Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical Systems

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) \textit{dynamic-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) \textit{middleware 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) \textit{hardware 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

Modeling and Verifying Cyber-Physical Systems with Hybrid Active Objects

Formal modeling of cyber-physical systems (CPS) is hard, because they pose the double challenge of combined discrete-continuous dynamics and concurrent behavior. Existing formal specification and verification languages for CPS are designed on top of their underlying proof search technology. They lack high-level structuring elements. In addition, they are not efficiently executable. This makes formal CPS models hard to understand and to validate, hence impairs their usability. Instead, we suggest to model CPS in an Active Objects (AO) language designed for concise, intuitive modeling of concurrent systems. To this end, we extend the AO language ABS and its runtime environment with Hybrid Active Objects (HAO). CPS models and requirements formalized in HAO must follow certain communication patterns that permit automatic translation into differential dynamic logic, a sequential hybrid program logic. Verification is achieved by discharging the resulting formulas with the theorem prover KeYmaera X. We demonstrate the practicality of our approach with case studies