Towards Fewer Control Laws via Continuous-Time Multiparametric Programming

Abstract

Multiparametric programming offers a powerful solution to the computational burden of solving optimal control problems repeatedly online. By solving the problem once offline, it yields the optimal control laws as explicit closed-form functions of the initial system state, reducing online execution to a direct evaluation with no iterations required. Most existing works build this idea on a discrete-time foundation, slicing the time horizon into intervals and applying KKT conditions to the resulting algebraic system. This discretization forces a tradeoff: too few intervals and the model fails to capture the true system dynamics, while too many cause the problem size, the number of decision variables, and the number of critical regions to grow rapidly, making both offline preparation and online lookup increasingly expensive. This work develops a multiparametric framework that works directly with the continuous-time problem. Pontryagin's Maximum Principle (PMP) is applied without any model discretization, and the optimal control is recovered as an explicit function of the initial state. Compared to the discrete-time formulation, the continuous-time approach produces substantially fewer critical regions, and this number remains fixed regardless of accuracy requirements, since it reflects the structure of the problem itself rather than a discretization grid. The framework also yields the switching times as explicit functions of the initial state, directly exposing when and how the optimal control structure changes over the horizon. Knowing these switching times in advance allows the real-time controller to skip unnecessary computations between them, further reducing the online execution cost. Results from a PAROC framework case study demonstrate that the continuous-time multiparametric approach is a rigorous alternative to the conventional discrete-time formulations.