Novel State-Space Self-Tuning Control of Two-Dimensional L

A two-dimensional (2-D) state-space self-tuning control (STC) scheme for multi-input multi-output (MIMO) linear discrete-time stochastic systems with actuator faults has been developed in this paper. To develop the 2-D state-space STC design, the 2-D linear discrete-time stochastic system is transformed to, and treated as, a two-dimensional Roesser’s (2D-RM) model. A 2-D state-space STC methodology for the 2-D linear discrete-time stochastic system constructs an adjustable auto-regressive moving average (ARMA)-based noise model with estimated state first. The relevant concepts of fault-tolerance along 2-D axes are introduced. For the detection of fault occurrence, a quantitative criterion is developed by comparing the innovation process errors occurring in the Kalman filter estimation algorithm. Also, for faulty system recovery, a 2-D weighting matrix resetting technique is newly developed by adjusting and resetting the covariance matrices of parameter estimate obtained in the Kalman filter estimation algorithm to improve the parameter estimation of the faulty systems. Then, the suboptimal tracker for the 2-D linear system in Roesser’s model (RM) has been proposed. The proposed control law can guarantee the closed-loop convergence along both directions to satisfy the tracking performance, even with actuator faults.