A synchronous rendering of hybrid systems for designing Plant-on-a-Chip (PoC)

TL;DR

This paper introduces a novel method for automatically generating semantic-preserving code from hybrid automata to emulate physical plants, enabling more efficient plant-on-a-chip systems for control validation.

Contribution

It presents a new approach using a synchronous abstraction and static well-formedness verification for hybrid automata, facilitating automatic code generation for plant emulation.

Findings

The Piha tool outperforms Simulink in execution time.

Generated code is smaller and more efficient.

Method enables emulation of physical plants across various domains.

Abstract

Hybrid systems are discrete controllers that are used for controlling a physical process (plant) exhibiting continuous dynamics. A hybrid automata (HA) is a well known and widely used formal model for the specification of such systems. While many methods exist for simulating hybrid automata, there are no known approaches for the automatic code generation from HA that are semantic preserving. If this were feasible, it would enable the design of a plant-on-a-chip (PoC) system that could be used for the emulation of the plant to validate discrete controllers. Such an approach would need to be mathematically sound and should not rely on numerical solvers. We propose a method of PoC design for plant emulation, not possible before. The approach restricts input/output (I/O) HA models using a set of criteria for well-formedness which are statically verified. Following verification, we use an abstraction based on a synchronous approach to facilitate code generation. This is feasible through a sound transformation to synchronous HA. We compare our method (the developed tool called Piha) to the widely used Simulink R simulation framework and show that our method is superior in both execution time and code size. Our approach to the PoC problem paves the way for the emulation of physical plants in diverse domains such as robotics, automation, medical devices, and intelligent transportation systems.