Modeling and trajectory tracking control for flapping-wing micro aerial vehicles

This article focuses on the identification of switched nonlinear systems, which are characterized as a collection of nonlinear dynamical systems. Each nonlinear subsystem is activated by a discrete-valued variable (switching signal). Specifically, we consider the continuous-time switched nonlinear systems in the state-space form in our article. The identification of switched nonlinear systems amounts to simultaneous estimation of the switching signal and the nonlinear dynamic subsystems via all measured state-input vectors. However, the problem is challenging and generally requires a large computational complexity to be solved. In this article, we propose a novel online approach to address the identification problem of switched nonlinear systems, which is capable to handle the measured state-input vectors in sequence. In particular, the principle used for estimating the switching signal is developed based on the subspace method. Subsequently, the integral concurrent learning identifier is extended to identify the dynamics of each subsystem recursively. The effectiveness of the proposed identification approach is demonstrated via simulation results.

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“…State space trajectory generated by the switched nonlinear system (27), whose color represents the corresponding subsysteṁ…”

Section: F I G U R Ementioning

confidence: 99%

“…[23][24][25] It is widely used to control the nonlinear or linear systems with varying parameters and initially uncertain, 26 especially for aircraft flies. [27][28][29] In this regard, we focus on the online identification approach for the continuous-time switched nonlinear systems in state-space form.…”

Section: Introductionmentioning

confidence: 99%

A novel recursive approach for online identification of continuous‐time switched nonlinear systems

Liu

Qiu

et al. 2021

Intl J Robust & Nonlinear

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“…(i) Most papers are designed for linear CPSs, but they are not applicable to nonlinear cyber-physical systems under false data injection attacks. (ii) Like human-computer interaction systems [12][13][14], robot systems [15][16][17] and bionic systems [18][19][20], NCPSs often have strict constraints on some critical states and output signals, but the current studies do not involve output constraints of NCPSs under false data injection attacks. Hence, how to realize the output constraint control of NCPSs under false data injection attacks is an urgent problem to be solved.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural network control for nonlinear cyber-physical systems subject to false data injection attacks with prescribed performance

Liu

Tang

Zhao

et al. 2021

Phil. Trans. R. Soc. A.

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Cyber-physical systems (CPSs), as emerging products of industry 4.0 , play a key role in the development of intelligent manufacturing. This paper proposes an observer-based adaptive neural network (NN) control for nonlinear strict-feedback CPSs subject to false data injection attacks. Since there may be strict constraints on the state or output signals of nonlinear cyber-physical systems (NCPSs), we propose a time-varying asymmetric barrier Lyapunov function to realize the specific output constraints of NCPSs under cyber-attacks. Besides, since false data injection attacks will corrupt the transmitted state variables, an observer is designed to obtain observations of the exact states, and NN is used to approximate the unknown nonlinearity of NCPSs. With the proposed control strategy, the constraint control problem of NCPSs subject to false data injection attacks is settled. Finally, a numerical simulation example verifies the effectiveness of the proposed controller. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

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“…Controller design in pHRI has been extensively studied in the field of pHRI [13,14], and many controllers have been proposed in robots for improving trajectory tracking performance considering constraints and external disturbances [15][16][17][18][19][20][21][22]. Some researchers focus on solving uncertain issues in robot dynamics from the engineering point of view [23][24][25][26]. Co-carrying tasks have been extensively studied in recent years.…”

Section: Introductionmentioning

confidence: 99%

Co-carrying an object by robot in cooperation with humans using visual and force sensing

Zhang

Liu

et al. 2021

Phil. Trans. R. Soc. A.

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Human–robot collaboration poses many challenges where humans and robots work inside a shared workspace. Robots collaborating with humans indirectly bring difficulties for accomplishing co-carrying tasks. In our work, we focus on co-carrying an object by robots in cooperation with humans using visual and force sensing. A framework using visual and force sensing is proposed for human–robot co-carrying tasks, enabling robots to actively cooperate with humans and reduce human efforts. Visual sensing for perceiving human motion is involved in admittance-based force control, and a hybrid controller combining visual servoing with force feedback is proposed which generates refined robot motion. The proposed framework is validated by a co-carrying task in experiments. There exist two phases in experimental processes: in Phase 1 , the human hand holds one side of the box object, and the robot gripper of the Baxter robot automatically approaches to the other side of the box object and finally holds it; in Phase 2 , the human and the Baxter robot co-carry the box object over a distance to different target positions. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

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Modeling and trajectory tracking control for flapping-wing micro aerial vehicles

Cited by 272 publications

References 38 publications

A novel recursive approach for online identification of continuous‐time switched nonlinear systems

A novel recursive approach for online identification of continuous‐time switched nonlinear systems

Adaptive neural network control for nonlinear cyber-physical systems subject to false data injection attacks with prescribed performance

Co-carrying an object by robot in cooperation with humans using visual and force sensing

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