Intelligent optimisation design of hydraulic presses for composite material forming based on the synergy of an enhanced particle swarm fuzzy control algorithm and an adaptive robust controller

The production of composite automotive leaf springs presents significant challenges in terms of control accuracy, stemming from the coupled effects of large-range load fluctuations and parameter uncertainties. This paper therefore proposes a synergistic control strategy that integrates a nonlinear adaptive robust controller (ARC) with an improved particle swarm optimisation (PSO) fuzzy PID algorithm. This architecture uses the ARC to establish strict physical boundaries, thereby eliminating model uncertainties. At the same time, the improved PSO fuzzy logic with dynamic inertia weighting based on fitness indices enables control rules to be reconstructed in milliseconds to suppress random external disturbances. Optimisation results show that this strategy greatly improves system robustness and sensitivity. Experimental validation shows displacement tracking error control of ≤0.05 mm and velocity tracking error control of ≤0.07 mm/s. Compared to conventional PID control, steady-state error decreases from ±0.05 mm to ±0.01 mm, substantially improving the consistency of high-performance automotive composite moulding quality.