# Integrated Offline and Online Optimization-Based Control in a Base-Parallel Architecture

## TL;DR

This paper introduces an integrated control architecture combining offline-tuned base controllers with online optimization-based parallel controllers, enabling efficient real-time control of nonlinear systems with constraints, demonstrated on highway traffic management.

## Contribution

The paper presents a novel flexible control architecture that integrates offline and online controllers for real-time nonlinear system management, adaptable online for performance improvements.

## Key findings

- Achieves real-time control within strict time budgets.
- Outperforms traditional online MPC in computational efficiency.
- Reduces overall system cost in traffic management case study.

## Abstract

We propose an integrated control architecture to address the gap that currently exists for efficient real-time implementation of MPC-based control approaches for highly nonlinear systems with fast dynamics and a large number of control constraints. The proposed architecture contains two types of controllers: base controllers that are tuned or optimized offline, and parallel controllers that solve an optimization-based control problem online. The control inputs computed by the base controllers provide starting points for the optimization problem of the parallel controllers, which operate in parallel within a limited time budget that does not exceed the control sampling time. The resulting control system is very flexible and its architecture can easily be modified or changed online, e.g., by adding or eliminating controllers, for online improvement of the performance of the controlled system. In a case study, the proposed control architecture is implemented for highway traffic, which is characterized by nonlinear, fast dynamics with multiple control constraints, to minimize the overall travel time of the vehicles, while increasing their total traveled distance within the fixed simulation time window. The results of the simulation show the excellent real-time (i.e., within the given time budget) performance of the proposed control architecture, with the least realized value of the overall cost function. Moreover, among the online control approaches considered for the case study, the average cost per vehicle for the base-parallel control approach is the closest to the online MPC-based controllers, which have excellent performance but may involve computation times that exceed the given time budget.