Optimal Signal Design in System Identification for Model Predictive Control (MPC)

In control engineering, system identification is frequently used to create models from input-output data. The MPC is a very well-known control strategy because of its capacity to deal with a system’s limitations. MPC determines the optimal input based on optimization. The optimization for each time step needs to be achieved in less time than the sampling time of the system. In this paper, the optimal signal design in system identification for MPC is presented. The reference signal has derived from the input perturbation signal in both time domain and frequency domain. An equation has derived to determine the relationship between the input signal and the reference signal. Three dissimilar amplitude spectra, namely flat spectrum signal is designed using the time-frequency swapping technique, MOOSE2 signal is designed from the MOOSE2 programme and optexcit signal is designed from the Frequency Domain System Identification Toolbox, has been examined under MPC. The time-frequency swapping procedure is employed to reduce the crest factor for input signal for the constraints on the rate of change of control signal. A new method has been proposed for mitigating the constraint on the rate of change of control signal. The crest factor is reduced by 13.42% for the input signal and 37.64% for the optimal parameter vector for the control sequence signal. This research contributes to advancing the state-of-the-art in MPC by offering a systematic approach for system identification that can lead to improved control performance and greater adaptability in real-world applications.