Investigation of Optimal Perturbation Signals for Multivariable System Under Model Predictive Control

Model Predictive Control (MPC) is a widely used control strategy for multivariable systems due to its ability to handle complex dynamics and constraints. The design of perturbation signals that can improve MPC's performance. This research investigates the selection of optimal perturbation signals for multivariable systems under MPC to enhance control effectiveness. Three dissimilar methods to shape of the amplitude spectra has been tasted with three different signal-to-noise ratios (SNRs). The scenarios with and without constraints in the closed loop setting are also studied for multivariable system. The findings offer valuable guidance for practitioners seeking to enhance the performance of MPC-based control systems in various applications, including industrial processes and advanced robotics.