Adaptive disturbance rejection control of magnetorheological seat suspension systems based on adaptive neuro-fuzzy inference system inverse modeling and improved crow search optimization

Abstract. Operators of agricultural vehicles are frequently exposed to prolonged vibrations, which pose serious health risks and degrade operational performance. Magnetorheological (MR) dampers have been increasingly adopted in seat suspension systems owing to their low power consumption and fast response. However, their inherent nonlinear hysteresis and time-varying dynamics significantly challenge the effectiveness of conventional control strategies. To overcome these limitations, this study proposes a hybrid ICSA-ANFIS-ADRC control framework that integrates an improved crow search algorithm (ICSA)-optimized adaptive neuro-fuzzy inference system (ANFIS) with an active disturbance rejection control (ADRC) strategy. The nonlinear behavior of the MR damper is first modeled using an improved Bouc–Wen approach. An inverse model is then constructed through ICSA-ANFIS training to predict the control current accurately. This inverse model is subsequently embedded within the ADRC framework to enable real-time multi-modal damping force regulation. Numerical simulations based on a 3-degrees-of-freedom seat suspension model demonstrate that the proposed method significantly outperforms conventional ANFIS-ADRC and CSA-ANFIS-ADRC controllers. Specifically, up to 32.9 % reduction in vertical vibration acceleration is achieved, while robust control performance is maintained under both random and shock road conditions. The inverse model attains a root mean square prediction error below 0.15 for control current, verifying its accuracy and adaptability. The proposed ICSA-ANFIS-ADRC control scheme thus provides a promising solution for intelligent seat suspension systems, effectively mitigating low-frequency resonance and enhancing ride comfort in agricultural vehicles.