Dual-Error Transformation Approach to Prescribed Performance Control for Unknown Euler–Lagrange Systems with Actuator Faults

This paper addresses the intricate control challenges posed by unknown Euler–Lagrange systems operating under actuator faults and subject to asymmetric error constraints. A novel prescribed performance control (PPC) strategy is proposed, leveraging a dual-error transformation technique. The first transformation is designed to decouple the initial tracking errors from the predefined performance functions, thereby significantly relaxing the stringent initial condition dependencies typical of conventional PPC schemes and ensuring errors remain confined within adjustable asymmetric boundaries. The second transformation introduces exponential decay constraint functions to dynamically regulate the convergence rate and steady-state accuracy of the tracking errors. Theoretical analysis rigorously demonstrates that the proposed strategy, without requiring prior knowledge of the system’s complex nonlinearities, guarantees global boundedness of all closed-loop signals. Furthermore, it ensures that tracking errors converge to prespecified asymmetric residual zones at a preset rate, even in the presence of actuator faults. The efficacy and superiority of the proposed strategy are validated through comparative simulations conducted on a two-link robotic manipulator. The experimental source code is available at https:// github.com/hclll22/Dual-Error-Transformation-Approach-PPC.