Closed-loop predictions in model based predictive control linear and nonlinear systems

Conventional model based predictive control (MPC) algorithms consider predicted control moves to be the degrees of freedom over which the predicted performance cost is to be optimised. Recent work introduced a closed-loop prediction paradigm which enables the overall MPC strategy to be decomposed into the design of a fixed-term linear inner loop with a variable nonlinear loop wrapped around it. By effecting the design of the inner loop off-line, this paradigm affords significant advantages for both the control of uncertain linear and nonlinear systems. This chapter exploits these advantages to develop effective predictive control algorithms which have guaranteed closed-loop stability and asymptotically can yield an optimal dynamic behaviour.