Fault-Tolerant Prescribed-Performance Intelligent Control of Fractional-Order Nonlinear Systems with State Error Constraints: Application to Electromechanical Dynamics of Electric Vehicles Motors

The control performance of permanent magnet brush (PMB) DC motors, which are vital components in electric vehicles (EVs), is fundamental to ensuring the smooth, efficient, and safe operation of these vehicles under varying working conditions. This study investigates intelligent control of fractional-order nonlinear systems (FONSs) subject to full-state error constraints, mismatched disturbances, and actuator failure, with the electromechanical dynamics of PMB DC motors serving as a representative application. An error-dependent transformation is proposed to reformulate the constrained tracking control problem into an equivalent unconstrained formulation with deferred error bounds, thereby eliminating the requirement that initial state errors satisfy predefined constraints. Unlike conventional backstepping-based approaches, the proposed strategy avoids the computation of higher-order derivatives of the reference trajectory, which significantly reduces implementation complexity and practical limitations. To compensate for unknown mismatched disturbances, an adaptive estimation law is developed to online approximate their unknown upper bounds in real time. Based on the transformed fractional-order system, a recursive intelligent control scheme is systematically constructed. By employing Lyapunov stability theory in conjunction with Barbalat’s lemma, it is rigorously proven that all state errors enter a prescribed bounded region within finite time and asymptotically converge to zero. The simulation results conducted on a PMB DC motor model demonstrate improved transient performance, enhanced robustness, and reliable constraint satisfaction compared with existing barrier Lyapunov function-based and adaptive control methods.