Dynamic System Identification for a Nonlinear Vehicle Model Using $q$ -Markov Cover under Different Operational Conditions

System identification plays a vital role in the control strategy development of dynamical systems. As system dynamics get complex and nonlinear, it is becoming even harder to perform conventional system identification using a physics-based approach, especially when the system dynamics change with time. Several data-driven strategies came to the rescue in such scenarios. Various methodologies are available to efficiently identify the system model and perform the control design on it. This work tries to address the scenario when the dynamics of the system modify with time, where a modified $q$ -Markov COVariance Equivalence Realization ( $q$ -Markov COVER) algorithm is used to perform the data-driven dynamic system identification. Various factors can cause the system dynamics to modify, such as system wear and aging, change in operational conditions, etc. For this work, a nonlinear vehicle model, operating under different road conditions: dry and wet pavement, is used to demonstrate the proposed approach. The proposed iterative $q$ -Markov COVER algorithm is able to quickly adapt to the identified linear model when the operational condition of the nonlinear vehicle model changes. Two different scenarios are created and studied: road condition changing from dry to wet to check the algorithm's efficacy and road condition from dry to wet to dry to validate if the algorithm could recover to the original identified system. From the simulation results, it can be observed that the algorithm efficiently identifies the associated linear dynamic model based on the data set from both scenarios. The proposed algorithm is generic, computationally inexpensive, and could be easily implemented for any general system.