Transcript Navigation and Control of Autonomous Vehicles with

POLItecnico
di MIlano
NAVIGATION AND CONTROL OF
AUTONOMOUS VEHICLES WITH
INTEGRATED FLIGHT ENVELOPE
PROTECTION
C.L. Bottasso
Politecnico di Milano
Workshop CRUI-ACARE
Napoli, July 14, 2006
Flight Envelope Protection for UAVs
Outline
• Background on flight envelope protection;
• Proposed research: model-based optimal control with integrated
flight envelope protection;
- Envelope-aware path planning (tactical control layer);
- Envelope-aware path tracking (reflexive control layer);
- Adaptive reduced vehicle model;
• Preliminary results;
• Conclusions and outlook.
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Background on Flight Envelope Protection
Flight Envelope Protection for UAVs
Care-Free Maneuvering (CFM):
Monitor and maintain vehicle operation within an operational
envelope (Massey 1992).
Example:
n
n
V
V
Pull-up with flight envelope violation
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Pull-up within the flight envelope
Background on Flight Envelope Protection
Flight Envelope Protection for UAVs
n
CFM working principle:
1) Predict limit onset
2) Cue pilot and/or modify control actions so as to
avoid boundary violation
V
Piloted flight:
• CFM cues pilot (often tactile cues through force-feel feedback on
active control stick, which can be overridden by the pilot), and/or
• CFM interacts with Flight Control System (FCS), which in turn
corrects the command inputs.
Autonomous flight:
• CFM interacts with trajectory planner (tactical controller) so as to
generate a safe-to-be-tracked response profile, and/or
• Interacts with trajectory tracker (reflexive controller), by correcting
the command inputs.
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Background on Flight Envelope Protection
Flight Envelope Protection for UAVs
CFM systems:
• Indispensable for utilizing full flight envelope without exceeding
aerodynamic, structural, propulsive and controllability limits;
• Avoid need for conservative envelope limits (reduced weight,
cost, etc. and/or improved performance, safety, handling qualities,
etc.);
• Contribute to the reduction of pilot work-load in piloted systems;
but difficult …
• Due to high agility and maneuverability of modern highperformance vehicles;
• Because of need to monitor multiple flight envelope limits, which
depend on multiple vehicle states and control inputs.
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Background on Flight Envelope Protection
Flight Envelope Protection for UAVs
Previous work:
dynamic trim (Calise & Prasad), peak-response estimation (Horn),
non-linear function response (Horn), reactionary envelope
protection (Prasad).
Available methods suffer from various limitations and approximations,
especially for UAVs:
• FCS can not typically deal directly with constraints
with CFM not trivial, possibly inefficient/ineffective;
⇨ coupling
• Adaptive limit parameter estimation does not exploit adaptive
capabilities of FCS;
• Trajectory planning typically very simple (interpolation of waypoints), unable to deal directly with constraints ⇨ no guarantee of
feasible within-the-boundary profile.
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Optimal Control CFM
Flight Envelope Protection for UAVs
Proposed work:
Optimal-control model-based tactical and reflexive control
architecture with integrated flight envelope protection.
Highlights:
• Optimal control can rigorously deal with constraints;
• Optimal-control planning of trajectories (tactical layer)
guaranteed feasibility;
• Optimal-control tracking (reflexive layer)
accounted for also at the level of the FCS;
• Adaptive reduced model
performance.
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⇨
⇨ constraints
⇨ improves both FCS and CFM
UAV Control Architecture
Flight Envelope Protection for UAVs
Hierarchical three-layer control architecture (Gat 1998):
• Strategic layer: assign mission objectives (typically relegated to a
human operator);
•Tactical layer: generate vehicle guidance information, based on
input from strategic layer and sensor information;
• Reflexive layer: track trajectory generated by tactical layer,
control, stabilize and regulate vehicle.
Obstacles
Vision/sensor range
Target
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Tactical Layer: Path Planning
Flight Envelope Protection for UAVs
Goal:
Plan paths compatible with the flight envelope boundaries for high
performance vehicles in complex/unstructured environments.
Approach: at each time step
• Discretize space and identify candidate way-points;
• Compute path by connecting way-points (A* search);
• Smooth path so as to make it compatible with flight envelope
boundaries, using motion primitives.
Obstacles
Target
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Flight Envelope Protection for UAVs
Tactical Layer: Motion Primitives
Vehicle model: maneuver automaton (Frazzoli et al. 2001), only two
possible states: trim or maneuver (finite-time transition between two
trims).
T5: high speed left turn
All maneuvers designed using
optimal control with envelope
protection constraints
T6: high speed right turn
T2: high speed level flight
M21: deceleration from T2 to T1
T1: low speed level flight
T3: low speed left turn
T4: low speed right turn
Highlights:
• Highly efficient transcription of the vehicle dynamics in small
solution space;
• Transcribed dynamics compatible with flight envelope boundaries.
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Flight Envelope Protection for UAVs
Tactical Layer: Maneuver Planning
Goal: plan a maneuver which is compatible with the flight
envelope boundaries.
y¤
Trajectory to be tracked
by reflexive controller
Optimal control: min
Subjected to:
J plan
¯
= Á(y; u)¯ +
T
Z
T
L(y; u) dt;
T0
_ y; u; p¤ ) = 0;
f (y;
• Reduced model equations:
Ã(y(T0 )) 2 [Ã0 ; Ã0 ];
min
max
• Boundary conditions:
(initial)
Ã(y(T )) 2 [Ã
;Ã
];
Tmin
• Constraints:
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Tmax
(final)
2
plan
g plan (y; u; T ) [g
; g plan ]:
min
max
Reflexive Layer: Trajectory Tracking
Flight Envelope Protection for UAVs
Goal: track trajectory while satisfying flight envelope constraints.
Prediction window
Prediction window
Tracking cost Tracking cost
Prediction window
Tracking cost
Reference
trajectory
Steering window
Steering window
Plant response
Steering window
1. Tracking 2. Steering
Optimal Control: min
Subjected to:
J track =
Z
T track
T track
Predictive solutions
(reduced model)
(jjy ¡ y ¤ jjS track + jju_ jj
y
S track
u
_
0
_ y; u; p¤ ) = 0;
f (y;
• Reduced model equations:
y(T track ) = ye0 ;
0
• Initial conditions:
g track (y; u; T ) 2 [g track ; g track ]:
• Constraints:
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min
max
) dt;
Reduced Model Adaption
Flight Envelope Protection for UAVs
Goal:
• Develop reduced model capable of predicting the behavior of the
plant with minimum error (same outputs when subjected to same
inputs) ⇨ critical for faithful flight envelope protection;
• Reduced model must be self-adaptive (capable of learning) to
adjust to varying operating conditions.
Prediction window
Prediction window
Tracking cost Tracking cost
Prediction window
Tracking cost
Prediction error
Prediction error
Steering window
Steering window
Prediction error
Plant response
1. Tracking
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Steering window
2. Steering
Reference
trajectory
Predictive solutions
3. Reduced model update
Reduced Model Adaption
Flight Envelope Protection for UAVs
Approach:
Neural-augmented reference model (Bottasso et al. 2004), using
extended Kalman parameter identification.
Idea:
A non-linear parametric function is identified online to capture
the mismatch (defect) between the plant and a non-linear
Reference model
reference vehicle model.
Plant
Highlights:
• Good predictions even before any
learning has taken place (otherwise
would need extensive pre-training);
• Easier and faster adaption: the
defect is typically a small quantity, if
the reference model is well chosen.
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Short transient =
fast adaption
Augmented reference
Preliminary Results
Flight Envelope Protection for UAVs
Procedures tested in a virtual environment using a high-fidelity
helicopter flight simulator.
Rotorcraft trajectory
Acceleration,
climb, aggressive
turn, descent,
deceleration, with
prescribed state
and control
limits:
Planned path
Rotorcraft trajectory when
tracking non-compatible path
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Flight Envelope Protection for UAVs
Preliminary Results
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Flight Envelope Protection for UAVs
Conclusions
• Proposed a procedure for navigation and control of vehicles which
respects the flight envelope;
• Flight envelope constraints are accounted for directly both at the
planning and tracking levels for the first time;
• Applicable to both fixed and rotary wing vehicles;
• Full system applicable to UAVs, but components applicable to
piloted flight to provide cues to pilots;
• On-line model adaption improves performance and limit
avoidance;
• Basic concept demonstrated in a virtual environment.
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Outlook
Flight Envelope Protection for UAVs
• Real-time implementation and integration in a rotorcraft UAV (in
progress) at the Autonomous Flight Lab at PoliMI;
• Testing and extensive experimentation;
• Integration with vision for fully autonomous navigation in complex
environments.
• Develop cueing system and test in the future flight simulation lab
at PoliMI.
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Flight Envelope Protection for UAVs
Acknowledgements
Work in collaboration with:
A. Croce (Post-Doc), L. Fossati (Graduate student), D. Leonello
(Ph.D. candidate), G. Maisano (Ph.D. candidate), R. Nicastro
(Graduate student), L. Riviello (Ph.D. candidate), B. Savini
(Ph.D. candidate).
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