Distributed mixed continuous-discrete receding horizon filter for multisensory uncertain active suspension systems with measurement delays

This study presents a new robust filtering method in modelling an active multisensory suspension system with measurement delays and parameteric uncertainties in a state-space dynamical model. To achieve good performance of the system, a new distributed fusion receding horizon filtering frameworks are constructed to couple the continuous dynamics with the multisensory discrete measurements, and to coordinately deal with the parametric uncertainty and time-delays. The novel filtering algorithm is proposed based on the receding horizon strategy, standard mixed continuous-discrete Kalman filtering and discrete Kalman filtering for systems with time-delays in order to achieve high estimation accuracy and stability under parametric uncertainties. The key theoretical contributions of this study are the derivation of the error cross-covariance equations between the local receding horizon filters in order to compute the optimal matrix fusion weights. The high accuracy and efficiency of the new filter are demonstrated through its implementation and performance and then compared to the existing vehicle active suspension system.