76 research outputs found
Closed-loop Reference Models for Output-Feedback Adaptive Systems
Closed-loop reference models have recently been proposed for states
accessible adaptive systems. They have been shown to have improved transient
response over their open loop counter parts. The results in the states
accessible case are extended to single input single output plants of arbitrary
relative degree.Comment: v1 Submitted to European Control Conference 2013, v2 Typos correcte
Cumulative Prospect Theory Based Dynamic Pricing for Shared Mobility on Demand Services
Cumulative Prospect Theory (CPT) is a modeling tool widely used in behavioral
economics and cognitive psychology that captures subjective decision making of
individuals under risk or uncertainty. In this paper, we propose a dynamic
pricing strategy for Shared Mobility on Demand Services (SMoDSs) using a
passenger behavioral model based on CPT. This dynamic pricing strategy together
with dynamic routing via a constrained optimization algorithm that we have
developed earlier, provide a complete solution customized for SMoDS of
multi-passenger transportation. The basic principles of CPT and the derivation
of the passenger behavioral model in the SMoDS context are described in detail.
The implications of CPT on dynamic pricing of the SMoDS are delineated using
computational experiments involving passenger preferences. These implications
include interpretation of the classic fourfold pattern of risk attitudes,
strong risk aversion over mixed prospects, and behavioral preferences of self
reference. Overall, it is argued that the use of the CPT framework corresponds
to a crucial building block in designing socio-technical systems by allowing
quantification of subjective decision making under risk or uncertainty that is
perceived to be otherwise qualitative.Comment: 17 pages, 6 figures, and has been accepted for publication at the
58th Annual Conference on Decision and Control, 201
Safe and Stable Adaptive Control for a Class of Dynamic Systems
Adaptive control has focused on online control of dynamic systems in the
presence of parametric uncertainties, with solutions guaranteeing stability and
control performance. Safety, a related property to stability, is becoming
increasingly important as the footprint of autonomous systems grows in society.
One of the popular ways for ensuring safety is through the notion of a control
barrier function (CBF). In this paper, we combine adaptation and CBFs to
develop a real-time controller that guarantees stability and remains safe in
the presence of parametric uncertainties. The class of dynamic systems that we
focus on is linear time-invariant systems whose states are accessible and where
the inputs are subject to a magnitude limit. Conditions of stability, state
convergence to a desired value, and parameter learning are all elucidated. One
of the elements of the proposed adaptive controller that ensures stability and
safety is the use of a CBF-based safety filter that suitably generates safe
reference commands, employs error-based relaxation (EBR) of Nagumo's theorem,
and leads to guarantees of set invariance. To demonstrate the effectiveness of
our approach, we present two numerical examples, an obstacle avoidance case and
a missile flight control case.Comment: 10 pages, 5 figures, IEEE CDC 202
Discrete-Time Adaptive Control of a Class of Nonlinear Systems Using High-Order Tuners
This paper concerns the adaptive control of a class of discrete-time
nonlinear systems with all states accessible. Recently, a high-order tuner
algorithm was developed for the minimization of convex loss functions with
time-varying regressors in the context of an identification problem. Based on
Nesterov's algorithm, the high-order tuner was shown to guarantee bounded
parameter estimation when regressors vary with time, and to lead to accelerated
convergence of the tracking error when regressors are constant. In this paper,
we apply the high-order tuner to the adaptive control of a particular class of
discrete-time nonlinear dynamical systems. First, we show that for plants of
this class, the underlying dynamical error model can be causally converted to
an algebraic error model. Second, we show that using this algebraic error
model, the high-order tuner can be applied to provably stabilize the class of
dynamical systems around a reference trajectory.Comment: 8 pages, submitted to the 2023 AC
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