Robust Force Estimation for Magnetorheological Damper Based on Complex Value Convolutional Neural Network

Abstract

Abstract Recent developments in semi-active control technologies enhance the possibility of an effective response reduction during a wide range of dynamic loading conditions. Most semi-active control schemes employ magnetorheological dampers (MRDs) as actuators. These devices exhibit nonlinear and hysterical behavior that complicates reactive force estimation to compensate for disturbances. In this paper, we present a novel robust schema to estimate MRD forces using a complex value convolutional neural network (CV-CNN) to overcome these problems. CV-CNN utilizes random complex value convolutional filters as parameters to reduce the measured noise by combining the training stage and the max-by-magnitude operation. Furthermore, CV-CNN is a hysteresis-model-free strategy that overcomes the parameterization in nonlinear systems. The proposed CV-CNN only requires displacement and voltage measurements for force estimation. Different metrics are used to compare results between the CV-CNN, genetic algorithm (GA), particle swarm optimization (PSO), and shallow neural network (SNN). Experimental results show the potential of the proposed CV- CNN for practical applications due to its simplicity and robustness. The CV-CNN computational time is less than that of GA and PSO. In the training stage, the CV-CNN uses 0.7% of GA's time and 1.4% of PSO. Although SNN uses 5.5% of the time consumed by the CV-CNN, the latter performs the force estimation for MRD better; its mean square error is 78.3% lower than the GA's and PSO's, and 71.4% lower than SNN's.