期刊
NONLINEAR DYNAMICS
卷 105, 期 4, 页码 3167-3189出版社
SPRINGER
DOI: 10.1007/s11071-021-06757-w
关键词
State estimator; Prescribed performances; Neural network; Formation; Underactuated quadrotors
资金
- National Natural Science Foundation of China [61803348]
- National Nature Science Foundation of China as National Major Scientific Instruments Development Project [61927807]
- State Key Laboratory of Deep Buried Target Damage [DXMBJJ2019-02]
- Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi [2020L0266]
- Shanxi Province Science Foundation for Youths [201701D221123]
- Youth Academic North University of China [QX201803]
- Program for the Innovative Talents of Higher Education Institutions of Shanxi
- Shanxi 1331 Project Key Subjects Construction [1331KSC]
This paper investigates a distributed adaptive formation control problem for underactuated quadrotors with guaranteed performances, achieving a robust and stable formation pattern with predefined behavior bounds by introducing a prescribed performance mechanism and adaptive consensus strategy.
This paper investigates a distributed adaptive formation control problem for underactuated quadrotors with guaranteed performances. To ensure a robust and stable formation pattern with predefined behavior bounds, by transforming the original constrained formation synchronization error dynamics into an equivalent unconstrained one, a prescribed performance mechanism is introduced in the translational loop to render the formation regulation as a prior. An adaptive consensus strategy is developed according to undirected graph theory and Lyapunov stability rules for follower quadrotors to achieve a distributed cooperative formation with prescribed tracking abilities via exchanging local information with neighbors. The presented control scheme has the following salient merits: (1) the formation synchronization errors can be guaranteed within pre-assigned bounds with desired transient behaviors despite of uncertain disturbances; (2) by using a state estimation error to update neural network (NN) parameters, rather than the tracking error that widely applied in traditional NN approximators, and with the help of MLP technique, the proposed SE-MLP observer capable of decreasing the computational complexity can achieve a fast identification of lumped disturbances without causing high-frequency oscillations even using a large adaptive gain, and the transient solutions of L-2 norm of the differential of neural weights are established to illustrate the mechanism of SE-MLP observer in reducing chattering behaviors. The merits of presented algorithm are confirmed by sufficient simulations.
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