# Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical Systems

arXiv: https://arxiv.org/abs/1902.02432 · HTML: https://mlsanity.com/paper/1902.02432/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems](https://mlsanity.com/paper/2108.11800/) (2021)
- [Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems](https://mlsanity.com/paper/2302.09750/) (2023)
- [Learning Contextual Runtime Monitors for Safe AI-Based Autonomy](https://mlsanity.com/paper/2601.20666/) (2026)
- [A Barrier Certificate-based Simplex Architecture for Systems with Approximate and Hybrid Dynamics](https://mlsanity.com/paper/2202.09710/) (2022)

## Abstract (arXiv)

Cyber Physical Systems (CPS) have increasingly started using Learning Enabled Components (LECs) for performing perception-based control tasks. The simple design approach, and their capability to continuously learn has led to their widespread use in different autonomous applications. Despite their simplicity and impressive capabilities, these models are difficult to assure, which makes their use challenging. The problem of assuring CPS with untrusted controllers has been achieved using the Simplex Architecture. This architecture integrates the system to be assured with a safe controller and provides a decision logic to switch between the decisions of these controllers. However, the key challenges in using the Simplex Architecture are: (1) designing an effective decision logic, and (2) sudden transitions between controller decisions lead to inconsistent system performance. To address these research challenges, we make three key contributions: (1) textitdynamic-weighted simplex strategy -- we introduce ``weighted simplex strategy" as the weighted ensemble extension of the classical Simplex Architecture. We then provide a reinforcement learning based mechanism to find dynamic ensemble weights, (2) textitmiddleware framework -- we design a framework that allows the use of the dynamic-weighted simplex strategy, and provides a resource manager to monitor the computational resources, and (3) textithardware testbed -- we design a remote-controlled car testbed called DeepNNCar to test and demonstrate the aforementioned key concepts. Using the hardware, we show that the dynamic-weighted simplex strategy has 60% fewer out-of-track occurrences (soft constraint violations), while demonstrating higher optimized speed (performance) of 0.4 m/s during indoor driving than the original LEC driven system.