This dissertation presents an MPC-based Reference Governor approach for control of constrained linear systems. A nominal closed-loop system is firstly designed to guarantee that, in the unconstrained case, asymptotic zero error regulation for (piecewise) constant reference signals is achieved. Then, a couple of exogenous signals is added to the reference signal and to the control variable and their value is determined by formulating a Model Predictive Control problem in order to guarantee that (i) when the state and control constraints are not active, the nominal closed-loop system is recovered, (ii) in transient conditions the constraints are always satisfied and the difference of the performances between the real and the nominal closed-loop systems are minimized, (iii) when the reference signal is infeasible, the output is brought to the nearest feasible value. Robust version of the proposed method is also developed for the constrained linear systems subject to the unknown but bounded disturbances. The proposed Reference Governor algorithm is then developed for large-scale systems made by the cascade interconnection of subsystems, a wide justify; LINE-HEIGHT: 150%; MARGIN: 0cm 0cm 0pt; tab-stops: 45.8pt 91.6pt 137.4pt 183.2pt 229.0pt 274.8pt 320.6pt 366.4pt 412.2pt 458.0pt 503.8pt 549.6pt 595.4pt 641.2pt 687.0pt 732.8pt" All the necessary requirements such as recursive feasibility, stability, convergence, offset-free tracking, robust constraint satisfaction are established. Some simulation examples are also reported to witness the potentialities of the approach. Keywords: Reference Governors; Model Predictive Control; Constrained linear systems; Reference tracking; On-line optimization; Distributed Control Systems.
