23,271 research outputs found
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
A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models
This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant
2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant
2009I0016
Portfolio selection problems in practice: a comparison between linear and quadratic optimization models
Several portfolio selection models take into account practical limitations on
the number of assets to include and on their weights in the portfolio. We
present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset
Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional
Value-at-Risk (LACVaR) models, where the assets are limited with the
introduction of quantity and cardinality constraints. We propose a completely
new approach for solving the LAM model, based on reformulation as a Standard
Quadratic Program and on some recent theoretical results. With this approach we
obtain optimal solutions both for some well-known financial data sets used by
several other authors, and for some unsolved large size portfolio problems. We
also test our method on five new data sets involving real-world capital market
indices from major stock markets. Our computational experience shows that,
rather unexpectedly, it is easier to solve the quadratic LAM model with our
algorithm, than to solve the linear LACVaR and LAMAD models with CPLEX, one of
the best commercial codes for mixed integer linear programming (MILP) problems.
Finally, on the new data sets we have also compared, using out-of-sample
analysis, the performance of the portfolios obtained by the Limited Asset
models with the performance provided by the unconstrained models and with that
of the official capital market indices
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