This talk discusses the problem of constructing control Lyapunov functions (CLFs) and feedback stabilization strategies for deterministic nonlinear control systems described by ordinary differential equations. Many numerical methods for solving the Hamilton–Jacobi–Bellman partial differential equations specifying CLFs typically require dense state space discretizations and consequently suffer from the curse of dimensionality. A relevant direction of attenuating the curse of dimensionality concerns reducing the computation of the values of CLFs and associated feedbacks at any selected states to finite-dimensional nonlinear programming problems. In this work, exit-time optimal control is used for that purpose. First, we state an exit-time optimal control problem with respect to a sublevel set of an appropriate local CLF and establish that, under a number of reasonable conditions, the concatenation of the corresponding value function and the local CLF is a global CLF in the whole domain of asymptotic null-controllability. Together with certain high-dimensional interpolation methods as well as some learning techniques, this leads to a curse-of-dimensionality-free approach to feedback stabilization that allows for the design of efficient model predictive control schemes. We also investigate the formulated optimal control problem. A modification of these constructions for the case when one does not find a suitable local CLF is provided as well. Supporting numerical simulation results that illustrate our development are subsequently presented and discussed. Furthermore, it is pointed out that the curse of complexity may cause significant issues in practical implementation even if the curse of dimensionality is mitigated.
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