Delay-Dependent Dynamics of Switched Cohen-Grossberg Neural Networks with Mixed Delays

This paper aims at studying the problem of the dynamics of switched Cohen-Grossberg neural networks with mixed delays by using Lyapunov functional method, average dwell time (ADT) method, and linear matrix inequalities (LMIs) technique. Some conditions on the uniformly ultimate boundedness, the existence of an attractors, the globally exponential stability of the switched Cohen-Grossberg neural networks are developed. Our results extend and complement some earlier publications

Delay-Dependent Dynamics of Switched Cohen-Grossberg Neural Networks with Mixed Delays

This paper aims at studying the problem of the dynamics of switched Cohen-Grossberg neural networks with mixed delays by using Lyapunov functional method, average dwell time (ADT) method, and linear matrix inequalities (LMIs) technique. Some conditions on the uniformly ultimate boundedness, the existence of an attractors, the globally exponential stability of the switched Cohen-Grossberg neural networks are developed. Our results extend and complement some earlier publications

Controller Evolution and Divergence: A Software Perspective

Successful controllers evolve as they are refined, extended, and adapted to new systems and contexts. This evolution occurs in the controller design and also in its software implementation. Model-based design and controller synthesis can help to synchronize this evolution of design and software, but such synchronization is rarely complete as software tends to also evolve in response to elements rarely present in a control model, leading to mismatches between the control design and the software. In this thesis, we perform a first-of-its-kind study on the evolution of two popular open-source safety-critical autopilot control software -- ArduPilot, and Paparazzi, to better understand how controllers evolve and the space of potential mismatches between control design and their software implementation. We then use that understanding to prototype a technique, called mutation tool, that can generate mutated versions of code to mimic evolution to assess its impact on a controller\u27s behavior. We report on three major findings. First, control software evolves quickly and controllers are rewritten in their entirety, many times over through the controller\u27s lifetime, which implies that the design, synthesis, and implementation of controllers must support not just the initial baseline system but also their incremental evolution. Second, many software changes stem from an inherent mismatch between the continuous time/space physical model and its corresponding discrete software implementation, but also from the mishandling of exceptional conditions, and limitations and distinct data representation of the underlying computing architecture. Third, using our mutation tool that we developed, we show that small code changes can have a dramatic effect in a controller\u27s behavior, which implies that further support is needed to bridge these mismatches as carefully verified model properties may not necessarily translate to its software implementation. Advisers: Justin Bradley and Sebastian Elbau