Type‐3 Adaptive Neuro‐Fuzzy Event‐Triggered Prescribed‐Performance Control of Uncertain Manipulators: Restartable Funnels and Saturation‐Aware Anti‐Windup

This paper develops a unified and PLC‐realizable control framework for prescribed‐performance tracking of uncertain manipulator systems under sampled‐data execution. The proposed design integrates a restartable prescribed‐performance control (PPC) mechanism, a scan‐based periodic event‐triggered control (PETC) strategy, and a type‐3 adaptive neuro‐fuzzy inference system (T3‐ANFIS) within a backstepping architecture. Unlike conventional PPC approaches, the proposed formulation removes the restrictive requirement on admissible initial tracking errors and introduces a measurable restart mechanism that guarantees reentry of the tracking error into the prescribed‐performance funnel within a designer‐specified finite time after any performance violation. To ensure practical deployability, a PLC‐compatible PETC scheme is constructed, where triggering decisions are evaluated only at scan instants and incorporate hysteresis, deadzone protection, and dwell‐time constraints, thereby achieving noise‐robust and nonchattering operation with reduced update frequency. Furthermore, the T3‐ANFIS approximation is not used solely for function estimation but is explicitly structured to provide a decomposed representation of nominal dynamics and uncertainty width, which is directly embedded into the Lyapunov‐based stability analysis. The resulting closed‐loop system exhibits hybrid characteristics due to the interaction of sample‐and‐hold execution and restart events, for which boundedness of all signals and prescribed‐performance recovery are rigorously established. Comparative simulations and PLC‐style real‐time implementations demonstrate accurate tracking under disturbances and saturation, guaranteed recovery after perturbations, and a significant reduction in control updates, confirming the effectiveness and practical applicability of the proposed framework.