi Su, Chun-Yi South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China Natl Univ Singapore Dept Biomed Engn Singapore 117583 Singapore Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

Teleoperated robot systems are able to support humans to accomplish their tasks in many applications. However, the performance of teleoperation largely depends on motor functionality and human operator's skill, especially when a human operator is short of skill training. In order to adapt to various unstructured environments for the robot system and the human operator, in this paper, a teleoperation scheme using integrated tremor attenuation with a variable gain control algorithm involving surface electromyogram is proposed to achieve personalized control performance and to reduce reliance on operator's skill. For attenuating tremor, a filter based on support vector machine is developed to guarantee normal operation. This filter depends on the machine learning scheme and does not rely on a priori filter parameters. Semiphysical experiments have been performed to demonstrate the effectiveness of the proposed methods.

关键词： Personalized control surface electromyographic (sEMG) teleoperated robot system tremor attenuation variable gain control

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New Discrete-Time ZNN Models for Least-Squares Solution of Dynamic Linear Equation System With Time-Varying Rank-Deficient Coefficient

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2018年第11期29卷 5767-5776页

作者： Qiu, Binbin Zhang, Yunong Yang, Zhi SYSU Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China SYSU CMU Shunde Int Joint Res Inst Foshan 528300 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Guangdong Peoples R China

In this brief, a new one-step-ahead numerical differentiation rule called six-instant g-cube finite difference (6IgCFD) formula is proposed for the first-order derivative approximation with higher precision than existing finite difference formulas (i.e., Euler and Taylor types). Subsequently, by exploiting the proposed 6IgCFD formula to discretize the continuous-time Zhang neural network model, two new-type discrete-time ZNN (DTZNN) models, namely, new-type DTZNNK and DTZNNU models, are designed and generalized to compute the least-squares solution of dynamic linear equation system with time-varying rank-deficient coefficient in real time, which is quite different from the existing ZNN-related studies on solving continuous-time and discrete-time (dynamic or static) linear equation systems in the context of full-rank coefficients. Specifically, the corresponding dynamic normal equation system, of which the solution exactly corresponds to the least-squares solution of dynamic linear equation system, is elegantly introduced to solve such a rank-deficient least-squares problem efficiently and accurately. Theoretical analyses show that the maximal steady-state residual errors of the two new-type DTZNN models have an O(g(4)) pattern, where g denotes the sampling gap. Comparative numerical experimental results further substantiate the superior computational performance of the new-type DTZNN models to solve the rank-deficient least-squares problem of dynamic linear equation systems.

关键词： Dynamic linear equation system least-squares solution new-type discrete-time Zhang neural network (DTZNN) models six-instant g-cube finite difference (6IgCFD) formula time-varying rank-deficient coefficient

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Neural network-based discrete-time Z-type model of high accuracy in noisy environments for solving dynamic system of linear equations

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NEURAL COMPUTING & APPLICATIONS 2018年第11期29卷 1217-1232页

作者： Jin, Long Zhang, Yunong Qiu, Binbin Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China SYSU CMU Shunde Int Joint Res Inst Foshan 528300 Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China

To solve dynamic system of linear equations with square or rectangular system matrices in real time, a discrete-time Z-type model based on neural network is proposed and investigated. It is developed from and studied with the aid of a unified continuous-time Z-type model. Note that the framework of such a unified continuous-time Z-type model is generic and has a wide range of applications, such as robotic redundancy resolution with quadratic programming formulations. To do so, a one-step-ahead numerical differentiation formula and its optimal sampling-gap rule in noisy environments are presented. We compare the Z-type model extensively with E-type and N-type models. Theoretical results on stability and convergence are provided which show that the maximal steady-state residual errors of the Z-type, E-type and N-type models have orders O(tau(3)), O(tau(2)), and O(tau), respectively, where tau is the sampling gap. We also prove that the residual error of any static method that does not exploit the time-derivative information of a time-dependent system of linear equations has order O(tau) when applied to solve discrete real-time dynamic system of linear equations. Finally, several illustrative numerical experiments in noisy environments as well as two application examples to the inverse-kinematics control of redundant manipulators are provided and illustrated. Our analysis substantiates the efficacy of the Z-type model for solving the dynamic system of linear equations in real time.

关键词： Recurrent neural network Discrete-time Z-type model Dynamic system of linear equations Noisy environments Residual error

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Neural network-based sliding mode adaptive control for robot manipulators

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NEUROCOMPUTING 2011年第14-15期74卷 2377-2384页

作者： Sun, Tairen Pei, Hailong Pan, Yongping Zhou, Hongbo Zhang, Caihong S China Univ Technol Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China S China Univ Technol Sch Automat Guangzhou 510640 Guangdong Peoples R China Qingdao Univ Sci & Technol Sch Automat Qingdao 266042 Peoples R China

This paper addresses the robust trajectory tracking problem for a robot manipulator in the presence of uncertainties and disturbances. First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator. It is shown using the Lyapunov theory that the tracking error asymptotically converge to zero. However, the assumption on the availability of the robot manipulator dynamics is not always practical. So, an NN-based adaptive observer is designed to estimate the velocities of the links. Next, based on the observer, a neural network-based sliding mode adaptive output feedback control (NNSMAOFC) is designed. Then it is shown by the Lyapunov theory that the trajectory tracking errors, the observer estimation errors asymptotically converge to zero. The effectiveness of the designed NNSMAC, the NN-based adaptive observer and the NNSMAOFC is illustrated by simulations. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Robot manipulators Neural network (NN) Sliding mode adaptive control Output feedback control

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New Discrete-Time Models of Zeroing Neural Network Solving systems of Time-Variant Linear and Nonlinear Inequalities

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2020年第2期50卷 565-576页

作者： Shi, Yang Zhang, Yunong Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China SYSU CMU Shunde Int Joint Res Inst Foshan 528300 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Peoples R China

In this paper, a new one-step-ahead numerical differentiation rule termed 5-instant discretization formula is proposed for the first-order derivative approximation with higher computational precision. Then, by exploiting the proposed formula to discretize the continuous-time zeroing neural network [or termed, continuous-time Zhang neural network (ZNN)] models, two new discrete-time zeroing neural network [or termed, discrete-time ZNN (DTZNN)] models are proposed, analyzed and investigated for solving systems of discrete time-variant inequalities, including the system of discrete time-variant linear inequalities and the system of discrete time-variant nonlinear inequalities. For comparative purposes, the recently developed Taylor-type DTZNN models and the widely used Euler-type DTZNN models are also presented. Theoretical analyses show that the proposed DTZNN models are convergent, and their steady-state residual errors have an O(g(4)) pattern with g denoting the sampling gap. Comparative numerical experimental results further substantiate the efficacy and superiority of the proposed DTZNN models for solving the systems of discrete time-variant inequalities.

关键词： 5-instant discretization formula (5IDF) convergence discrete-time zeroing neural network (DTZNN) residual error systems of time-variant inequalities

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A novel redesign framework to extend the application scope of a class of disturbance-rejection algorithms

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2020年第1期30卷 321-337页

作者： Yang, Hao Pei, Hailong South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China South China Univ Technol Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China

In this paper, a novel redesign method is developed for a class of disturbance-rejection algorithms so that they can be applied to pure-feedback nonaffine-in-control nonlinear systems with matched and mismatched disturbances. First, a series of augmented dynamical equations, which evolve faster than the original system, are constructed to establish a boundary-layer subsystem to derive the virtual and actual inputs for the nominal system. Then, the composite interconnected system is studied in the standard singular perturbed form. In the slow timescale, the reduced slow subsystem (RSS) is transformed into the chain of integrators form in the error coordinate, for which the existing antidisturbance methods can be employed. The tracking performance of the closed-loop system is approximated by RSS under singular perturbation theory. The proposed redesign method is adopted to three existing disturbance-rejection algorithms for a pure-feedback nonaffine-in-control numerical example in the presence of matched and mismatched disturbances. The effectiveness is demonstrated by simulation experimental results.

关键词： disturbance-rejection matched and mismatched disturbances nonaffine-in-control pure-feedback systems singular perturbation

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Robust wavelet network control for a class of autonomous vehicles to track environmental contour line

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NEUROCOMPUTING 2011年第17期74卷 2886-2892页

作者： Sun, Tairen Pei, Hailong Pan, Yongping Zhang, Caihong S China Univ Technol Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China S China Univ Technol Sch Automat Guangzhou 510640 Guangdong Peoples R China Qingdao Univ Sci & Technol Sch Automat Qingdao 266042 Peoples R China

We address the problem of environmental contour line tracking for a class of autonomous vehicles. A reference velocity is designed for the autonomous vehicles to do contour line tracking. Based on Lashall invariance principle, an ideal controller is designed for the vehicle with ideal model and ideal information about the environmental concentration function to track the desired contour line. For the vehicle with possibly modeling uncertainty, we combine a neural controller containing a wavelet neural network (WNN) identifier with a robust control to construct a robust adaptive WNN control for the vehicle to track the desired environmental contour line. Then we give theoretical proof of the efficiency of the designed robust adaptive WNN control. Simulation results and conclusion are presented and discussed. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Environmental contour line Wavelet neural network Robust control autonomous vehicles

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