Task 5 2 2005 Review

44 %
56 %
Information about Task 5 2 2005 Review
Entertainment

Published on November 7, 2007

Author: Fenwick

Source: authorstream.com

Slide1:  PI: Asst. Prof. Joseph F. Horn Tel: (814) 865 6434 Email: joehorn@psu.edu Graduate Students: Dooyong Lee, PhD Candidate Derek Bridges, PhD Candidate Project PS 5.2 Simulation and Control of Shipboard Launch and Recovery Operations 2005 RCOE Program Review May 3, 2005 Slide2:  The shipboard launch and recovery task is one of the most challenging, training intensive, and dangerous of all rotorcraft operations The helicopter / ship dynamic interface (DI) is difficult to accurately model Industry and government could use better tools for analyzing shipboard operations to reduce the flight test time and cost to establish safe operating envelopes Workload requirements could be reduced using task-tailored control systems for shipboard operations Background / Problem Statement Technical Barriers Accurate models require the integration of the time-varying ship airwake and the flight dynamics of the helicopter Currently pilot workload requirements and HQ analysis must be assessed using expensive flight tests and piloted simulation. Better engineering tools needed to reduce costs for analyzing current and future ships / aircraft. A practical, fully autonomous or piloted assisted landing AFCS has not yet been developed, need to assess requirements and potential benefits Slide3:  Develop advanced simulation model of the shipboard dynamic interface Validate the model using available test data Use the model to develop advanced flight control systems to address workload issues in the DI Task Objectives: Approaches: Expected Results: A simulation tool for analyzing handling qualities in the DI and predicting safe landing envelopes A methodology for designing a task-tailored AFCS for shipboard operations A conceptual design of an autonomous landing systems and assessment of the system requirements for such a system (possible UAV applications) Develop a MATLAB/SIMULINK based simulation of the H-60 based on GenHel (will facilitate model improvements and control law development) Develop a maneuver controller to simulate pilot control during launch and recovery operations Integrate simulation with ship airwake models, investigate relative effects of steady and time-accurate CFD wakes, and stochastic wake models based on CFD and flight test data Simulate UH-60 operating off LHA and validate model with JSHIP flight test data Develop new concepts in AFCS design for shipboard operations Develop a real-time simulation facility of shipboard operations (using DURIP funds) PSU DI Simulation Program:  PSU DI Simulation Program Developed “tunable” pilot model for different levels of tracking tolerance Integrated CFD solutions of ship airwake with non-linear flight dynamics model Demonstrated using UH-60A / LHA combination, same as JSHIP flight test program Validated model with flight test data from JSHIP program Evaluate task tailored control laws Matlab based DI simulation program (based on GENHEL) Human pilot model (Optimal control model) Time-accurate ship airwake from CFD Stochastic airwake model Real-time simulation Validation with flight test data (from JSHIP program) Task-tailored control law design (using CONDUIT) Stochastic Ship Airwake Modeling:  Stochastic Ship Airwake Modeling A method for extracting equivalent airwake disturbances from flight test data (or high order simulation model) has been developed Method is similar to that used for turbulence models developed at NASA Ames (Ref. Labows and Tischler et al, MacFarland - SORBET Model) Filters are derived to simulate the spectral properties of the airwake, can compare to traditional turbulence models (e.g. von Karman, Dryden) Spectral filters are based on von Karman model, and modified to fit the desired forms of spectral characteristics Stochastic airwake model can be readily used for flight control optimization Helicopter Dynamics Pilot stick inputs SAS + + + White noise Linear filter Stochastic airwake model Optimized to reject disturbances Designed to fit the spectral properties of the airwake Stochastic Ship Airwake Modeling Sample Results for Vertical Component, 0° WOD:  Stochastic Ship Airwake Modeling Sample Results for Vertical Component, 0° WOD Extracted from simulation with full time-varying airwake von Karman Turbulence Model Lw = 37.8667 ft, sw = 2.8067 ft/sec “Best Fit” Spectral Filter Lw =10.7156 ft, sw = 4.81 ft/sec Frequency (rad/sec) PSD of vertical gust component, (ft/sec)2/(rad/sec) Stochastic Ship Airwake Modeling:  Stochastic Ship Airwake Modeling Equivalent Airwake Disturbances Full Time-Varying Airwake Comparison of response with stochastic airwake model, equivalent disturbances and full time-varying airwake (spectral data averaged over five runs) Frequency (rad/sec) LAT LON PED COL - Input autospectrum(0 deg WOD), dB Autospectra identified by CIFER Stochastic Airwake LAT LON PED COL Frequency (rad/sec) - Input autospectrum(30 deg WOD), dB Slide8:  Using CONDUIT to optimize SAS gains Include ADS-33 HQ specs as constraints in optimization Include longitudinal acceleration feedback and pitch attitude feedback Airwake Spectral Filters Task-Tailored Control Design Longitudinal acceleration feedback to improve gust response Pitch attitude feedback to provide closed-loop stability Stability Augmentation System:  Stability Augmentation System Optimize gains using CONDUIT Based on phase-lag compensator Design parameters include the prefix “dpp_” Roll SAS Pitch SAS Yaw SAS HQ specs :  HQ specs Selected design specs from CONDUIT as constraints Closed-loop eigenvalues(EigLcG1), Gain/Phase margin(StbMgG1), Crossover frequency(CrsLnG1), Bandwidth for roll/pitch(BnwAtH1) New spec for disturbance rejection(DisRnL1) Based on psd of angular rate response to corresponding gust input White noise PSD Transfer function (Example) – Pitch rate Frequency [rad/sec] Magnitude [dB] Level I Level II Level III HQ Specification Window:  HQ Specification Window Original SAS configurations - 30 degree WOD condition CrsLnG1 (1) CrsLnG1 (2) CrsLnG1 (3) EigLcG1 (1) EigLcG1 (2) EigLcG1 (3) StbMgG1 (1) StbMgG1 (2) StbMgG1 (3) BnwAtH1 (1) BnwAtH1 (2) DisRnL1 (2) DisRnL1 (3) DisRnL2 (1) S J J H H H HQ Specification Window:  HQ Specification Window Modified SAS configurations - 30 degree WOD condition S J J H H H CrsLnG1 (1) CrsLnG1 (2) CrsLnG1 (3) EigLcG1 (1) EigLcG1 (2) EigLcG1 (3) StbMgG1 (1) StbMgG1 (2) StbMgG1 (3) BnwAtH1 (1) BnwAtH1 (2) DisRnL1 (2) DisRnL1 (3) DisRnL2 (1) Simulation Results - Hovering Flight:  Simulation Results - Hovering Flight Angular rate responses (deg/sec) Result with Original SAS configurations Result with Optimized SAS configurations Time [sec] P, deg/sec Q, deg/sec R, deg/sec - 30 degree WOD P, deg/sec Q, deg/sec R, deg/sec Time [sec] - 0 degree WOD Simulation Results - Hovering Flight:  Simulation Results - Hovering Flight SAS outputs (%) Result with Original SAS configurations Result with Optimized SAS configurations - 30 degree WOD Time [sec] RSAS, % PSAS, % YSAS, % RSAS, % PSAS, % YSAS, % Time [sec] - 0 degree WOD Simulation Results - Hovering Flight:  Pilot stick inputs (%) Simulation Results - Hovering Flight - 0 degree WOD - 30 degree WOD Time [sec] Time [sec] Result with Original SAS configurations Result with Optimized SAS configurations Lateral cyclic input : Left  0%, Right  100% Longitudinal cyclic input : Forward  0% , Aft 100% Collective input : Down  0%, Up  100% Pedal input : Left  0%, Right  100% LAT, % LON, % PED, % COL, % LAT, % LON, % PED, % COL, % Simulation Results - Hovering Flight:  Simulation Results - Hovering Flight Angular rate autospectrum (dB) Result with Original SAS configurations Result with Optimized SAS configurations - 30 degree WOD P, dB Q, dB R, dB - 0 degree WOD Autospectra identified by CIFER P, dB Q, dB R, dB Frequency [rad/sec] Frequency [rad/sec] Simulation Results - Hovering Flight:  Pilot stick input autospectrum (dB) Simulation Results - Hovering Flight - 0 degree WOD - 30 degree WOD Frequency [rad/sec] Frequency [rad/sec] Result with Original SAS configurations Result with Optimized SAS configurations LAT, dB LON, dB PED, dB COL, dB Autospectra identified by CIFER LAT, dB LON, dB PED, dB COL, dB H infinity Controller for SAS:  H infinity Controller for SAS Include frequency-dependent weight functions for control inputs and outputs Produce a controller K∞ to reduce the tracking deviations to reject disturbances We is a high-gain low-pass filter for good tracking and disturbance rejection Wu is a low-gain high-pass filter to improve the robustness and to limit the control activity H∞ controller (K∞) Aircraft (UH-60) Weighting (We) Weighting (Wu) Gust Filter (Wg) + + + - dw d u ee eu y ref + + dt H infinity Controller Design:  H infinity Controller Design Obtain a controller solving a classical 4-block problem based on 8-rigid-state linearized aircraft model 3 diagonal components of weighting functions iterate to find the optimal weighting parameters Aircraft We Wu = , , , , , 14-state H∞ controller Simulation Results - Hovering Flight:  Simulation Results - Hovering Flight Angular rate responses (deg/sec) Time [sec] P, deg/sec Q, deg/sec R, deg/sec - 30 degree WOD P, deg/sec Q, deg/sec R, deg/sec Time [sec] - 0 degree WOD Simulation Results - Hovering Flight:  Simulation Results - Hovering Flight SAS outputs (%) - 30 degree WOD Time [sec] RSAS, % PSAS, % YSAS, % RSAS, % PSAS, % YSAS, % Time [sec] - 0 degree WOD Simulation Results - Hovering Flight:  Simulation Results - Hovering Flight Pilot stick inputs (%) - 0 degree WOD - 30 degree WOD Time [sec] Time [sec] LAT, % LON, % PED, % COL, % LAT, % LON, % PED, % COL, % Simulation Results - Hovering Flight:  Simulation Results - Hovering Flight Angular rate autospectrum (dB) - 30 degree WOD P, dB Q, dB R, dB - 0 degree WOD Autospectra identified by CIFER P, dB Q, dB R, dB Frequency [rad/sec] Frequency [rad/sec] Simulation Results - Hovering Flight:  Simulation Results - Hovering Flight Pilot stick input autospectrum (dB) - 0 degree WOD - 30 degree WOD Frequency [rad/sec] Frequency [rad/sec] LAT, dB LON, dB PED, dB COL, dB Autospectra identified by CIFER LAT, dB LON, dB PED, dB COL, dB Slide25:  Rotorcraft Flight Simulator Flight dynamics model is based on Genhel Use FlightGear environment for visualization Integrated with time-varying airwake data from CFD Integrated with CHARM freewake model Slide26:  Schedule and Milestones Tasks 2001 2002 2004 2005 Update GenHel Simulation for shipboard simulation Develop simplified MATLAB Sim for control design Interface GenHel with ship air wake solutions and ship motion Develop maneuver controller Validation of DI simulation (using JSHIP data) Develop stochastic airwakes disturbance model and develop physical understanding Develop real-time simulation capability at PSU Incorporate CHARM free wake into the model Task tailored control law design, support with real-time simulator at PSU Lee PhD Degree Derek Bridges PhD Degree 2003 2006 Slide27:  Developed stochastic airwake disturbance model for 0° and 30° WOD, use for off-line analysis, real-time simulation and flight control design Real-time simulation facility is ready, integrated with time-varying airwake model and CHARM freewake model Developed task-tailored control laws using CONDUIT and H infinity control method Presented results at 2004 AIAA AFM conference, paper published in AIAA Journal of Aircraft, paper submitted to Journal of Aerospace Engineering (special issue on shipboard aviation) 2004 Accomplishments Planned Accomplishments for 2005 Will present results at 2005 AHS Forum and submit as journal article Continue to update and improve model, include the deck ground effects Further study in task tailored control laws to improve disturbance rejection Expand flight control design efforts, autonomous landing flight control system, position hold over ship deck Investigate use of equivalent airwake disturbances as tool for validating ship CFD airwake models. Slide28:  Technology Transfer Activities: Leveraging or Attracting Other Resources or Programs: Recommendations at the 2004 Review: Actions Taken: Presented results at 2004 AIAA AFM Conference Briefing to Navy Flight Dynamics Group at in Summer 2004, planning further interaction. Obtained JSHIP flight test data for validation, Cdr. Kevin Delemar at NRTC is contact Integrating with CHARM free wake model Integrated model and controllers with simulation facility developed under DURIP funds Get with Navy to focus the project and also to interface with CFD activities (flow field). Met with Navy. Received recommendations and we are planning more interaction. Proposed use of equivalent airwake disturbance model as tool for validation of CFD airwakes. Slide29:  Overview of Accomplishments 2001-2005 Advanced Simulation Model for Shipboard Operations Interface with time accurate CFD solutions of ship airwake High order Peters-He inflow Tunable OCM pilot model MATLAB / Simulink version of model for rapid development and control design Validation against JSHIP flight test data Implemented in real-time simulation facility at PSU Stochastic Airwake Disturbance Model Method for extracting equivalent disturbances from simulation with full CFD airwake (can also be applied to flight test data) Derived spectral filters to represent airwake disturbances Task-Tailored Control Design for Shipboard Operations Optimized SAS for operation in airwake using CONDUIT® Use spectral filters in control synthesis Optimized SAS using H∞ synthesis Publications 5 conference papers, 1 journal paper published, 1 journal paper under review Slide30:  Future Path Additional Basic Research Transition to Applications / Applied Research Should pursue similar analyses to study effects of building airwakes on UAVs operating in urban areas, proposed as follow on for next RCOE Potential to investigate impacts on shipboard handling qualities requirements – Maritime ADS-33. Could make further efforts to pursue the fully coupled problem, model effect of rotor wake on ship airwake, would need more CFD expertise Apply equivalent airwake disturbance method to validate ship airwake CFD analysis. Airwake disturbance can be extracted from flight test and compared to simulation with CFD wake Use stochastic airwake model as a simplified and more compact model for use in trainers Apply maneuver controller and simulation for analysis of new aircraft and new ship designs

Add a comment

Related presentations