Adaptive recursive system identification using optimally tuned Kalman filter by the metaheuristic algorithm

This paper developed a new and efficient approach, the metaheuristic optimisation approach based on the Kalman filter (KF), to accurately identify the infinite impulse response (IIR) system coefficients. A recently developed metaheuristic optimisation technique called the manta-ray foraging optimisation (MRFO) algorithm has been employed to solve the proper parameters tuning exertion of KF. The problem of achieving better unknown state estimations by the conventional KF is closely connected with decent prior information about the noise statistics (measurement and process noise). However, the statistics mentioned above depend on the types of applications and process dynamics. The use of insufficiently known priori filter statistics would reduce the precision of the estimated filter or sometimes lead to a practical divergence of the filter. To alleviate this problem, the proposed technique initially produces optimally tuned KF parameters using the MRFO algorithm, and then the conventional KF method estimates the coefficients of the IIR model using the optimised KF parameters obtained at the beginning. The simulation studies have been carried out on three distinct order benchmark IIR systems (both same and reduced-order), and two prominent IIR applications called direct current (DC) motor and fractional order digital differentiator (FODD) using the proposed method. The overall identification results demonstrate that the proposed MRFO-KF technique significantly improves performance metrics over competing techniques, such as basic KF, other employed metaheuristic-based KF schemes and recently reported state-of-the-art algorithms.