This brief addresses the adaptive neural asymptotic tracking issue for uncertain non-strict feedback systems subject to full-state constraints. By introducing the significant nonlinear transformed function (NTF), the command filtered technology, and the boundary estimation method into control design, a novel command filtered backstepping adaptive controller is proposed. The proposed control scheme is able to not only deal with full-state constraints but also avoid the ``explosion of complexity'' issue. By means of a Lyapunov stability analysis, we prove that: 1) the tracking error asymptotically converges to zero; 2) all the variables in the controlled systems are bounded; and 3) all the states are constrained in the asymmetric predefined sets. Finally, a numerical simulation is used to demonstrate the validity of the proposed algorithm.
|Journal||IEEE Transactions on Neural Networks and Learning Systems|
|Publication status||Accepted/In press - 2022|
- Artificial neural networks
- Asymptotic tracking control
- command filter backstepping
- full-state constraints
- Lyapunov methods
- neural networks (NNs)
- non-strict feedback systems.
- Nonlinear systems
ASJC Scopus subject areas
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence