Broad Fuzzy Neural Network-Based Prescribed-Time Control for a Flexible Single-Link Manipulator With Multiple Constraints
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Updated: Nov 8, 2026

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Published on: November 14, 2015
Abstract:
Flexible manipulator control can be impacted by vibrations alongside input and output constraints, potentially increasing safety risks or causing serious accidents. This study develops a broad fuzzy neural network (BFNN)-based prescribed-time control (PTC) scheme for a flexible single-link manipulator (FSLM) subject to flexible-link vibration, a compound actuator nonlinearity comprising dead-zone and saturation, and output constraints. The BFNN is employed specifically to estimate and compensate for the actuator-induced mismatch between the commanded and actual control inputs. A two-domain stability analysis establishes semi-global practical finite-time stability (SGPFS) in the transformed time domain and shows that the tracking errors enter an adjustable neighborhood within the user-assigned time in the original time domain. Meanwhile, a barrier Lyapunov function (BLF) ensures that the constrained tracking errors remain within their prescribed bounds. The simulation and hardware experimental results demonstrate prescribed-time tracking, constraint satisfaction, vibration suppression, and practical implementability.
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