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Now showing items 1 - 11 of 11

  • Agility Analysis of the James Webb Space Telescope

    Karpenko, Mark   King, Jeffrey T.   Dennehy, Cornelius. J.   Michael Ross, I.  

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  • Off-Target Look Angle Control Guidance Law for Moving Targets

    Jeon, In-Soo   Karpenko, Mark  

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  • Flight Implementation of Shortest-Time Maneuvers for Imaging Satellites

    Karpenko, Mark   Bhatt, Sagar   Bedrossian, Nazareth   Ross, I. Michael  

    Shortest-time maneuvers are constrained time-optimal slews that enable spacecraft to be maneuvered more quickly than conventional rotations. Shortest-time maneuvers can increase a spacecraft's imaging capability without any changes to the hardware. Previous studies have shown that, depending upon the spacecraft design, the increased capability can be over 50%. Motivated by such high increases in efficiencies, the first flight demonstration of a shortest-time maneuver was performed on 10 August 2010 onboard NASA's TRACE Space Telescope. To transition this new technology from flight demonstration to standard operational procedures, several qualification thresholds need to be met. This paper demonstrates two of these qualification thresholds: 1) the ability to consistently and reliably generate flight-implementable shortest-time maneuvers on demand, and 2) flight demonstrations over multiple operational scenarios involving minimum-time slewing, attitude hold for point data collection, and transition maneuvers for scanning operations. The key technology for meeting both thresholds is flight-implementable pseudospectral controls. Flight and operational considerations quickly narrow down the plethora of pseudospectral options to the Legendre and Chebyshev techniques. All flight implementations were performed using the spectral algorithm of the Legendre pseudospectral method.
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  • Quantitative Fault Tolerant Control Design for a Leaking Hydraulic Actuator

    Karpenko, Mark   Sepehri, Nariman  

    This paper documents the design of a low-order, fixed-gain, controller that can maintain the positioning performance of an electrohydraulic actuator operating under variable load with a leaking piston seal. A set of linear time-invariant equivalent models of the faulty hydraulic actuator is first established, in the frequency domain, by Fourier transformation of acceptable actuator input-output responses. Then, a robust position control law is synthesized by quantitative feedback theory to meet the prescribed design tolerances on closed-loop stability and reference tracking. The designed fault tolerant controller uses only actuator position as feedback, yet it can accommodate nonlinearities in the hydraulic functions, maintain robustness against typical parametric uncertainties, and maintain the closed-loop performance despite a leakage fault that can bypass up to 40% of the rated servovalve flow across the actuator piston. To demonstrate the utility of the fault tolerant control approach in a realistic application, the experimental fault tolerant hydraulic system is operated as a flight surface actuator in the hardware-in-the-loop simulation of a high-performance jet aircraft. [DOI: 10.1115/1.4001707]
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  • Experimental Implementation of Riemann–Stieltjes Optimal Control for Agile Imaging Satellites

    Karpenko, Mark   Proulx, Ronald J.  

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  • Decentralized Coordinated Motion Control of Two Hydraulic Actuators Handling a Common Object

    Karpenko, Mark   Sepehri, Nariman   Anderson, John  

    In this paper reinforcement learning is applied to coordinate, in a decentralized fashion, the motions of a pair of hydraulic actuators whose task is to firmly hold and move an object along a specified trajectory tinder conventional position control. The learning goal is to reduce the interaction forces acting on the object that arise due to inevitable positioning errors resulting from the imperfect closed-loop actuator dynamics. Each actuator is therefore outfitted with a reinforcement learning neural network that modifies a centrally planned formation constrained position trajectory in response to the locally measured interaction force. It is shown that the actuators, which form a multiagent learning system, can learn decentralized control strategies that reduce the object interaction forces and thus greatly improve their coordination on the manipulation task. However, the problem of credit assignment, a common difficulty in multiagent learning systems, prevents the actuators from learning control strategies where each actuator contributes equally to reducing the interaction force. This problem is resolved in this paper via the periodic communication of limited local state information between the reinforcement learning actuators. Using both simulations and experiments, this paper examines some of the issues pertaining to learning in dynamic multiagent environments and establishes reinforcement learning as a potential technique for coordinating several nonlinear hydraulic manipulators performing a common task.
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  • Equivalent Time-Invariant Modelling of Electrohydraulic Actuators with Application to Robust Control Synthesis

    Karpenko, Mark   Sepehri, Nariman  

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    Sepehri, Nariman   Karpenko, Mark   An, Liang   Karam, Suha  

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  • [American Institute of Aeronautics and Astronautics AIAA Guidance, Navigation, and Control Conference - Portland, Oregon (08 August 2011 - 11 August 2011)] AIAA Guidance, Navigation, and Control Conference - Flight Implementation of Pseudospectral Optimal Control for the TRACE Space Telescope

    Karpenko, Mark   Bhatt, Sagar   Bedrossian, Nazareth   Fleming, Andy   Ross, Isaac  

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  • [American Institute of Aeronautics and Astronautics AIAA/AAS Astrodynamics Specialist Conference - Minneapolis, Minnesota ()] AIAA/AAS Astrodynamics Specialist Conference - Implementation of Shortest-Time Maneuvers for Generic CMG Steering Laws

    Karpenko, Mark   Ross, Michael  

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  • A Pseudospectral optimal motion planner for autonomous unmanned vehicles

    Ross, I. Michael   Karpenko, Mark   Sekhavat, Pooya   Hurni, Michael A.  

    This paper presents a pseudospectral (PS) optimal control algorithm for the autonomous motion planning of a fleet of unmanned ground vehicles (UGVs). The UGVs must traverse an obstacle-cluttered environment while maintaining robustness against possible collisions. The generality of the algorithm comes from a binary logic that modifies the cost function for various motion planning modes. Typical scenarios including path following and multi-vehicle pursuit are demonstrated. The proposed framework enables the availability of real-time information to be exploited by real-time reformulation of the optimal control problem combined with real-time computation. This allows the each vehicle to accommodate potential changes in the mission/environment and uncertain conditions. Experimental results are presented to substantiate the utility of the approach on a typical planning scenario.
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