A Bioinspired Filtered Backstepping Tracking Control of 7000-m Manned Submarine Vehicle

Sun, Bing; Zhu, Daqi; Yang, Simon X.

doi:10.1109/tie.2013.2267698

Cited by 134 publications

(65 citation statements)

References 30 publications

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“…The simulation results show that the proposed algorithm provides more accurate parameter estimation than the MISG algorithm at the expense of a little increase in computation. The basic idea of the proposed method can be extended to study other linear or linear-in-parameter systems [15] and nonlinear systems [18,33], and applied to other fields [25,35,36].…”

Section: Discussionmentioning

confidence: 99%

Data Filtering-Based Multi-innovation Stochastic Gradient Algorithm for Nonlinear Output Error Autoregressive Systems

Mao

Ding

2015

Circuits Syst Signal Process

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This paper discusses the parameter estimation problems of nonlinear output error autoregressive systems and presents a data filtering-based multi-innovation stochastic gradient algorithm for improving the parameter estimation accuracy of the stochastic gradient algorithm by combining the multi-innovation identification theory and the data filtering technique. The proposed algorithm is effective and can generate highly accurate parameter estimates compared with the multi-innovation stochastic gradient algorithm. The simulation results confirm this conclusion.

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Section: Discussionmentioning

confidence: 99%

Data Filtering-Based Multi-innovation Stochastic Gradient Algorithm for Nonlinear Output Error Autoregressive Systems

Mao

Ding

2015

Circuits Syst Signal Process

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“…An outstanding representative of Chinese-manned submersible vehicles is "Jiaolong" shown in Fig. 1(a), which has been used for scientific investigation below the ocean's surface to a depth of 7000 m [2].…”

Section: Development Of a Virtual Platform For Telepresence Control Omentioning

confidence: 99%

“…then the joint velocity vector and the joint acceleration vector can be obtained by solving the first derivative and second derivative to (2). One can obtain the output moments of each joint along with time by plugging the conditions above and the data in Table I into (1), as shown in Fig.…”

mentioning

confidence: 99%

Development of a Virtual Platform for Telepresence Control of an Underwater Manipulator Mounted on a Submersible Vehicle

Zhang

et al. 2017

IEEE Trans. Ind. Electron.

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Abstract-This paper develops a virtual platform of an underwater manipulator mounted on a submersible vehicle via the three-dimensional simulator "Webots" for teleoperation through a replica master arm. The graphical, kinematic, and dynamic models of the manipulator refer to a master-slave servo hydraulic manipulator with seven functions, consisting of six degrees of freedom and a parallel gripper, while the "Jiaolong" deep-manned submersible vehicle, operating below the sea surface down to 7000 m, is chosen as the underwater manipulator carrier. This study uses the virtual platform for training an operator to telepresence control the virtual manipulator to complete basic tasks in subsea environments. When training the operator, one has to consider uncertain external disturbances and the visual impacts that stem from subsea environments. In order to demonstrate the feasibility and effectiveness of the virtual platform, one designs two typical underwater operational tasks: grasping a marine organism sample and reaching at a given position. This paper presents the comparative studies: 1) the performances demonstrated by remotely controlling the virtual manipulator and the real manipulator; 2) the operating performances delivered by three operators before and after training when using the platform.Index Terms-Telepresence control, training an operator, underwater manipulator, virtual platform.

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“…The filtering technique has been applied to many fields, such as adaptive control [25,32], fault detection [1,48] and system identification [4,11,16]. Wang proposed the least squares-based recursive and iterative algorithms for output error systems by combining the auxiliary model idea with the data filtering [39].…”

Section: Introductionmentioning

confidence: 99%

Joint Estimation of States and Parameters for an Input Nonlinear State-Space System with Colored Noise Using the Filtering Technique

Wang

Ding

2015

Circuits Syst Signal Process

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This paper concerns the state and parameter estimation problem for an input nonlinear state-space system with colored noise. By using the data filtering and the over-parameterization technique, we transform the original nonlinear state-space system into two identification models with filtered states: one containing the system parameters and the other containing the noise model's parameters. A combined state and parameter estimation algorithm is developed for identifying the state-space system. The key is that the estimation of system parameters uses the estimated states, and the estimation of states uses the preceding parameter estimates. A simulation example is provided to show that the proposed algorithm can work well.

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A Bioinspired Filtered Backstepping Tracking Control of 7000-m Manned Submarine Vehicle

Cited by 134 publications

References 30 publications

Data Filtering-Based Multi-innovation Stochastic Gradient Algorithm for Nonlinear Output Error Autoregressive Systems

Data Filtering-Based Multi-innovation Stochastic Gradient Algorithm for Nonlinear Output Error Autoregressive Systems

Development of a Virtual Platform for Telepresence Control of an Underwater Manipulator Mounted on a Submersible Vehicle

Joint Estimation of States and Parameters for an Input Nonlinear State-Space System with Colored Noise Using the Filtering Technique

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