Stability and robustness of model based predictive controller design - Koh, Alvin Chin Thye. (2010)
Model-based predictive control (MBPC) is a popular method currently employed in the process industries. This is probably due to its versatility in controlling various difficult plants. Besides that, it is also conceptually simple to consider variables constrained control problems that are commonly found in physical systems. Though many stability results have been developed for the MBPC system, however, few have considered physical saturation constraints. Those results which do consider such constraints are either too conservative or apply only to some limiting cases such as the use of theoretically infinite horizon. One of the objective of this thesis is to conduct an in-depth investigation on the stability performance of MBPC in the presence of physical saturation constraints. As such, two stability-guaranteed conditions are developed for the purpose. Though these conditions are derived without the consideration of any errors, it is easily shown that some forms of modelling errors are tolerable.