Robust missile intercept control strategies can effectively be synthesized using convex programming. The major obstacle when implementing such strategies as receding horizon controllers is the online computational load, since the strategies have to be derived at each time instant. Hence, the application of these robust strategies for missile intercept control systems has been very limited. In this paper, a novel approach is suggested that transfers the computational effort from online to offline computations. As a result, an automatic scheduling of the robust strategies is provided that does not require any online optimization. The proposed model-based method results in a piecewise affine outputfeedback strategy.

Summary In this paper, robust disturbance-feedback strategies for finite time-horizon problems are studied. Linear discrete-time systems subject to linear control, state constraints, and quadratic objective functions are considered. In addition, persistent disturbances, which enter the system additively and are contained in a polytopic set, act on the system. The synthesis of robust strategies leads in the case of the traditional robust state-feedback and open-loop min-max strategies to, respectively, nonconvex problems or conservatism. However, robust disturbance-feedback problems can easily be reformulated as convex problems and solved by tractable linear matrix inequalities. Hence this approach bypasses the nonconvexity issue while maintaining the advantages of feedback strategies. As a key result, it is shown that both sources of conservatism attributed to this approach, namely, the relaxation method and the affine parametrization, can be removed at the expense of an increase in computational effort.