485 research outputs found
Method for Solving State-Path Constrained Optimal Control Problems Using Adaptive Radau Collocation
A new method is developed for accurately approximating the solution to
state-variable inequality path constrained optimal control problems using a
multiple-domain adaptive Legendre-Gauss-Radau collocation method. The method
consists of the following parts. First, a structure detection method is
developed to estimate switch times in the activation and deactivation of
state-variable inequality path constraints. Second, using the detected
structure, the domain is partitioned into multiple-domains where each domain
corresponds to either a constrained or an unconstrained segment. Furthermore,
additional decision variables are introduced in the multiple-domain
formulation, where these additional decision variables represent the switch
times of the detected active state-variable inequality path constraints. Within
a constrained domain, the path constraint is differentiated with respect to the
independent variable until the control appears explicitly, and this derivative
is set to zero along the constrained arc while all preceding derivatives are
set to zero at the start of the constrained arc. The time derivatives of the
active state-variable inequality path constraints are computed using automatic
differentiation and the properties of the chain rule. The method is
demonstrated on two problems, the first being a benchmark optimal control
problem which has a known analytical solution and the second being a
challenging problem from the field of aerospace engineering in which there is
no known analytical solution. When compared against previously developed
adaptive Legendre-Gauss-Radau methods, the results show that the method
developed in this paper is capable of computing accurate solutions to problems
whose solution contain active state-variable inequality path constraints.Comment: 31 pages, 7 figures, 5 table
Eigenvector approximate dichotomic basis method for solving hyper-sensitive optimal control problems
Throughput Optimization in High Speed Downlink Packet Access (HSDPA)
In this paper, we investigate throughput optimization
in High Speed Downlink Packet Access (HSDPA). Specifically,
we propose offline and online algorithms for adjusting
the Channel Quality Indicator (CQI) used by the network to
schedule data transmission. In the offline algorithm, a given
target BLER is achieved by adjusting CQI based on ACK/NAK
history. By sweeping through different target BLERs, we can
find the throughput optimal BLER offline. This algorithm could
be used not only to optimize throughput but also to enable fair
resource allocation among mobile users in HSDPA. In the online
algorithm, the CQI offset is adapted using an estimated short
term throughput gradient without specifying a target BLER. An
adaptive stepsize mechanism is proposed to track temporal variation
of the environment. We investigate convergence behavior
of both algorithms. Simulation results show that the proposed
offline algorithm can achieve the given target BLER with good
accuracy. Both algorithms yield up to 30% HSDPA throughput
improvement over that with 10% target BLER
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