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Designing a Completely Distributed Interval Observer for the LTI System

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第3期70卷 1793-1808页

作者： Wang, Xiaoling Xu, Weichen Su, Housheng Gao, Zhiwei Chen, Guanrong Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Jiangsu Engn Lab IOT Intelligent Robots IOTRobot Nanjing 210023 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Wuhan 430074 Peoples R China Univ Northumbria Fac Engn & Environm Newcastle Upon Tyne NE1 8ST England City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China

In this article, the distributed interval observer design problem for a continuous-time linear time-invariant system with unknown external disturbance and measurement noise is revisited. By performing detectability decomposition for partial outputs of each sensor from the system matrix, a classical interval observer and an internally positive representation-based interval observer are designed for the detectable and the undetectable subsystems, respectively, which reduces the order of the resulting distributed interval observer. Furthermore, with a dynamic coupling gain on the communications among the interval observers, a completely distributed interval observer is designed without any prerequisite, except for the joint detectability of the partial measurements and the strong connectivity of the properly defined directed sensor network. Finally, after detailed analyses, the theoretical results were validated through numerical simulations.

关键词： Observers Measurement uncertainty Uncertainty Noise measurement Linear systems Observability Noise Intellectual property Time measurement Performance evaluation Bounded information distributed interval observer internally positive representation linear time-invariant (LTI) system order reduction

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Particle filtering-based recursive identification for controlled auto-regressive systems with quantised output

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IET CONTROL THEORY AND APPLICATIONS 2019年第14期13卷 2181-2187页

作者： Ding, Jie Chen, Jiazhong Lin, Jinxing Jiang, Guoping Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Jiangsu Engn Lab IOT Intelligent Robots Nanjing 210023 Jiangsu Peoples R China

Recursive prediction error method is one of the main tools for analysis of controlled auto-regressive systems with quantised output. In this study, a recursive identification algorithm is proposed based on the auxiliary model principle by modifying the standard stochastic gradient algorithm. To improve the convergence performance of the algorithm, a particle filtering technique, which approximates the posterior probability density function with a weighted set of discrete random sampling points is utilised to correct the linear output estimates. It can exclude those invalid particles according to their corresponding weights. The performance of the particle filtering technique-based algorithm is much better than that of the auxiliary model-based one. Finally, results are verified by examples from simulation and engineering.

关键词： stochastic processes parameter estimation least squares approximations filtering theory gradient methods autoregressive processes recursive estimation probability particle filtering (numerical methods) auxiliary model principle standard stochastic gradient algorithm novel particle filtering technique posterior probability density function discrete random sampling points linear output estimates invalid particles particle filtering technique-based algorithm auxiliary model-based particle filtering-based recursive identification controlled auto-regressive systems quantised output recursive prediction error method main tools novel recursive identification algorithm

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Measuring Low-Order Photometric Parameters of Light Fields: Methods Exploration and Simulations

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IEEE ACCESS 2020年 8卷 97408-97417页

作者： Xia, Ling Tian, Tian Xu, Ruipeng Zhang, Tingting Liu, Xiaofeng Hohai Univ Coll IoT Engn Jiangsu Key Lab Robot & Intelligent Technol Changzhou 213022 Peoples R China

Low-order photometric parameters of light fields are important aspects determining lighting qualities but difficult to be measured. In this article, we did a systematical investigation into the performance of a cubic meter and a tetrahedron meter as well as an HDR panoramic map-based method in simultaneously recovering light density, direction, and diffuseness. Five metrics were introduced with two based on the measurement using a cubic illumination meter, two based on a tetrahedron shaped illumination meter and one based on SH decomposition of HDR panoramic maps. Furthermore, the measurement of five metrics was simulated under six HDR Panoramic maps of natural scenes in various postures which mimic the complexity of real environments. The results indicate that the measurement of low-order photometric parameters of light fields using Spherical Harmonics (i.e. latter referred to as SH) decomposition of HDR panoramic map gave the most reliable results. The tetrahedron meter based metrics gave more robust results in the measurement of light density while the cubic meter based metrics performed better when measuring light direction. This study also provides a solution for a more robust measurement of light diffuseness based on the ratio between the vector strength measured with a cubic meter and light density measured with a tetrahedron meter. With the quick development of high-speed built-in camera and mobile computing techniques, this research provides confidence in developing applications in mobile phone by capturing HDR panoramic maps or built in light meters to measure the low-order photometric parameters of light fields in 3D spaces.

关键词： Lighting light density light direction light diffuseness HDR panoramic map spherical harmonics mobile application cubic meter tetrahedron meter 360-degree picture

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Threshold image segmentation based on improved sparrow search algorithm (Apr, 10.1007/s11042-022-13073-x, 2022)

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第23期81卷 33547-33547页

作者： Wu, Dongmei Yuan, Chengzhi Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Jiangsu Engn Lab IOT Intelligent Robots Nanjing Peoples R China

Threshold segmentation based on swarm intelligence optimization algorithm is a research hotspot in image processing, because of its good segmentation effect and easy implementation. This paper proposes an image threshold segmentation method based on an improved sparrow search algorithm and 2-D maximum entropy method. In the proposed algorithm, the nonlinear inertia weight is introduced into the entrants’ update formula to improve the local exploration ability of the algorithm, and Levy flight is introduced into the vigilant sparrows’ update formula to prevent the algorithm from falling into the local optimal solution in the later stage of iteration. In addition, improved sparrow search algorithm is tested on fifteen benchmark functions. The results represent the merit of the proposed algorithm with respect to other algorithms. Finally, the proposed algorithm is applied to entropy based image segmentation. Experiment results on classical images and medical images show that the proposed method improves the segmentation effect in terms of peak signal-to-noise ratio and feature similarity.

关键词： 2-D histogram Levy flight Maximum entropy Nonlinear weight Swarm optimization algorithm Threshold image segmentation

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Convergence analysis of the modified adaptive extended Kalman filter for the parameter estimation of a brushless DC motor

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2021年第16期31卷 7606-7620页

作者： Ding, Jie Chen, Lijuan Cao, Zhengxin Guo, Honghao Nanjing Univ Posts & Telecommun Jiangsu Engn Lab IOT Intelligent Robots Sch Automat & Artificial Intelligence Nanjing Peoples R China

This article concentrates on the parameter estimation of brushless DC motor, where the stator current and winding back electromotive force are taken as the motor states, while the stator resistance and inductance are taken into consideration and augmented into the state vector. Based on this augmented model, a modified adaptive extended Kalman filter is proposed which updates the process noise covariance matrix in real time with the current input-output data, and takes the state estimates by the traditional extended Kalman filter as one-step estimation for the calculation of the covariance matrix. The convergence analysis is given to verify the theoretical results. Finally, the simulation results show that the proposed algorithm can effectively improve the estimation accuracy.

关键词： back electromotive force brushless DC motor convergence analysis nonlinear Kalman filter

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Weighted Parameter Estimation for Hammerstein Nonlinear ARX Systems

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2020年第4期39卷 2178-2192页

作者： Ding, Jie Cao, Zhengxin Chen, Jiazhong Jiang, Guoping Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Jiangsu Engn Lab IoT Intelligent Robots Nanjing 210023 Peoples R China

This paper proposes parameter estimation algorithms for Hammerstein nonlinear ARX systems. By making full use of the current and previous input-output data of the system, a weighted multi-innovation stochastic gradient algorithm is presented to improve the convergence rate of identification. The innovation term in the traditional identification algorithms can be treated as a particle in the particle-filtering technique, and the weight of each innovation then can be computed according to their importance. The simulation results indicate that the algorithm can improve the accuracy of parameter estimation.

关键词： Hammerstein system ARX system Multi-innovation identification Particle-filtering technique

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Improved sliding mode based EKF for the SOC estimation of lithium-ion batteries

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IONICS 2020年第6期26卷 2875-2882页

作者： Feng, Liang Ding, Jie Han, Yiyang Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Jiangsu Engn Lab IOT Intelligent Robots Nanjing 210023 Peoples R China

This paper combines the discrete sliding mode observer with the weighted innovation extended Kalman filter to improve the accuracy of the SOC estimation. The main work of this paper can be divided into two parts: (1) The proposed algorithms utilize the previous information and the current innovation by choosing proper weights to estimate the SOC accurately. (2) The improved discrete sliding mode observer is introduced into the weighted innovation extended Kalman filter to solve the chattering problem. The experimental results show that the accuracy of the SOC estimation is improved effectively.

关键词： Discrete sliding mode observer Weighted innovation extended Kalman filter State of charge estimation

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Discrete fractional order PID controller design for nonlinear systems

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2021年第15期52卷 3206-3213页

作者： Li, Zhijie Ding, Jie Wu, Min Lin, Jinxing Nanjing Univ Posts & Telecommun Jiangsu Engn Lab IOT Intelligent Robots Sch Automat & Artificial Intelligence Nanjing 210023 Peoples R China

In this study, a discrete fractional order PID controller for nonlinear systems is proposed. U-model is firstly employed to transform nonlinear systems into a linear-like model and model complexity is then degraded and easier for controller design. Then, a new discrete fractional order PID control law in difference equation is presented which takes advantage of two more adjustable parameters. Finally, robust stability of the control law is analysed. Both deterministic and stochastic examples are provided to illustrate the effectiveness and superiority of the proposed controller.

关键词： Discrete nonlinear system fractional order PID controller U-model robust stability

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Composite Control of Post-Chlorine Dosage During Drinking Water Treatment

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IEEE ACCESS 2019年 7卷 27893-27898页

作者： Wang, Dongsheng Xiang, Hao Jiangsu Engn Lab IOT Intelligent Robots Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Automat Nanjing 210023 Jiangsu Peoples R China

Chlorination is an effective disinfection method of drinking water treatment. Optimizing this process poses certain challenges, as the objectives of reaching the required disinfection effect and limiting the formation of disinfection by-products must be met simultaneously. These need adjusting the chlorine dosage in real time to cope with fluctuations in water quality and maintaining a free chlorine residual at the clear-water reservoir outlet that does not exceed regulatory limits. This paper proposes a composite control scheme that combines disturbance observer (DOB) and model predictive control (MPC) to meet the aforementioned objectives. MPC is employed for the feedback control of post-chlorine dosage, and DOB is adapted to monitor fluctuations in influent water quality. The estimated value from the DOB is applied in the feed-forward control of post-chlorine dosage. Both simulation and experimental results demonstrate that the proposed DOB-MPC composite control scheme provides a better disturbance rejection effect against fluctuations in influent water quality than MPC-only and PID-only control schemes, and it can effectively stabilize the quality of treated water.

关键词： Post-chlorine dosage composite control disturbance observer model predictive control

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intelligent Ocean Convergence Platform Based on iot Empowered with Edge Computing

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JOURNAL OF INTERNET TECHNOLOGY 2020年第1期21卷 235-244页

作者： Liang, Mingzhou Su, Xin Liu, Xiaofeng Zhang, Xuewu Hohai Univ Coll IOT Engn Nanjing Peoples R China Jiangsu Key Lab Special Robots Changzhou Peoples R China Changzhou Key Lab Special Robots & Intelligent Te Changzhou Peoples R China

The ocean is currently crowded with vessels, including but not limited to commercial ships and submarines used for military operations or scientific investigations. Each of these vessels and their on-board equipment produce a massive amount of data that need to be shared with potential destinations. The current popular intelligent Ocean Convergence Platform is suggested to support oceanic services by taking advantage of the novel concepts of the Internet of Things and 5G communications. However, the processing activities are not always centrally performed within the cloud but are sometimes shifted to the edge of the network according to edge computing. In this paper, we propose a combination of software-defined networking and edge computing, where software technology is used to support interoperability of heterogeneous network technologies, as well as edge computing enables ultra-reliability, scalability, and low latency in ocean networks. This will meet the rapid growth of marine vessels' demand for rapid computing and communication capabilities. Through the simulation of the average end-to-end delay, the efficiency of the proposed architecture based edge computing is evaluated.

关键词： Ocean network Edge computing SDN SANET End-to-end delay

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