( t ) + a 1 ( t ) + a 0 sin( ( t ) 0 ) = bu ( t ) a 1 , a 0 , - - PowerPoint PPT Presentation

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State estimation over the limited-band communication channel for pitch motion control of LAAS helicopter benchmark Boris ANDRIEVSKY & Alexander FRADKOV & Dimitri PEAUCELLE IPME-RAS - St Petersburg, RUSSIA LAAS-CNRS -


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State estimation over the limited-band communication channel for pitch motion control of LAAS helicopter benchmark

Boris ANDRIEVSKY‡ & Alexander FRADKOV‡ & Dimitri PEAUCELLE†

‡ IPME-RAS - St Petersburg, RUSSIA † LAAS-CNRS - Universit´

e de Toulouse, FRANCE CNRS-RAS cooperative research project ”Robust and adaptive control of complex systems: Theory and applications”

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Introduction

CNRS-RAS cooperation objectives

➙ Investigate robustness issues of adaptive algorithms for control

both theoretically and through experiments

➙ Adaptive Identification (CCA’07, ALCOSP’07) ➙ Direct adaptive control (ROCOND’06, ALCOSP’07, ACC’07)

This presentation

➙ State-estimation in limited-band communication channel

with adaptive tuning of coder/decoder

& 1 IFAC ACA’07, June 2007, Toulouse

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Introduction ➙ Sensor data transmission over band limited communication channels:

navigation systems, distributed sensor networks, remote surveillance systems...

➙ Hybrid control:

continuous-time system ad control / discrete-time communication channel

➙ Use of smart sensors/coders: transmit condensed information ➙ Observer-based synchronization [Fradkov et. al., Physical Review 2006]

u(t) u(t)

− +

q(kT) e(kT) e(kT) Communication channel Observer Controller System Observer Decoder Adaptive tuning Coder Adaptive tuning Decoder X(t) e(t) y(t) y(t)

➙ Experiments with an ”Helicopter” benchmark

& 2 IFAC ACA’07, June 2007, Toulouse

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System, Observer and Controller

”Helicopter” Benchmark by Quanser at LAAS-CNRS ”Pitch” motion under assumptions (neglected gyroscopic torques, airspeed depen-

dence, aerodynamical forces, dry friction, travel and elevation coupling, motor dynamics)

¨ θ(t) + a1 ˙ θ(t) + a0 sin(θ(t) − θ0) = bu(t) a1, a0, θ0 and b have been identified and model validated on experiments.

& 3 IFAC ACA’07, June 2007, Toulouse

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System, Observer and Controller

PID with estimates of the state

u(t) = kPǫ(t) + kI

t

0 ǫ(τ)dτ + kDˆ

ω(t)

where ǫ(t) = θref(t) − ˆ

θ(t) and (ˆ θ, ˆ ω) are estimates of (θ, ω = ˙ θ). (kP, kI, kD) designed by loop shaping for the linearized system.

Dynamic observer for estimation   

˙ ˆ θ(t) ˙ ˆ ω(t)

   =   

ˆ ω(t) −a1ˆ ω(t) − a0 sin(ˆ θ(t) − θ0) + bu(t)

   +    l1

l2

   ¯

e(t)

where ¯

e(t) = ¯ e(kT) for t ∈ [ kT (k + 1)T[. (l1, l2) designed by pole placement for the linearized system.

& 4 IFAC ACA’07, June 2007, Toulouse

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Coder / Decoder

u(t) u(t)

− +

q(kT) e(kT) e(kT) Communication channel Observer Controller System Observer Decoder Adaptive tuning Coder Adaptive tuning Decoder X(t) e(t) y(t) y(t)

Communication channel characteristics

➙ T sampling period ➙ µ symbols in the coding alphabet. ➙ Channel data rate: R = log2(µ) bit per interval, ¯ R = R/T bit/s ➙ [Nair, Evans Automatica 2003] closed loop α-stability cannot hold if R <

  • |λi|>α

log2 |λi/α|

where λi are the poles of the sampled open-loop discrete time system.

➙ For the considered system and T = 0.1s one needs R > 0.4 bit per interval.

& 5 IFAC ACA’07, June 2007, Toulouse

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Coder / Decoder

Coder characteristics

➙ Uniform scaled saturated quantization q(kT) = satMk(βround(β−1δ(kT)))

q(kT) δ(kT) −M +M

➙ One-step memory centering: δ(kT) = e(kT) − ck with ck+1 = ck + q(kT). ➙ One-step memory adaptive zooming (ρ > 1): Mk+1 = m + ρMk

if

|q(kT) + q((k + 1)T)| ≥ 2Mk Mk+1 = m + ρ−1Mk

  • therwise

where m = (1 − ρ−1)Mmin

& 6 IFAC ACA’07, June 2007, Toulouse

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Coder / Decoder

Binary coding

➙ Binary coding (2 symbols in the coding alphabet, {−1, 1})

is optimal w.r.t. channel data rate

➙ Centering not needed (coding of error signal) ➙ Coding resumes to σ(kT) = sign(e(kT)) ➙ Adaptive zooming is given as Mk+1 = m + ρMk

if

|σ(kT) + σ((k + 1)T)| = 0 Mk+1 = m + ρ−1Mk

  • therwise

➙ Decoding is ¯ e(kT) = Mkσ(kT).

& 7 IFAC ACA’07, June 2007, Toulouse

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Implementation requirements

u(t) u(t)

− +

q(kT) e(kT) e(kT) Communication channel Observer Controller System Observer Decoder Adaptive tuning Coder Adaptive tuning Decoder X(t) e(t) y(t) y(t)

➙ Both Observers and Adaptive algorithms have same initial conditions ➙ If control signal u(t) not available to the sensor then duplicate controller

− +

e(kT) e(kT) q(kT) Communication channel Decoder Observer Controller Adaptive tuning Coder System Controller Observer Adaptive tuning Decoder

e(t) X(t) y(t) y(t) X(t) u(t) u(t)

➙ Needs to synchronize both controllers with same reference signal:

  • ther coder/decoder pair for reference signal transmission.

& 8 IFAC ACA’07, June 2007, Toulouse

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Experimental results

”Ideal” control using sensor data ¯

R = 300 bit/s

Same experimental conditions with adaptive coder/decoder at T = 0.05s.

& 9 IFAC ACA’07, June 2007, Toulouse

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Experimental results

Same experimental conditions with adaptive coder/decoder at T = 0.1s.

& 10 IFAC ACA’07, June 2007, Toulouse

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Conclusions

Control with band limited communication channel

➚ Smart sensors with observers and adaptive coder/decoder ➚ Relatively low transmission rate validated on experiments

Future experiments

➙ 3-D control of the benchmark ➙ Transmission channel with delays ➙ Transmission channel with errors

& 11 IFAC ACA’07, June 2007, Toulouse