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Standard versions of the maximum principle for optimal control problems with pathwise state inequality constraints are satisfied by a trivial set of multipliers in the case when the left endpoint is fixed and lies in the boundary of the state constraint set, and so give no useful information about optimal controls. Recent papers have addressed the problem of overcoming this degenerate feature of the necessary conditions. In these papers it is typically shown that, if a constraint qualification is imposed, requiring existence of inward pointing velocities, then sets of multipliers exist in addition to the trivial ones. A simple, new approach for deriving nondegenerate necessary conditions is presented, which permits relaxation of hypotheses previously imposed, concerning data regularity and convexity of the velocity set.

International audienceIn this paper we investigate normal and nondegenerate forms of the maximum principle for optimal control problems with state con-straints. We propose new constraint qualifications guaranteeing non-degeneracy and normality that have to be checked on smaller sets of points of an optimal trajectory, than those in known sufficient con-ditions. In fact, the constraint qualifications proposed impose the existence of an inward pointing velocity just on the instants of time for which the optimal trajectory has an outward pointing velocity

This note proposes a model predictive control (MPC) algorithm for the solution of a robust control problem for continuous-time systems. Discontinuous feedback strategies are allowed in the solution of the min-max problems to be solved. The use of such strategies allows MPC to address a large class of nonlinear systems, including among others nonholonomic systems. Robust stability conditions to ensure steering to a certain set under bounded disturbances are established. The use of bang-bang feedbacks described by a small number of parameters is proposed, reducing considerably the computational burden associated with solving a differential game. The applicability of the proposed algorithm is tested to control a unicycle mobile robot.

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