Quadratic Programming Approach Research Articles

Numerical investigation of optimal yaw misalignment and collective pitch angle for load imbalance reduction of rigid and flexible HAWT blades under sheared inflow

Wind shear can strongly influence the cyclic loading on horizontal axis wind turbine blades. These load fluctuation causes a variation of power output and introduces fatigue load. Thus, individual pitch controllers have been developed that are focused on the load alleviations, however, comes at a price of actuator requirements for control. Moreover, these controllers are unable to apply to already existing wind turbines with active yaw and collective pitch control system. Therefore, the investigations for minimizing load imbalance through the adjustments of yaw misalignment and collective pitch angle are implemented for the rigid and flexible blades under the sheared inflow. By applying the optimization process based on a sequential quadratic programming approach, the optimal yaw and pitch angle can be estimated. Then, the numerical simulations for predicting the performance are performed. The results showed that the fluctuation range of the root flapwise bending moment for the rigid blades can be reduced by 84.5%, whereas the vibratory bending moment for the flexible blades can be reduced by up to approximately 82.4% in the best case. Therefore, the magnitudes of load imbalance can be minimized by the adjustment of the optimal yaw misalignment and collective pitch angle without any power loss.

Two-stage observer based offset-free MPC

This paper addresses design of model predictive control (MPC) for dynamic systems in the presence of disturbances. For the systems with disturbances, it is common to use observers to estimate states and disturbances, allowing control algorithm to reject disturbances. In this paper, we demonstrate how one can extend model predictive control for systems with disturbances by applying full state and disturbance observer. To this aim, a two-stage approach is proposed where the observer design procedure is completely decoupled from the MPC problem, known as the separation principle. We focus on the decoupling principal and it is shown that the observer can be designed independently via a norm minimization problem to reduce effects of disturbances and model mismatch. On the other hand, the MPC law is obtained using the multi-parametric quadratic programming approach where the parameters are the components of the state vector.

Applying Pay-Burst-Only-Once Principle for Periodic Power Management in Hard Real-Time Pipelined Multiprocessor Systems

Pipelined computing is a promising paradigm for embedded system design. Designing a power management policy to reduce the power consumption of a pipelined system with nondeterministic workload is, however, nontrivial. In this article, we study the problem of energy minimization for coarse-grained pipelined systems under hard real-time constraints and propose new approaches based on an inverse use of the pay-burst-only-once principle. We formulate the problem by means of the resource demands of individual pipeline stages and propose two new approaches, a quadratic programming-based approach and fast heuristic, to solve the problem. In the quadratic programming approach, the problem is transformed into a standard quadratic programming with box constraint and then solved by a standard quadratic programming solver. Observing the problem is NP-hard, the fast heuristic is designed to solve the problem more efficiently. Our approach is scalable with respect to the numbers of pipeline stages. Simulation results using real-life applications are presented to demonstrate the effectiveness of our methods.

Reconstruction of free-form space curves using NURBS-snakes and a quadratic programming approach

In this study, we propose a robust algorithm for reconstructing free-form space curves in space using a Non-Uniform Rational B-Spline (NURBS)-snake model. Two perspective images of the required free-form curve are used as the input and a nonlinear optimization process is used to fit a NURBS-snake on the projected data in these images. Control points and weights are treated as decision variables in the optimization process. The Levenberg–Marquardt optimization algorithm is used to optimize the parameters of the NURBS-snake, where the initial solution is obtained using a two-step procedure. This makes the convergence faster and it stabilizes the optimization procedure. The curve reconstruction problem is reduced to a problem that comprises stereo reconstruction of the control points and computation of the corresponding weights. Several experiments were conducted to evaluate the performance of the proposed algorithm and comparisons were made with other existing approaches.

Natural Preconditioning and Iterative Methods for Saddle Point Systems

The solution of quadratic or locally quadratic extremum problems subject to linear(ized) constraints gives rise to linear systems in saddle point form. This is true whether in the continuous or the discrete setting, so saddle point systems arising from the discretization of partial differential equation problems, such as those describing electromagnetic problems or incompressible flow, lead to equations with this structure, as do, for example, interior point methods and the sequential quadratic programming approach to nonlinear optimization. This survey concerns iterative solution methods for these problems and, in particular, shows how the problem formulation leads to natural preconditioners which guarantee a fast rate of convergence of the relevant iterative methods. These preconditioners are related to the original extremum problem and their effectiveness---in terms of rapidity of convergence---is established here via a proof of general bounds on the eigenvalues of the preconditioned saddle point matri...

Frequency Dependent Spencer Modeling of Magnetorheological Damper Using Hybrid Optimization Approach

Magnetorheological dampers have been widely used in civil and automotive industries. The nonlinear behavior of MR fluid makes MR damper modeling a challenging problem. In this paper, a frequency dependent MR damper model is proposed based on Spencer MR damper model. The parameters of the model are identified using an experimental data based hybrid optimization approach which is a combination of Genetic Algorithm and Sequential Quadratic Programming approach. The frequency in the proposed model is calculated using measured relative velocity and relative displacement between MR damper ends. Therefore, the MR damper model will be function of frequency. The mathematical model is validated using the experimental results which confirm the improvement in the accuracy of the model and consistency in the variation damping with the frequency.

Degree reduction of Bézier curves with restricted control points area

In this paper, we propose a new approach to the problem of degree reduction of Bézier curves with respect to the least squares norm. We impose restrictions of the control points area to get more intuitive location of the control points. As a result, the degree reduced curve is suitable for further modification and applications. We present and compare two methods of solving the new problem. First one is based on quadratic programming approach and the other on BVLS algorithm. Some illustrative examples are given.

Anomaly detection and foresight response strategy for wireless sensor networks

Anomaly detection is an important challenge in wireless sensor networks (WSNs) for fault diagnosis and intrusion detection applications. Sensor nodes are usually designed to be small and inexpensive, so they have limited capabilities, such as limited computational power, memory and energy. This paper presents novel light-weight distributed anomaly detection and a foresight response strategy based on support vector data description (SVDD) for wireless sensor network. SVDD could sometimes generate such a loose decision boundary, when some noisy samples (outliers) exist in the training set. In addition, it requires the solution of a computationally intensive quadratic programming approach which is not applicable in WSNs. Hence, we modified the standard version of SVDD, and proposed the Linear Programming-based Fuzzy-Constraint SVDD (LP-FCSVDD) method to detect the outliers with more accuracy in acceptable time. Then we present a foresight response strategy to resist the intentional, unintentional and false anomalies. The overall experiments show prominence of our proposed method to achieve high detection accuracies on a variety of real and synthetic wireless sensor network datasets.

Counterparty Risk Reduction by the Optimal Netting of OTC Derivatives

The netting of OTC derivatives trades, known as 'compression', reduces systemic risk in financial markets by minimising counterparty exposures between large financial institutions, in particular the large dealer banks. We present here a framework for compression in the OTC derivatives market for interest rate swaps. We minimise the total net counterparty exposure by partially or fully unwinding existing swap trades and determine the degree of compression obtained as a function of the number of trades, the number of participating parties and the number of risk constraints. We do this using both linear programming (LP) and quadratic programming (QP) approaches. We are able to separately quantify the benefit of bilateral and multilateral netting. We also compare the tendency of both LP and QP approaches to favour full unwinds of existing trades versus partial unwinds. We calculate the performance of both optimisation approaches by calculating their average reduction in counterparty risk by simulating over large numbers of randomly generated trade sets. We show that significant compression can be achieved, and find that LP approaches are preferable as they are generally computationally faster and produce solutions with more full unwinds than QP approaches.

A sequential quadratic programming algorithm for equality-constrained optimization without derivatives

In this paper, we present a new model-based trust-region derivative-free optimization algorithm which can handle nonlinear equality constraints by applying a sequential quadratic programming (SQP) approach. The SQP methodology is one of the best known and most efficient frameworks to solve equality-constrained optimization problems in gradient-based optimization [see e.g. Lalee et al. (SIAM J Optim 8:682–706, 1998), Schittkowski (Optim Lett 5:283–296, 2011), Schittkowski and Yuan (Wiley encyclopedia of operations research and management science, Wiley, New York, 2010)]. Our derivative-free optimization (DFO) algorithm constructs local polynomial interpolation-based models of the objective and constraint functions and computes steps by solving QP sub-problems inside a region using the standard trust-region methodology. As it is crucial for such model-based methods to maintain a good geometry of the set of interpolation points, our algorithm exploits a self-correcting property of the interpolation set geometry. To deal with the trust-region constraint which is intrinsic to the approach of self-correcting geometry, the method of Byrd and Omojokun is applied. Moreover, we will show how the implementation of such a method can be enhanced to outperform well-known DFO packages on smooth equality-constrained optimization problems. Numerical experiments are carried out on a set of test problems from the CUTEst library and on a simulation-based engineering design problem.

A Stackelberg security game with random strategies based on the extraproximal theoretic approach

In this paper we present a novel approach for representing a real-world attacker–defender Stackelberg security game-theoretic model based on the extraproximal method. We focus on a class of ergodic controlled finite Markov chain games. The extraproximal problem formulation is considered as a nonlinear programming problem with respect to stationary distributions. The Lagrange principle and Tikhonov׳s regularization method are employed to ensure the convergence of the cost functions. We transform the problem into a system of equations in a proximal format, and a two-step (prediction and basic) iterated procedure is applied to solve the formulated problem. In particular, the extraproximal method is employed for computing mixed strategies, providing a strong optimization formulation to compute the Stackelberg/Nash equilibrium. Mixed strategies are especially found when the resources available for both the defender and the attacker are limited. In this sense, each equation in this system is an optimization problem for which the minimum is found using a quadratic programming approach. The model supports a defender and N attackers. In order to address the dynamic execution uncertainty in security patrolling, we provide a game-theoretic based method for scheduling randomized patrols. Simulation results provide a validations of our approach.

A hybrid algorithm based on fuzzy linear regression analysis by quadratic programming for time estimation: An experimental study in manufacturing industry

In time studies, estimation of the standard times with direct or indirect measurement methods is particularly difficult in companies having complex production schedules or ones employing an inexperienced workforce. Such companies require new and specific time measurement procedures. In this study, a new time estimation algorithm based on fuzzy linear regression analysis (FLRA) by quadratic programming (QP) is proposed for specific manufacturing systems. In our study, data is provided by one of the biggest casting and machining companies in Europe. The database includes items that have similar production processes. A fuzzy linear regression model is built by using the previously measured standard times of a product family. The model developed is used for estimating the standard times of the remaining products. FLRA based on QP approach facilitates integration of the central tendency of least squares and possible properties of fuzzy regression. The main factors that directly impact standard times are determined and used for the estimation of the fuzzy standard times. Through utilization of sum of squares error (SSE) and index of confidence (IC), the important factors in the model are identified. The use of QP makes it possible to reconcile the minimization of the deviation of central tendency and the spreads of membership functions in a simultaneous manner. In this study, the efficiency of the proposed algorithms in casting companies is authenticated. Besides, it is seen that this estimation procedure could be implemented easily for various sectors using the relevant algorithms.

Optimal PV Inverter Reactive Power Control and Real Power Curtailment to Improve Performance of Unbalanced Four-Wire LV Distribution Networks

The rapid uptake of residential photovoltaic (PV) systems is causing serious power quality issues such as significant voltage fluctuation and unbalance that are restricting the ability of networks to accommodate further connections. Based on the latent reactive power capability and real power curtailment of single-phase inverters, this paper proposes a new comprehensive PV operational optimization strategy to improve the performance of significantly unbalanced three-phase four-wire low voltage (LV) distribution networks with high residential PV penetrations. A multiobjective optimal power flow (OPF) problem that can simultaneously improve voltage magnitude and balance profiles, while minimizing network losses and generation costs, is defined and then converted into an aggregated single-objective OPF problem using the weighted-sum method, which can be effectively solved by the global Sequential Quadratic Programming (SQP) approach with multiple starting points in MATLAB. Detailed simulations are performed and analyzed for various operating scenarios over 24 h on a real unbalanced four-wire LV distribution network in Perth Solar City trial, Australia. Finally, smart meter readings are used to justify the validity and accuracy of the proposed optimization model and considerations on the application of the proposed PV control strategy are also presented.

Sensitivity analysis in linear programming approach to optimal SVM classification

At present, linear programming (LP) techniques for optimal one-class and two-class classification can be considered well established and feasible; they pose an alternative to the quadratic programming (QP) approach, which is usually credited with having greater complexity. Sensitivity analysis, well developed in the LP context, is generally employed to furnish answers describing how an optimal solution changes when varying the parameters in an LP problem; as a possible application in optimal classification, it can be employed for the definition of the free parameters present in LP procedures, reducing the events of computational restart from scratch when searching for a satisfactory classifier through repeated trials. The proposed method is demonstrated on a simple example which exhibits its effectiveness in reducing the computational burden, but this procedure can be extrapolated to large problems as well. Keywords : Linear Programming, Optimal Classification, Sensitivity Analysis, Support Vector Machines. Normal 0 14 false false false IT X-NONE X-NONE

Development of a graphical user interface for determining the optimal mixture parameters of normal weight concretes: A response surface methodology based quadratic programming approach

The optimization of a normal weight concrete mixture proportions for determination of the desired concrete quality is an important issue for concrete manufacturers and users. In this study, the constrained quadratic programming methodology is based on meta-models developed by using response surface methodology for determining optimal normal weight concrete mixture proportions. We developed a graphical user interface to take the burden of numerous experiments and complex mathematical calculations away from laboratory experts and manufacturers. However, we developed a graphical user interface based on MATLAB® toolbox which allows performing optimization of normal weight concrete mixture proportions interactively. The GUI was tested through real case studies and satisfactorily results were obtained. The results showed that developed graphical user interface was functional, effective and flexible in solving the optimization problems of normal weight concrete mixture proportions.

Investment Behavior of Canadian Egg Producers: Analyzing the Impacts of Risk Aversion and Variability of Prices and Costs of Production

Animal welfare is a major concern for consumers. This concern has not gone unnoticed by sector stakeholders, especially egg producers. One of the fundamental changes likely to affect egg producers regards modes of production, specifically changes in housing systems, ranging from conventional cages to free range. From farmers’ perspective, changing their mode of production generates a technological and economic/marketing risk. This study documents the level of risk in the Canadian egg sector (conventional and specialty eggs) using data from 2009 to 2011. Our results indicate multiple uncertainty sources (technological, cost of production, price of eggs) that vary according to the types of eggs. We use a quadratic programming approach applied to expected mean-variance models to analyze the impact of risk on decision to invest when the resources must be allocated to different types of production that have different risk levels. Overall our results show how, given risk aversion parameters, producers minimize their risk levels by devoting their resources to the least risky type of eggs. An important result of our study is that supply management, by reducing the perceived risk level, has favored the development of specialty eggs, for the benefit of consumers.

ROBUST SPARSITY-BASED DEVICE-FREE PASSIVE LOCALIZATION IN WIRELESS NETWORKS

As an emerging technique with a promising application prospect, the device-free passive localization (DFPL) technique has drawn considerable research efiorts due to its ability of realizing wireless localization without the need of carrying any device and participating actively in the localization process. Recent technological achievements of the DFPL technique have made it feasible to realize location estimation using the received signal strength (RSS) information of wireless links. However, one major disadvantage of the RSS-based DFPL technique is that the RSS measurement is too sensitive to noise and environmental variations, which incur the misjudgment of shadowed links and degradation of localization performance. Based on the natural sparsity of location flnding in the spatial domain, this paper proposes an environmental-adaptive sparsity-based localization method for the DFPL problem in the existence of model mismatch. The novel feature of this method is to adjust both the overcomplete basis (a.k.a. dictionary) and the sparse solution using a dictionary learning (DL) technique based on the quadratic programming approach so that the location solution can better match the changes of the RSS measurements between the node pairs to the spatial location of the target. Moreover, we propose a modifled re-weighting l1 norm minimization algorithm to improve reconstruction performance for sparse signals. The efiectiveness of the proposed scheme is demonstrated by experimental results where the proposed algorithm yields substantial improvement for localization performance.

Parametric Integrated Perturbation Analysis - Sequential Quadratic Programming Approach for Minimum-time Model Predictive Control

A minimum-time Model Predictive Control (MPC) problem is considered. By employing a time scaling transformation and cost regularization, it is shown that this problem becomes amenable to the application of parametric Integrated Perturbation Analysis - Sequential Quadratic Programming (IPA-SQP). The IPA-SQP framework exploits neighboring extremal optimal control and sequential quadratic programming based updates to efficiently and rapidly compute approximations to solutions in receding horizon optimal control. An interesting feature of the minimum-time MPC problem is that, after reformulation, the optimization needs to be performed simultaneously with respect to the control sequence and a constant parameter (terminal time) over the prediction horizon. Two examples are considered. The first example is for a double integrator with a control constraint. The second example is based on a two-dimensional model of a hypersonic vehicle.

A Conic Quadratic Programming Approach to Physical Layer Multicasting for Large-Scale Antenna Arrays

We investigate the problem of downlink physical layer multicasting that aims at minimizing the transmit power with a massive antenna array installed at the transmitter site. We take a solution based on semidefinite relaxation (SDR) as our benchmark. It is shown that instead of working on the semidefinite program (SDP) naturally produced by the SDR, the dual counterpart of the same problem may provide a more efficient numerical implementation. Later, by using a successive convex approximation strategy, we arrive at a provably convergent iterative second-order cone programming (SOCP) solution. Our thorough numerical investigations report that the newly proposed SOCP solution offers improved power efficiency and a massively reduced computational complexity. Therefore, the SOCP solution is seen as a suitable candidate for obtaining beamformers that minimize transmit power, especially, when a very large number of antennas is used at the transmitter.

Structural optimization of an asymmetric automotive brake disc with cooling channels to avoid squeal

Brake squeal is still a major issue in the automotive industry due to comfort complaints of passengers and resulting high warranty costs. Many measures to avoid squeal have been discussed in the engineering community reaching from purely passive measures like the increase of damping, e.g. by the application of shims, to the active or semiactive suppression of squeal. While active measures can be effective but are elaborate and therefore more expensive, passive measure are less complex in most cases. This leads to the necessity to develop passive, economic and robust measures to avoid squeal. Asymmetry of the brake rotor has been proposed to achieve this goal and the resulting split of all double eigenfrequencies of the brake rotor has lately been shown to stabilize the system. Thus, a structural optimization of an automotive brake disc with cooling channels is presented in this paper with the objective to split all eigenfrequencies of the brake rotor in a certain frequency range by introducing asymmetry to the cooling channels. Constraints of the optimization are balance constraints, to guarantee a balanced operation for all rotor speeds, and minimal and maximal distance constraints of the cooling ribs, due to cooling and material strength requirements. First, a modeling approach of the brake disc with cooling channels is shortly presented which helps to avoid remeshing during the structural optimization. The introduced optimization problem is known to be highly nonlinear, nonconvex and with many local optima to be expected. Therefore, two approaches for the solution of the problem are chosen. The first, a deterministic one, is a Sequential Quadratic Programming (SQP) approach efficiently targeting local optima. In order to increase the possibility to find the global optimum, a set of randomly distributed starting configurations is chosen, leading to satisfying results. The other, a heuristic approach, uses a Genetic Algorithm (GA) directly aiming for the global optimum. The GA also delivers very satisfying results, nevertheless, the best solution has been found with the SQP approach. In order to validate the basic idea that a defined separation of eigenfrequencies helps to avoid squeal, modal analysis and squeal tests have been performed with a simplified disc with radial holes. The conducted experiments strongly support the theoretical findings and demonstrate the superior squeal behavior of the optimized disc.