3,246 research outputs found
Energy-Efficient Resource Allocation Optimization for Multimedia Heterogeneous Cloud Radio Access Networks
The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm
which incorporates the cloud computing into heterogeneous networks (HetNets),
thereby taking full advantage of cloud radio access networks (C-RANs) and
HetNets. Characterizing the cooperative beamforming with fronthaul capacity and
queue stability constraints is critical for multimedia applications to
improving energy efficiency (EE) in H-CRANs. An energy-efficient optimization
objective function with individual fronthaul capacity and inter-tier
interference constraints is presented in this paper for queue-aware multimedia
H-CRANs. To solve this non-convex objective function, a stochastic optimization
problem is reformulated by introducing the general Lyapunov optimization
framework. Under the Lyapunov framework, this optimization problem is
equivalent to an optimal network-wide cooperative beamformer design algorithm
with instantaneous power, average power and inter-tier interference
constraints, which can be regarded as the weighted sum EE maximization problem
and solved by a generalized weighted minimum mean square error approach. The
mathematical analysis and simulation results demonstrate that a tradeoff
between EE and queuing delay can be achieved, and this tradeoff strictly
depends on the fronthaul constraint
A numerical study on active control for tiltrotor whirl flutter stability augmentation
The use of active control to augment whirl flutter stability of tiltrotor aircraft is studied by means of a multibody simulation. The numerical model is based on a 1/5 scale semi-span aeroelastic wind tunnel model of a generic tiltrotor concept and possesses a gimballed, stiff-in-plane rotor that is windmilling. A single-input single-output controller and two types of multi-input multi-output algorithms, Linear Quadratic Gaussian Control and Generalized Predictive Control, are studied. They are using measured wing deflections in order to calculate appropriate swashplate input. Results on the closed-loop behavior of three wing and two gimbal natural modes are given. Robustness analyses with respect to major parameters like wing natural frequencies or structural damping are also briefly discussed. The rotor shear force is shown in the uncontrolled condition and in presence of a controller in order to illustrate the whirl flutter mechanism. The single-input single-output controller yielded substantial gain in stability and turned out to be most suitable for industrial application, whereas the Linear Quadratic Gaussian Regulator yielded even higher damping and still had good robustness characteristics
On Optimal Input Design for Feed-forward Control
This paper considers optimal input design when the intended use of the
identified model is to construct a feed-forward controller based on measurable
disturbances. The objective is to find a minimum power excitation signal to be
used in system identification experiment, such that the corresponding
model-based feed-forward controller guarantees, with a given probability, that
the variance of the output signal is within given specifications. To start
with, some low order model problems are analytically solved and fundamental
properties of the optimal input signal solution are presented. The optimal
input signal contains feed-forward control and depends of the noise model and
transfer function of the system in a specific way. Next, we show how to apply
the partial correlation approach to closed loop optimal experiment design to
the general feed-forward problem. A framework for optimal input signal design
for feed-forward control is presented and numerically evaluated on a
temperature control problem
An optimal controller for time-varying stochastic systems with multiple time delays
A flexible controller for optimal control of linear time-varying stochastic systems with multiple time delays is developed. The plants to be controlled are represented using a multi-input multi-output controlled autoregressive moving average model. The delays are described using a diagonal matrix. Input and output filters in the form of linear time-varying moving average operators are introduced into a generalized minimum variance control cost functional in order to meet the needs of various applications. The controller is applicable to a large class of linear time-varying systems
5G green cellular networks considering power allocation schemes
It is important to assess the effect of transmit power allocation schemes on
the energy consumption on random cellular networks. The energy efficiency of 5G
green cellular networks with average and water-filling power allocation schemes
is studied in this paper. Based on the proposed interference and achievable
rate model, an energy efficiency model is proposed for MIMO random cellular
networks. Furthermore, the energy efficiency with average and water-filling
power allocation schemes are presented, respectively. Numerical results
indicate that the maximum limits of energy efficiency are always there for MIMO
random cellular networks with different intensity ratios of mobile stations
(MSs) to base stations (BSs) and channel conditions. Compared with the average
power allocation scheme, the water-filling scheme is shown to improve the
energy efficiency of MIMO random cellular networks when channel state
information (CSI) is attainable for both transmitters and receivers.Comment: 14 pages, 7 figure
Gaussian process based model predictive control : a thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering, School of Engineering and Advanced Technology, Massey University, New Zealand
The performance of using Model Predictive Control (MPC) techniques is highly dependent
on a model that is able to accurately represent the dynamical system. The datadriven
modelling techniques are usually used as an alternative approach to obtain such
a model when first principle techniques are not applicable. However, it is not easy to
assess the quality of learnt models when using the traditional data-driven models, such
as Artificial Neural Network (ANN) and Fuzzy Model (FM). This issue is addressed in
this thesis by using probabilistic Gaussian Process (GP) models.
One key issue of using the GP models is accurately learning the hyperparameters.
The Conjugate Gradient (CG) algorithms are conventionally used in the problem of
maximizing the Log-Likelihood (LL) function to obtain these hyperparameters. In this
thesis, we proposed a hybrid Particle Swarm Optimization (PSO) algorithm to cope with
the problem of learning hyperparameters. In addition, we also explored using the Mean
Squared Error (MSE) of outputs as the fitness function in the optimization problem.
This will provide us a quality indication of intermediate solutions.
The GP based MPC approaches for unknown systems have been studied in the past
decade. However, most of them are not generally formulated. In addition, the optimization
solutions in existing GP based MPC algorithms are not clearly given or are
computationally demanding. In this thesis, we first study the use of GP based MPC approaches
in the unconstrained problems. Compared to the existing works, the proposed
approach is generally formulated and the corresponding optimization problem is eff-
ciently solved by using the analytical gradients of GP models w.r.t. outputs and control
inputs. The GPMPC1 and GPMPC2 algorithms are subsequently proposed to handle
the general constrained problems. In addition, through using the proposed basic and
extended GP based local dynamical models, the constrained MPC problem is effectively
solved in the GPMPC1 and GPMPC2 algorithms. The proposed algorithms are verified
in the trajectory tracking problem of the quadrotor.
The issue of closed-loop stability in the proposed GPMPC algorithm is addressed
by means of the terminal cost and constraint technique in this thesis. The stability
guaranteed GPMPC algorithm is subsequently proposed for the constrained problem. By
using the extended GP based local dynamical model, the corresponding MPC problem
is effectively solved
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