Particle swarm optimization based fuzzy gain scheduled subspace predictive control

The key feature of data-driven Subspace Predictive Control (SPC) is its capability in on-line and automatically adaptation of SPC gains with no need to obtain the explicit model of the system. This feature makes SPC suitable to control nonlinear and time-varying systems. However, in conventional SPC persistently excitation (PE) signals are required to update the SPC gains in the presence of system variations. This procedure demands high computational load and has convergency issues. In this paper we propose a new approach to eliminate the requirement of applying PE signals without degrading the SPC performance. This can be done by using Particle Swarm Optimization (PSO) based Fuzzy Gain Scheduling (FGS) method to optimally update the SPC gains directly with no need to apply PE signals. The method is denoted by PSO-based FGS-SPC. In PSO-based FGS-SPC the SPC gains are optimally adapted by utilizing and evaluating auxiliary scheduling variables, which are correlated with the changes in system dynamics, as soon as a changes are observed in system dynamics without applying PE signals. Eliminating the PE in our proposed method reduces the computational load drastically. Moreover, in PSO-based FGS-SPC, the controller gain ranges (CGRs) of FGS technique are optimally auto-tuned by minimizing the SPC cost function via the PSO algorithm. As a result, the difficulty in finding the CGRs in FGS procedure for inverting the normalized gains is overcome by applying PSO technique on FGS. Consequently, the PSO-based FGS-SPC shows more efficient controlling performance than the SPC by optimally adapting the SPC gains. In addition, PSO-based FGS-SPC shows fast convergence capability and time efficiency over the SPC. Simulation results confirm efficiency and robustness of the method in the presence of constraints and noisy data.