Orbital Research Linear Adaptive Control User Manual

Page 2

Orbital Research
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leveland, OH 44143-2140

Contact: Frederick J. Lisy, Ph.D.

Telephone (440) 449-5785

E-mail [email protected]

www.orbitalresearch.com

Rev D RMK 12-05-2003

Adaptive Control for Linearly Parameterized
Systems

Orbital Research Intelligent Control

Algorithm (ORICA)

In linear adaptive control algorithms, a control law is designed
based upon an assumed linear model. During operation, the
parameters of this linear model are updated and the
corresponding control design is adapted to reflect the updated

model. Several different approaches exist for the updating of
parameters, each based upon slightly different assumptions.
One approach is to treat the problem as if the parameters are
constant but unknown. This is known as the

problem

and is the underlying assumption of

, and related control algorithms. Typically, an

identification algorithm such as least squares estimation is used

to identify the unknown parameters. The

, which holds that the identified

parameters can be used as if they are equal to the true
parameters, is invoked and the identified parameters are used
to compute updated controller parameters via an automated
design process. Another approach makes the assumption that

the parameters are changing, though much more slowly than
the state variables. The rate at which the parameters evolve
in time can be controlled by the designer's choice of adaption
rate.

and their variants use this approach.

As noted above, adaptive controllers involve control law
calculations that are based upon estimates of the controlled
plant parameters, , that are identified in real time. This
identification is typically performed via a recursive formulation
of the

, though other

estimation techniques such as

(LMS)

estimation can also be used. The control law calculations can

be performed directly, in which case the estimation is
configured to explicitly identify the controller parameters, or
indirectly, in
which case the
estimation
routine identifies

the plant
parameters,
which are in turn
used to compute
the controller
parameters.

Another adaptive
control approach
is

GPC is based

upon an assumed model

of the plant or process to
be controlled and on an
assumed scenario of
future control signals.
Predictive controllers

produce a sequence of
control signals for the
range of times from the
next time step to a future
time, known as the

Only the first of these
control signals is input to
the system and a new
sequence of control
signals is calculated when a new measurement is obtained.
This approach has proven to be robust to both model order

and system dead time assumptions as well as for nonminimum
phase systems and systems with unstable or badly damped
open loop poles.

The major limitation on the application of the GPC algorithm
is the computational cost associated with the computation of
the block of control inputs. Because of this, the application of
this algorithm has generally limited to the control of processes
and plants whose dynamics are sufficiently slow to permit the
computation the control inputs. The computational power of

modern processor as well as the development of more
efficient algorithms for computing block control inputs has
however made the application of this algorithm feasible for
the real time control of systems with significantly faster
dynamics such as aircraft wing flutter. Orbital Research, Inc.
has developed an efficient predictive adaptive control

algorithm, the

, that has successfully been used to

control aircraft wing flutter in NASA Langley Research
Center's Benchmark Active Controls Test (BACT) platform
and to control the Smart Munitions Tracking Scope (SMTS) at
the White Sands Missile Test Range.

tuning

Self Tuning Regulators

(STR)

Certainty

Equivalence Principle

Model Reference Adaptive Controllers (MRAC)

Least Squares Estimator (LSE)

Least Mean Squared

Generalized

Predictive Control
(GPC).

prediction horizon

Orbital Research Intelligent Control

Algorithm, (ORICA)

Åström, K.J., Wittenmark, B.,

, Addison Wesley, Reading,

MA, 1989

Dong, Y., Chizeck, H.J., “A Unified Approach to the Analysis and Design of

Predictive Control,” In Proc. 1991 American Control Conference, Boston,
MA, 1991.

Dong, Y., Chizeck, H.J., Khoury, J.M., Schmidt, R.N., “Extended Horizon
Adaptive Block Predictive Controller with an Efficient Prediction System,”

U.S. Patent Number 5424942, June 13, 1995.

Adaptive Control

ORICA has been used to

cont

smart munitions

tracking scope (SMTS) at

White Sands Missile Test Range.

rol the

ORICA has successfully been

used forsuppression of wing

flutter in NASA Lanfley’s BACT

test bed facility