Power Distribution Systems (PDSes) protect Cyber-Physical Systems from power faults. This work proposes an intelligent PDS with real-time current monitoring to improve fault tolerance. To bound the real-time task utilization, we define linear and nonlinear utilization bounding techniques unique to PDSes. We exploit sub-maximal loading inherent in all practical PDSes, providing a total task set utilization bound less than the sum of individual task bounds. We show these techniques are safe and applicable to real PDSs. Simulation of existing and randomly generated PDSes show up to 90 % utilization reduction versus a naive approach. An experimental, open-source PDS implementation is also provided to validate feasibility.

Autonomous mobile robots (AMRs) have the capability to execute a wide range of tasks with minimal human intervention. However, one of the major limitations of AMRs is their limited battery life, which often results in interruptions to their task execution and the need to reach the nearest charging station. Optimizing energy consumption in AMRs has become a critical challenge in their deployment. Through empirical studies on real AMRs, we have identified a lack of coordination between computation and control as a major source of energy inefficiency. In this paper, we propose a comprehensive energy prediction model that provides real-time energy consumption for each component of the AMR. Additionally, we propose three path models to address the obstacle avoidance problem for AMRs. To evaluate the performance of our energy prediction and path models, we have developed a customized AMR called Donkey, which has the capability for fine-grained (millisecond-level) end-to-end power profiling. Our energy prediction model demonstrated an accuracy of over 90% in our evaluations. Finally, we applied our energy prediction model to obstacle avoidance and guided energy-efficient path selection, resulting in up to a 44.8% reduction in energy consumption compared to the baseline.

Characterizing computational demand of Cyber-Physical Systems (CPS) is critical for guaranteeing that multiple hard real-time tasks may be scheduled on shared resources without missing deadlines. In a CPS involving repetition such as industrial automation systems found in chemical process control or robotic manufacturing, sensors and actuators used as part of the industrial process may be conditionally enabled (and disabled) as a sequence of repeated steps is executed. In robotic manufacturing, for example, these steps may be the movement of a robotic arm through some trajectories followed by activation of end-effector sensors and actuators at the end of each completed motion. The conditional enabling of sensors and actuators produces a sequence of Monotonically Ascending Execution times (MAE) with lower WCET when the sensors are disabled and higher WCET when enabled. Since these systems may have several predefined steps to follow before repeating the entire sequence each unique step may result in several consecutive sequences of MAE. The repetition of these unique sequences of MAE result in a repeating WCET sequence. In the absence of an efficient demand characterization technique for repeating WCET sequences composed of subsequences with monotonically increasing execution time, this work proposes a new task model to describe the behavior of real-world systems which generate large repeating WCET sequences with subsequences of monotonically increasing execution times. In comparison to the most applicable current model, the Generalized Multiframe model (GMF), an empirically and theoretically faster method for characterizing the demand is provided. The demand characterization algorithm is evaluated through a case study of a robotic arm and simulation of 10,000 randomly generated tasks where, on average, the proposed approach is 231 and 179 times faster than the state-of-the-art in the case study and simulation respectively.

Engine-triggered tasks are real-time tasks that are released when the crankshaft in an engine completes a rotation, which depends on the angular speed and acceleration of the crankshaft itself. In addition, the execution time of an engine-triggered task depends on the speed of the crankshaft. Tasks whose execution times depend on a variable period are referred to as adaptive-variable rate (AVR) tasks. Existing techniques to calculate the worst-case demand of AVR tasks are either inexact or computationally intractable. In this paper, we transform the problem of finding the worst-case demand of AVR tasks over a given time interval into a variant of the knapsack problem to efficiently find the exact solution. We then propose a framework to systematically reduce the search space associated with finding the worst-case demand of AVR tasks. Experimental results reveal that our approach is at least 10 times faster, with an average runtime improvement of 146 times, for randomly generated tasksets when compared to the state-of-the-art technique.

Short-circuit faults are a potential source of damage to circuitry in DC-powered systems. Industrial applications including power converters, inverters, and insulated-gate bipolar transistors (IGBTs) often rely on fault detection systems in the form of dedicated circuitry to prevent damage. To increase flexibility in short-circuit detection and decrease dedicated circuitry, a software-based approach is presented. This implementation requires minimal circuitry and allows for tradeoff between board space and processor utilization. The design relies on a single inductor and microprocessor running a real-time task for identifying current and monitoring circuitry for faults. Experiments demonstrate detection of both hard-switching faults (HSF) and fault under load (FUL) shorts. The depicted relationship between processor utilization and board space consumed by the circuitry is confirmed through experimentation and allows optimization of board space with respect to utilization and vice versa. As a result, the proposed software-based detection is implementable with the addition of a single component and protects against damage from both HSF and FUL shorts.