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This research aims to provide an automated framework for the structural analysis and instrumentation of hybrid system models developed in Simulink. The behavior of the components introducing discontinuities in the model are automatically extracted in the form of state transition graphs. The framework is integrated in the S-TaLiRo toolbox to demonstrate the improvement in mode coverage.
System and software verification is a vital component in the development and reliability of cyber-physical systems - especially in critical domains where the margin of error is minimal. In the case of autonomous driving systems (ADS), the vision perception subsystem is a necessity to ensure correct maneuvering of the environment and identification of objects. The challenge posed in perception systems involves verifying the accuracy and rigidity of detections. The use of Spatio-Temporal Perception Logic (STPL) enables the user to express requirements for the perception system to verify, validate, and ensure its behavior; however, a drawback to STPL involves its accessibility. It is limited to individuals with an expert or higher-level knowledge of temporal and spatial logics, and the formal-written requirements become quite verbose with more restrictions imposed. In this thesis, I propose a domain-specific language (DSL) catered to Spatio-Temporal Perception Logic to enable non-expert users the ability to capture requirements for perception subsystems while reducing the necessity to have an experienced background in said logic. The domain-specific language for the Spatio-Temporal Perception Logic is built upon the formal language with two abstractions. The main abstraction captures simple programming statements that are translated to a lower-level STPL expression accepted by the testing monitor. The STPL DSL provides a seamless interface to writing formal expressions while maintaining the power and expressiveness of STPL. These translated equivalent expressions are capable of directing a standard for perception systems to ensure the safety and reduce the risks involved in ill-formed detections.
This dissertation presents a control-theoretic analysis of mean-field models for which the agent dynamics are governed by either a continuous-time Markov chain on an arbitrary state space, or a discrete-time Markov chain on a continuous state space. Three main problems are investigated. First, the problem of stabilization is addressed, that is, the design of transition probabilities/rates of the Markov process (the agent control parameters) that make a target distribution, satisfying certain conditions, invariant. Such a control approach could be used to achieve desired multi-agent distributions for spatial coverage and task allocation. However, the convergence of the multi-agent distribution to the designed equilibrium does not imply the convergence of the individual agents to fixed states. To prevent the agents from continuing to transition between states once the target distribution is reached, and thus potentially waste energy, the second problem addressed within this dissertation is the construction of feedback control laws that prevent agents from transitioning once the equilibrium distribution is reached. The third problem addressed is the computation of optimized transition probabilities/rates that maximize the speed at which the system converges to the target distribution.