Optimization and Control of Cyber-Physical Vehicle Systems

A cyber-physical system (CPS) is composed of tightly-integrated computation, communication and physical elements. Medical devices, buildings, mobile devices, robots, transportation and energy systems can benefit from CPS co-design and optimization techniques. Cyber-physical vehicle systems (CPVSs) are rapidly advancing due to progress in real-time computing, control and artificial intelligence. Multidisciplinary or multi-objective design optimization maximizes CPS efficiency, capability and safety, while online regulation enables the vehicle to be responsive to disturbances, modeling errors and uncertainties. CPVS optimization occurs at design-time and at run-time. This paper surveys the run-time cooperative optimization or co-optimization of cyber and physical systems, which have historically been considered separately. A run-time CPVS is also cooperatively regulated or co-regulated when cyber and physical resources are utilized in a manner that is responsive to both cyber and physical system requirements. This paper surveys research that considers both cyber and physical resources in co-optimization and co-regulation schemes with applications to mobile robotic and vehicle systems. Time-varying sampling patterns, sensor scheduling, anytime control, feedback scheduling, task and motion planning and resource sharing are examined

Rethinking Sampled-Data Control for Unmanned Aircraft Systems

Unmanned aircraft systems are expected to provide both increasingly varied functionalities and outstanding application performances, utilizing the available resources. In this paper, we explore the recent advances and challenges at the intersection of real-time computing and control and show how rethinking sampling strategies can improve performance and resource utilization. We showcase a novel design framework, cyber-physical co-regulation, which can efficiently link together computational and physical characteristics of the system, increasing robust performance and avoiding pitfalls of event-triggered sampling strategies. A comparison experiment of different sampling and control strategies was conducted and analyzed. We demonstrate that co-regulation has resource savings similar to event-triggered sampling, but maintains the robustness of traditional fixed-periodic sampling forming a compelling alternative to traditional vehicle control design

Design-time detection of physical-unit changes in product lines

Software product lines evolve over time, both as new products are added to the product line and as existing products are updated. This evolution creates unintended as well as planned changes to Systems. A persistent problem is that unintended changes are hard to detect. Often they are not discovered until testing or operations. Late discovery is a problem especially in safety-critical, cyberphysical product lines such as avionics, pacemakers, and smart-braking systems, where unintended changes may lead to accidents. This thesis proposes an approach and a prototype tool to detect unintended changes earlier in development of a new product in the product line. The capability to detect potentially risky, unintended changes at the design stage is beneficial because repair is easier, less costly, and safer in design than when detection is delayed to testing or operations. The Product Line Change Detector (PLCD) introduced here analyzes products’ SysML block and parametric diagrams, which are typical project artifacts for cyber-physical systems, in order to detect problematic, unintended changes. The PLCD software automatically detects potential change-related issues, ranks them in terms of severity using the products’ safety-analysis artifacts, and reports them to developers in a graphical format. Developers select and fix the reported issues with the assistance of the tool’s displays, with the tool recording the fixes and updating the SysML diagrams accordingly. The evaluation of PLCD’s performance and capabilities uses three product lines, extended from cyber-physical systems in the literature: NASA astronaut jetpack, vehicle dynamics, and low-earth satellite. The evaluation focuses on unintended changes that cause physical unit inconsistencies, such as between meters and feet, since those may lead to accidents in cyber-physical product lines. The evaluation results show that PLCD successfully detects such unintended changes both in a single product and between products in a software product line

Co-Regulated Consensus of Cyber-Physical Resources in Multi-Agent Unmanned Aircraft Systems

Intelligent utilization of resources and improved mission performance in an autonomous agent require consideration of cyber and physical resources. The allocation of these resources becomes more complex when the system expands from one agent to multiple agents, and the control shifts from centralized to decentralized. Consensus is a distributed algorithm that lets multiple agents agree on a shared value, but typically does not leverage mobility. We propose a coupled consensus control strategy that co-regulates computation, communication frequency, and connectivity of the agents to achieve faster convergence times at lower communication rates and computational costs. In this strategy, agents move towards a common location to increase connectivity. Simultaneously, the communication frequency is increased when the shared state error between an agent and its connected neighbors is high. When the shared state converges (i.e., consensus is reached), the agents withdraw to the initial positions and the communication frequency is decreased. Convergence properties of our algorithm are demonstrated under the proposed co-regulated control algorithm. We evaluated the proposed approach through a new set of cyber-physical, multi-agent metrics and demonstrated our approach in a simulation of unmanned aircraft systems measuring temperatures at multiple sites. The results demonstrate that, compared with fixed-rate and event-triggered consensus algorithms, our co-regulation scheme can achieve improved performance with fewer resources, while maintaining high reactivity to changes in the environment and system

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