AI Article Synopsis
- The brief discusses a new control design for uncertain non-strict feedback systems that also consider full-state constraints.
- The proposed command filtered backstepping adaptive controller uses techniques like nonlinear transformed functions, command filtering, and boundary estimation to effectively manage complexities in control systems.
- The approach is validated through Lyapunov stability analysis, ensuring that tracking errors reduce to zero, all variables remain bounded, and states stay within predefined limits, with numerical simulations confirming the algorithm's effectiveness.
Article Abstract
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.
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| http://dx.doi.org/10.1109/TNNLS.2022.3141091 | DOI Listing |
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