CDC
2018 Workshop on
Parameter Convergence in Adaptive Control
without Persistence of Excitation
Date,
Time and Location: Sunday, December 16, 2018, 9:00 am – 1:00
pm, Splash 7-8, Fontainebleau
Hotel, Miami Beach, FL, USA.
Organizers:
Ø Sayan Basu
Roy, IIT Delhi, India.(email: sayanetce@gmail.com)
Ø Shubhendu Bhasin, IIT Delhi,
India.(email: sbhasin@ee.iitd.ac.in)
Ø Rushikesh Kamalapurkar,
Oklahoma State University, USA.(email:rushikesh.kamalapurkar@okstate.edu)
Objectives:
This workshop is aimed at
providing a comprehensive understanding of recent adaptive control algorithms
with a special focus on parameter convergence and transient performance
improvement. Classical adaptive controllers typically guarantee closed-loop
system stability and asymptotic convergence of tracking error, however,
parameter convergence is only guaranteed if a stringent condition of
persistence of excitation (PE) is satisfied by the regressor
signal. Some recent contributions in adaptive control, like concurrent learning
(CL), integral concurrent learning (ICL), Initial Excitation (IE)-based
adaptive control, have shown that parameter convergence is possible without
requiring the restrictive PE condition, rather milder conditions like a
full-rank condition on past stored data along systems trajectory (in CL-based
framework) or the condition of initial excitation (in IE-based framework) can
suffice for parameter convergence, and thereby improve transient response by
ensuring exponential stability. The workshop would cover crucial details of
these recent developments for relaxing the PE condition for parameter
convergence in adaptive control.
Broad
Areas Covered:
Ø Stability/Convergence
properties of Classical Adaptive Control
Ø Stability/Convergence
properties of Composite Adaptive Control
Ø Persistence
of Excitation (PE) condition and its practical limitations
Ø Recent
Advances to relax the PE condition for parameter convergence
Ø Concurrent
Learning (CL)-based Adaptive Control
Ø Integral
Concurrent Learning (ICL)-based Adaptive Control
Ø Initial
Excitation (IE)-based Adaptive Control
Expected
Outcome:
The workshop aims to
provide a comprehensive exposition to the issue of parameter convergence in
adaptive control with an in-depth understanding of the underlying mathematical
framework. The workshop attendees would be exposed to new classes of adaptive
controllers which relax the PE condition for parameter convergence, and thereby
guarantee exponential convergence of tracking and parameter estimation error to
zero. As the design methodologies are mostly based on modifications in the
adaptive estimation routine without hampering the classical Lyapunov-based
component, these design algorithms would be widely applicable to a varied class
of linear/nonlinear systems, which are already known to be stabilized by
classical adaptive design methodologies.
Target
Audience:
This workshop will be
beneficial to a wide range of people from academia as well as industry (it is
expected that the audience is having some preliminary understanding of
classical adaptive control.), since the new techniques are on one hand
analytically rigorous and on the other hand they have immediate practical
significance in terms of transient performance and robustness enhancement of
the adaptive controllers. For graduate students and researchers with an
interest in adaptive control, this workshop would expose them to the recent
advances in adaptive control with a focus on parameter convergence and
transient performance improvement. This workshop also aims at attracting
researchers interested in applications and practising engineers, who wish to
know the implementation aspects of these new adaptive control techniques.
Industries related to aircraft, spacecraft, robotics, process control etc.,
where adaptive control is popularly used, will especially benefit from the
workshop, since they can learn how to suitably integrate these high performance
adaptive control techniques with the existing adaptive control architecture.
List
of Speakers:
Ø Sayan Basu
Roy, IIT Delhi, India.
Ø Shubhendu Bhasin, IIT Delhi, India.
Ø Rushikesh Kamalapurkar,
Oklahoma State University, USA.
Ø Girish Chowdhary,
UIUC, USA.
Ø Warren Dixon, University of Florida,
Gainesville, USA.
Tentative
Title and Abstract of the Presentations:
Ø Adaptive Control and Parameter
Convergence
by Sayan Basu
Roy & Shubhendu Bhasin
Abstract:
This
talk would recapitulate the basic concepts of adaptive control in brief and
comment on the stability/convergence properties of classical and composite
adaptive controllers. The definition of persistence of excitation (PE) would be
introduced with simple examples and its effect on parameter convergence would
be discussed. The practical limitations of classical PE condition would be
emphasised to motivate the need for recently developed adaptive controllers like
concurrent learning (CL), integral concurrent learning (ICL) and initial
excitation (IE)-based techniques, which ensure parameter convergence without
requiring the restrictive PE condition.
Concurrent
learning and beyond: Towards adaptive control with learning and guarantees
by Girish Chowdhary
Abstract:
TBD
Related
video:
Reference:
Girish Chowdhary, Eric N. Johnson, Rajeev Chandramohan, M. Scott Kimbrell,
and Anthony Calise. "Guidance and Control of
Airplanes under Actuator Failures and Severe Structural Damage", Journal of Guidance, Control, and Dynamics,
Vol. 36, No. 4 (2013), pp. 1093-1104.
Ø Integral Concurrent Learning and
Finite Window Integrals
by Warren Dixon
Abstract:
TBD
Ø Concurrent learning for simultaneous
state and parameter estimation
by Rushikesh Kamalapurkar
Abstract:
In
this talk, concurrent learning techniques are described for a class of
nonlinear systems where state measurements are unavailable. Excitation over a
finite time-interval (as opposed to persistent excitation) is required for
exponential convergence in the linear case and practical convergence in the
nonlinear case for the state and the parameter estimates. Purging methods are
developed to refresh the concurrent learning history stacks to capitalize on
the improved state estimates. Applications of the developed method for
model-based adaptive dynamic programming and model-based inverse reinforcement
learning are also discussed.
Ø Initial Excitation based Adaptive
Control and Two-tier Filter Architecture
by Sayan Basu
Roy & Shubhendu Bhasin
Abstract:
This
talk would provide a comprehensive description of two-tier filter design based adaptive
control mechanism, which ensures parameter convergence using a newly introduced
concept of initial excitation (IE). It has been established in several
research papers of the speakers that the IE condition is remarkably less
stringent than the classical PE condition while being online-verifiable unlike
PE. The talk would cover several aspects of the IE-based adaptive control
including intuitive motivation, analytical proofs, algorithmic implementation
and real world applications of the proposed set of adaptive controllers.