<p>Vehicle suspension systems are crucial for ensuring passenger comfort, vehicle stability, and overall safety. Magnetorheological (MR) suspension systems have garnered significant attention in the industry due to their fast response and broad adjustability. However, magnetorheological dampers (MRDs) exhibit substantial nonlinear saturation effects, including hysteresis, bi-viscosity, and the Stribeck effect. These nonlinearities limit the full potential of MRD performance and present significant challenges in MRD controller design. In this paper, a load-dependent multi-model affine H∞ robust controller (LDMA-H∞) is proposed to improve the performance of MR suspension system and overcome its nonlinear constraints within MRD. A novel multi-model segmentation approach using a Takagi–Sugeno fuzzy neural network is developed to transform the nonlinear constraints into manageable linear subsystem constraints. An affine H∞ robust controller based on a multi-model Lyapunov function is then designed. The affine term is incorporated into the feedback control law to address damping constraint asymmetry. Furthermore, the multi-model Lyapunov functions ensure system stability under varying loads, reducing the control conservatism. Finally, a real vehicle experiment is completed to identify the effectiveness of the proposed control method. The results show that the LDMA-H∞ controller effectively reduces control conservatism and enhances the ride comfort and handling stability under varying loads for vehicles.</p>