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Michael J. Acheson

Publications and source records attributed to Michael J. Acheson.

Examination of Unified Control Approaches Incorporating Generalized Control Allocation

Transition vehicles which combine vertical take off and landing with cruise configurations pose a unique challenge for control design and implementation. For this class of vehicle, successful control designs have historically broken the flight envelope into phases and modified the control approach for each phase. This research approaches control in a unified way across the entire envelope using a robust optimal design which provides effector weighting then implemented in a generalized Affine Generalized Inverse control allocation algorithm. System performance for a Lift plus Cruise transition vehicle is presented.

unified control, robust control, optimal control,

Benchmark Problem Development for Testing Maturity of Intelligent Contingency Management Tools

Increasingly autonomous Advanced Air Mobility (AAM) vehicles will be required to handle diverse conditions with limited human intervention. Intelligent contingency management (iCM) approaches are under development to address how automated agents can handle unforeseen, unplanned, and unanticipated events. Benchmark scenario is needed to test the maturity of the developed iCM tools and techniques.

Jon Holbrook

Candidate Performance Metrics for Generalized Control for Autonomous Flight

Contingency management is the most challenging aspect of autonomous flight. In order to accommodate the most flexible response to unpredicted events and unexpected circumstances, i.e. contingencies, a new integrated path planning, trajectory following, flight control architecture is required that would maximize the safe operating envelope. For this highly integrated generalized control architecture, a new set of performance metrics that extends beyond traditional stability and performance is required. This paper proposes a candidate set of new performance metrics relevant to urban air mobility mission scenarios.

Control metrics

Candidate Performance Metrics for Generalized Control for Autonomous Flight

Contingency management is the most challenging aspect of autonomous flight. In order to accommodate the most flexible response to unpredicted events and unexpected circumstances, i.e. contingencies, a new integrated path planning, trajectory following, flight control architecture is required that would maximize the safe operating envelope. For this highly integrated generalized control architecture, a new set of performance metrics that extends beyond traditional stability and performance is required. This paper proposes a candidate set of new performance metrics relevant to urban air mobility mission scenarios.

Control metrics

Community Benchmark Problem for Intelligent Contingency Management

This paper introduces a Community Benchmark Problem (CBP) for Intelligent Contingency Management (ICM) for Urban Air Mobility (UAM) aircraft. The CBP aims to provide a common framework for measuring and comparing the progress of autonomy solutions for UAM aircraft in handling emergency situations. The paper proposes a methodology for defining and quantifying five measures of complexity that capture the challenges and requirements of ICM for UAM: Mission, Environmental, Autonomy, Decision-Making, and Mission Fault. In addition, it proposes a methodology for defining and quantifying mission risk acceptability with the same goals: Contingency Management, Mission Success, Operational, Mission Redefinition, and Environmental. We describe how to use these measures to track progress of the development of ICM capability, as well as to create scenarios and evaluate the performance of different autonomy solutions.

autonomy