Successive Convexification for Optimal Control with Signal Temporal Logic Specifications

As the scope and complexity of modern cyber-physical systems increase, newer and more challenging mission requirements will be imposed on the optimal control of the underlying unmanned systems. This paper proposes a solution to handle complex temporal requirements formalized in Signal Temporal Logic (STL) specifications within the Successive Convexification (SCvx) algorithmic framework. This SCvx-STL solution method consists of four steps: 1) Express the STL specifications using their robust semantics as state constraints. 2) Introduce new auxiliary state variables to transform these state constraints as system dynamics, by exploiting the recursively defined structure of robust STL semantics. 3) Smooth the resulting system dynamics with polynomial smooth min- and max-functions. 4) Convexify and solve the resulting optimal control problem with the SCvx algorithm, which enjoys guaranteed convergence and polynomial time subproblem solving capability. Our approach retains the expressiveness of encoding mission requirements with STL semantics, while avoiding the usage of combinatorial optimization techniques such as Mixed-integer programming. Numerical results are shown to demonstrate its effectiveness.