IPSO-Optimized DE-MFAC Strategy for Suspension Servo Actuators Under Compound-Degradation Faults

The dynamic response accuracy of suspension servo actuators directly determines the vibration-reduction performance of active-suspension systems. However, during long-term service, the system is prone to the influence of compound-degradation faults, such as internal leakage and time delay, leading to a significant decline in control performance. To address this issue, this paper proposes a collaborative control framework combining model-free adaptive control with a differential term of tracking error (DE-MFAC) and an improved particle swarm optimization (IPSO) algorithm. Firstly, to overcome the limitations of traditional model-free adaptive control (MFAC), a DE-MFAC strategy is constructed by implicitly handling the time-delay term and introducing the differential term of tracking error and dynamic weight factor into the performance index. Secondly, to enhance the parameter-tuning effect, the traditional particle swarm optimization (PSO) algorithm is improved (IPSO) by incorporating a dynamic inertia weight and an out-of-bounds random reflection mechanism, thereby strengthening the global optimization capability. On this basis, a suspension servo actuator system model incorporating internal leakage and time-delay faults is established based on the co-simulation platform of Simulink and AMESim, and the proposed method is validated. The simulation results show that, compared with the optimized traditional MFAC, the DE-MFAC tuned by IPSO exhibits superior position-tracking accuracy, faster response speed, and stronger overshoot-suppression capability under various compound-fault conditions. Further analysis indicates that the Integral of Absolute Cubic Error (IACE) function, due to its higher sensitivity to large deviations, can more effectively suppress overshoot and is suitable for engineering scenarios with strict requirements on dynamic performance. In addition, the optimization of control parameters using the IPSO algorithm can effectively compensate for the performance degradation caused by degradation faults, providing a feasible technical approach for extending the service life of actuators through adaptive adjustment.