Computational study demonstrates practical prescribed-time tracking in non-strict-feedback nonlinear systems, suggesting reliable control without singularities or initial state constraints.
In this paper, we investigate the practical prescribed‐time (PPT) tracking control problem for a class of nonlinear non‐strict‐feedback systems with arbitrarily bounded initial states. A radial basis function neural network (RBFNN) is employed to approximate unknown and possibly non‐differentiable system dynamics. By introducing a preset error trajectory, the tracking error is guided along a predefined evolution, it not only avoids the singularities and parameter‐coupling issues commonly associated with time‐varying mappings but also allows arbitrarily bounded initial errors. A Lyapunov‐like function is further constructed to guarantee closed‐loop stability while implicitly accommodating the filtering errors generated by the dynamic surface control (DSC) and maintaining bounded transient behavior. Based on Lyapunov analysis, all closed‐loop signals are shown to be uniformly bounded. The effectiveness of the proposed method is demonstrated through two illustrative examples.
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Jin et al. (2026) studied this question.
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