Validation of Convex Optimization Algorithms and Credible Implementation for Model Predictive Control

Abstract

Advanced real-time embedded algorithms are growing in complexity and length, related to the growth in autonomy, which allows vehicles to plan paths of their own. However, this promise cannot happen without proper attention to the considerably stronger operational constraints that real time, safety-critical applications must meet. This paper discusses the formal verification for optimization algorithms with a particular emphasis on receding-horizon controllers. Following a brief historical overview, a prototype autocoder for embedded convex optimization algorithms is discussed. Options for encoding code properties and proofs, and their applicability and limitations is detailed as well.