Hierarchical Control of Rotorcraft for Enhanced Performance and Structural Durability

Tolani, Devendra; Horn, Joseph F.; Yasar, Murat; Ray, Asok

doi:10.2514/6.2005-6349

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…This also involves enforcing the operational requirements in terms of altitude and speed. 20 The following propulsion, electric and thermal configurations were considered: Dynamic 74 0 6 American Institute of Aeronautics and Astronautics 1. Ground: One engine is online; electrical loads are supplied from ground.…”

Section: B Concept Of Operationsmentioning

confidence: 99%

Aircraft Energy Management: Finite-time Optimal Control with Dynamic Constraints

Yasar

Kwatny

Bajpai

2015

AIAA Guidance, Navigation, and Control Conference

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Traditional aircraft design has been based on peak power and peak thermal loads. With the introduction of more-electric aircraft, increasing requirements for on-demand highquality power for flight controls and electro-mechanical actuation devices will pose further challenges for energy efficiency and mission capability. This paper examines a new dynamic optimization strategy for energy management of more-electric aircraft based on hybrid systems theory. An expressive framework is developed to address the pertinent issues for energy management in aircraft systems, creation of optimization metrics, implementation of necessary computational tools and initial verification through simulation. Nomenclaturec = constraints f, g, h = generic functions i = discrete mode index j = electric network bus index k = discrete time index l, m, n = space dimensions p, r, s = optimization weights q = discrete states t = continuous time index u = control inputs x = continuous state vector y = measurements B = bound F = fuel rate I = current J = objective function M = Mach number V = voltage ℝ = real field  = discrete events  = discrete transition function  = cost function

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Section: B Concept Of Operationsmentioning

confidence: 99%

Aircraft Energy Management: Finite-time Optimal Control with Dynamic Constraints

Yasar

Kwatny

Bajpai

2015

AIAA Guidance, Navigation, and Control Conference

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“…In the formulation of intelligent decision-making policies, the supervision of the entire fleet is managed by a chain of multi-tier discrete-event supervisors that are optimized at each level of operation, 5 while the interactions between the supervisors in the hierarchy are obtained from the relationship between the discrete events and continuously-varying sensory information. 6,7 The C&C component of the test bed obtains the information from the local supervisors of the RUAV and UGVs through a wireless network. The C&C computer receives constant updates on the current location of each vehicle, selects a desired path for each vehicle based on the current mission status, known enemy positions and expected behavioral patterns of enemy and friendly units, and issues corresponding waypoint commands.…”

Section: Figure 1 Overall Architecture Of the C 4 Isr Test Bedmentioning

confidence: 99%

“…The C&C computer receives constant updates on the current location of each vehicle, selects a desired path for each vehicle based on the current mission status, known enemy positions and expected behavioral patterns of enemy and friendly units, and issues corresponding waypoint commands. 7 The C&C computer also determines what objects, such as enemy vehicles or buildings, each vehicle can see based on its position in the virtual (and/or actual) world via a line-of-sight algorithm.…”

Section: Figure 1 Overall Architecture Of the C 4 Isr Test Bedmentioning

confidence: 99%

“…3 The local discrete event supervisor for rotorcraft maneuvers is used to decide on the set points for the continuous controllers depending on the operation and mission objectives. 7 An FSA model of the rotorcraft is created considering the requirements to accomplish the mission and maneuvering capability of the rotorcraft. The local supervisor restricts the rotorcraft behavior while making the optimal trade-off such that the maneuver is completed with minimal effort and mission objectives are still satisfied.…”

Section: High-level -Local Discrete Event Supervisor For Rotorcraftmentioning

confidence: 99%

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A Simulation Test Bed for Coordination of Unmanned Rotorcraft and Ground Vehicles

Yasar

Bridges

Mallapragada

et al. 2006

AIAA Modeling and Simulation Technologies Conference and Exhibit

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This paper presents the development of a simulation test bed for a command, control, computer, communication, intelligence, surveillance and reconnaissance (C 4 ISR) system. The test bed features coordination of a rotorcraft unmanned aerial vehicle (RUAV) and multiple unmanned ground vehicles (UGVs) by integrating a high-fidelity rotorcraft model with actual ground-based robots that emulate UGVs. The RUAV component of the test bed is realized on a networked computer simulation, whereas networked robotics hardware is employed for the UGV representation. The RUAV dynamics run a high-fidelity nonlinear simulation model of a UH-60A Black Hawk helicopter. To emulate the dynamics of the UGVs, several networked Segway robots are employed. The proposed C 4 ISR system undertakes the task of mission management from the top level discrete-event supervision (DES) to the bottom level continuous-time regulation. Nomenclature h Rotorcraft altitude above ground, ft K D Derivative control factor K I Integral control factor K P Proportional control factor r b Rotorcraft yaw rate, rad/s V Rotorcraft total airspeed, kt V D Rotorcraft vertical speed, ft/s γ Simulated sensor visibility angle, rad θ Angle between center of sensor and vertical axis, rad φ Rotorcraft roll angle, rad θ Rotorcraft pitch angle, rad ψ Rotorcraft heading angle, rad v Robot linear velocity, m/s ω Robot angular velocity, rad/s Subscript cmd Commanded value err Error r Rotorcraft t Target

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“…For such control problems, modeling and simulation methods can be employed very effectively for optimization studies and control synthesis. [4][5][6] The drawback here has been traditionally associated with the fundamental trade-off between the computational speed and the accuracy of the computations. If a high-fidelity model is employed for Monte-Carlo type optimization, the computation time drastically increases for the control synthesis.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Modeling, Simulation and Analysis Environment for Coupled Aircraft Subsystems to Facilitate Control Synthesis and Validation

Yasar

Kwatny

Bajpai

2015

AIAA Modeling and Simulation Technologies Conference

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Hierarchical Control of Rotorcraft for Enhanced Performance and Structural Durability

Cited by 6 publications

References 4 publications

Aircraft Energy Management: Finite-time Optimal Control with Dynamic Constraints

Aircraft Energy Management: Finite-time Optimal Control with Dynamic Constraints

A Simulation Test Bed for Coordination of Unmanned Rotorcraft and Ground Vehicles

An Integrated Modeling, Simulation and Analysis Environment for Coupled Aircraft Subsystems to Facilitate Control Synthesis and Validation

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