A generalized Bellman-Ford Algorithm for Application in Symbolic Optimal Control

Symbolic controller synthesis is a fully-automated and correct-by-design synthesis scheme whose limitations are its immense memory and runtime requirements. A current trend to compensate for this downside is to develop techniques for parallel execution of the scheme both in mathematical foundation and in software implementation. In this paper we present a generalized Bellman-Ford algorithm to be used in the so-called symbolic optimal control, which is an extension of the aforementioned synthesis scheme. Compared to the widely used Dijkstra algorithm our algorithm has two advantages. It allows for cost functions taking arbitrary (e.g. negative) values and for parallel execution with the ability for trading processing speed for memory consumption. We motivate the usefulness of negative cost values on a scenario of aerial firefighting with unmanned aerial vehicles. In addition, this four-dimensional numerical example, which is rich in detail, demonstrates the great performance of our algorithm.Comment: This version has been accepted for publication in Proc. European Control Conference (ECC), 202

Symbolic Optimal Control

We present novel results on the solution of a class of leavable, undiscounted optimal control problems in the minimax sense for nonlinear, continuous-state, discrete-time plants. The problem class includes entry-(exit-)time problems as well as minimum time, pursuit-evasion and reach-avoid games as special cases. We utilize auxiliary optimal control problems (`abstractions') to compute both upper bounds of the value function, i.e., of the achievable closed-loop performance, and symbolic feedback controllers realizing those bounds. The abstractions are obtained from discretizing the problem data, and we prove that the computed bounds and the performance of the symbolic controllers converge to the value function as the discretization parameters approach zero. In particular, if the optimal control problem is solvable on some compact subset of the state space, and if the discretization parameters are sufficiently small, then we obtain a symbolic feedback controller solving the problem on that subset. These results do not assume the continuity of the value function or any problem data, and they fully apply in the presence of hard state and control constraints.Comment: corrected Theorem V.6; simplified definitions of plants, controllers and closed-loops; references added; full proof of Th VII.3 rather than just a sketc