The primary disadvantage of current design techriiques for xriodel predictive control (MPC) is their inability to deal explicitly with plant model uncertainty. In this paper, we present a new approacli for robust MPC synthesis which allows explicit irlcorporation of the description of pla,nt uricertainty in the problem formulation. The uncertainty is expressed both in the time domain and the frequency domain. The goal is to design, at each time step, a statefeedbaclr coritrol law which minimizes a "worst-case" infinite horizon objective function, subjec-t to constraints on thc control input arid plant output. Using standard techniques, the problem of minimizing an upper bound on the "worst-case" objective fu~iction, subject to input and output constraints, is reduced to a convex optimizatiori involving linear matrix inequalities (LMIs). It is shown that the feasible receding horizon state-feedback co~ltrol design robustly stabilizes the set of uncertairi plants under consideration. Several extensions, such as applicatiori to systems with time-delays and problems involving constant set-point tracking, trajectory tracking and disturbance rejection, which follow naturally from our formulation, are discussed. The co~~troller design procedure is illustrated with two examples. Finally, conclusions are presented.

The primary disadvantage of current design techriiques for xriodel predictive control (MPC) is their inability to deal explicitly with plant model uncertainty. In this paper, we present a new approacli for robust MPC synthesis which allows explicit irlcorporation of the description of pla,nt uricertainty in the problem formulation. The uncertainty is expressed both in the time domain and the frequency domain. The goal is to design, at each time step, a statefeedbaclr coritrol law which minimizes a "worst-case" infinite horizon objective function, subjec-t to constraints on thc control input arid plant output. Using standard techniques, the problem of minimizing an upper bound on the "worst-case" objective fu~iction, subject to input and output constraints, is reduced to a convex optimizatiori involving linear matrix inequalities (LMIs). It is shown that the feasible receding horizon state-feedback co~ltrol design robustly stabilizes the set of uncertairi plants under consideration. Several extensions, such as applicatiori to systems with time-delays and problems involving constant set-point tracking, trajectory tracking and disturbance rejection, which follow naturally from our formulation, are discussed. The co~~troller design procedure is illustrated with two examples. Finally, conclusions are presented.

We present a unified framework for the study of linear time-invariant (LTI) systems subject to control input nonlinearities. The framework is based on the following two-step design paradigm: UDesign the linear controller ignoring control input nonlinearities and then add anti-windup bumpless transfer (AWBT) compensation to minimize the adverse eflects of any control input nonlinearities on closed loop performance". The resulting AWBT compensation is applicable to multivariable controllers of arbitrary structure and order. All known LTI anti-windup and/or bumpless transfer compensation schemes are shown to be special cases of this framework. It is shown how this framework can handle standard issues such as the analysis of stability and performance with or without uncertainties in the plant model. The actual analysis of stability and performance, and robustness issues are problems in their own right and hence not detailed here. The main result is the unification of existing schemes for AWBT compensation under a general framework.

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