Following the research strands of enhanced index tracking and of portfolio performance measures optimization, we propose to choose, among the feasible asset portfolios of a given market, the one that maximizes the geo...
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Following the research strands of enhanced index tracking and of portfolio performance measures optimization, we propose to choose, among the feasible asset portfolios of a given market, the one that maximizes the geometric mean of the differences between its risk and gain and those of a suitable reference benchmark, such as the market index. This approach, which has a peculiar geometric interpretation and enjoys remarkable features, provides the efficient portfolio that dominates the largest amount of portfolios dominating the reference benchmark index. Preliminary empirical results highlight good out-of-sample performances of our approach compared with those of the market index.
In this paper, we propose a propulsion blade design for multirotor systems under actuator failures. The key challenge of the research is to balance the propulsion efficiency and performance on both rotating directions...
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ISBN:
(数字)9781624105784
ISBN:
(纸本)9781624105784
In this paper, we propose a propulsion blade design for multirotor systems under actuator failures. The key challenge of the research is to balance the propulsion efficiency and performance on both rotating directions. The fault tolerant performance of multirotor aircrafts is restricted by the unidirectional rotors that are not allowed to rotate reversely, while the bidirectional rotors can aid a multirotor to accommodate more patterns of actuator failures and enhance the resilience in severe environment. The shape of propeller blades is designed to meet desired aerodynamic performance. A hybrid optimization design process is carried out to produce rotor blades for specific requirements. An integrated method is used to reduce the computational costs and achieve global optimal, which is a combination of genetic algorithm and non-linear programming.
In this study, a distributed scheme is developed for actuators aboard of a small unmanned aerial system to achieve consensus in allocating aerodynamic moment contributions among them. These actuators include the main ...
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ISBN:
(数字)9781624105784
ISBN:
(纸本)9781624105784
In this study, a distributed scheme is developed for actuators aboard of a small unmanned aerial system to achieve consensus in allocating aerodynamic moment contributions among them. These actuators include the main control surfaces (aileron, rudder, and elevator) governed by 2nd order linear ordinary differential equations and distributed micro-scale synthetic jets governed by 2nd order nonlinear ordinary differential equations. This research is to address one technical gap about how to actuate control surfaces and synthetic jets in responding to the rich amount of information measured using micro-scale flow sensors (e.g. pressure and shear stress). The proposed allocator has three components to cancel nonlinear terms, achieve consensus, and track the desired total aerodynamic moment. In addition, weighting matrix is added to solve the issue relating to control limitations in synthetic jets and control surface deflections. Different from traditional centralized optimization based allocation approaches, the newly developed allocation scheme is decentralized, in a feedback form, and able to respond to uncertainties and noise. A Lyapunov approach is used to prove the asymptotically stability of the closed-loop system, and simulations are used to validate the proposed control allocator.
The problem of optimal obstacle avoidance in the presence of chance constraints is considered using the time minimization of a Dubin's car with a circular keep-out zone. Three cases of the Dubin's car problem ...
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ISBN:
(数字)9781624105784
ISBN:
(纸本)9781624105784
The problem of optimal obstacle avoidance in the presence of chance constraints is considered using the time minimization of a Dubin's car with a circular keep-out zone. Three cases of the Dubin's car problem are explored. For the first case, the only constraint is the circular keep out zone. For the second case, a three-sided box with side length equal to the diameter of the keep out zone is added as a second path constraint and for which the deterministic optimal control problem is unsolvable. The keep out zone is then transformed to have a probabilistic boundary, converting the problem from a deterministic to a chance-constrained optimal control problem. A Split-Bernstein approximation method is used to transform the chance constraint into a deterministic nonlinear constraint. The chance-constrained optimal control problem is solvable and the solution has a lower optimal cost than that of the first deterministic optimal control problem. Thus in certain applications there is a correlation between a lower optimal cost and higher risk that has numerous practical applications. Numerical results are shown that demonstrate the effectiveness of the technique.
Metaheuristic algorithms are useful techniques to cope with complex real-life optimization problems due to their efficiency, flexibility and adaptability to different classes of problems and restrictions. A wide varie...
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ISBN:
(数字)9781624105784
ISBN:
(纸本)9781624105784
Metaheuristic algorithms are useful techniques to cope with complex real-life optimization problems due to their efficiency, flexibility and adaptability to different classes of problems and restrictions. A wide variety of metaheuristic algorithms can be found in the literature. In this paper, we compare the performance of three different metaheuristics, namely Teaching-Learning Based Optimization (TLBO), Genetic Algorithm (GA) and Simulated Annealing (SA), in the solution of the system-level preventive maintenance planning problem. The problem consists in defining a maintenance interval for each component within the system to minimize the Estimated Cost of Repair (ECR), subjected to a reliability constraint. Numerical experiments were performed using systems with different complexity levels. The algorithms were compared in terms of relative speed of convergence, implementation complexity, accuracy, precision and execution time.
A model of train optimal operation problem is established in according to the process dynamics equations, the fraction and brake capability, the railway resistance, and the speed limits. As the ramp resistance and the...
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ISBN:
(纸本)9781728112497
A model of train optimal operation problem is established in according to the process dynamics equations, the fraction and brake capability, the railway resistance, and the speed limits. As the ramp resistance and the running resistance are introduced, the process described by Ordinary Differential Equations (ODEs) in the constrains are then transformed into Differential Algebraic Equations (DAEs), which makes the problem more difficult. In order to solve this problem, the state and control variables are parameterized in the time domain, converting the original control problem into a constrained nonlinear programming problem, which is finally solved by primal-dual predictor-corrector interior-point algorithm.
Viral load values and CD4(+)T cells count are markers currently evaluated in the clinical follow-up of HIV/AIDS patients. In this context, it is relevant to develop methods that provide a more complete temporal descri...
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ISBN:
(纸本)9783030306113;9783030306106
Viral load values and CD4(+)T cells count are markers currently evaluated in the clinical follow-up of HIV/AIDS patients. In this context, it is relevant to develop methods that provide a more complete temporal description of these markers, e.g. in between clinical appointments. To this end, we combine a mathematical model and a Bayesian methodology to estimate trajectories from a set of observed values. Also, we construct a variation band containing the most central trajectories for one patient, by exploring the range of values in the a posteriori distributions. The methods are illustrated with simulated data.
In this paper, climb performance optimization is studied to determine control trajectories that minimize the Direct Operating Cost of an air transport. First a model of the vehicle motion is developed. A reduced-order...
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ISBN:
(数字)9781624105890
ISBN:
(纸本)9781624105890
In this paper, climb performance optimization is studied to determine control trajectories that minimize the Direct Operating Cost of an air transport. First a model of the vehicle motion is developed. A reduced-order model is then derived, and the calculus of variations is applied to formulate the optimization problem using the reduced-order model. An algorithm is designed to solve the problem, and the efficacy of the solver is validated using the high-order model. Two optimization methods are developed. In the first method, nonlinear programming (NLP) is used to determine the optimal open-loop control. In the second method, the reduced-order model is further simplified-relative to the equations used in the first method-and termed the energy state approximation (ESA) method. The second method is more robust and faster in terms of computing a solution, but the resulting control is marginally sub-optimal relative to the control determined by the first method. Accordingly, the relative benefits of each approach are studied to determine the best design for a real-time embedded application. It is shown that the optimal path determined by a simplified model (i.e., a point mass model and energy state approximation) is indeed a singular arc. It is further shown that if the optimal arrival to the singular arch and the optimal departure from the singular arc are added to the control trajectory, the resulting flight path is a close approximation of the optimal control determined by the NLP method. The optimal controls generated by the first method are tested using the higher-order-model and compared to the performance of a typical Flight Management System for a mid-range narrow-body transport. Minimum fuel burn is studied first. The time-related cost to operate the airplane is then included in the cost function to minimize the total Direct Operating Cost. A generic narrow-body transport with modern turbofan engines is the subject of the analyses, but the developed methods are
In this work we solve a higly-nonlinear structural optimization problem for the sandwich panel with external thermal protection layer that can be used in the spacecraft systems. Objective function of the problem is th...
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In this work we solve a higly-nonlinear structural optimization problem for the sandwich panel with external thermal protection layer that can be used in the spacecraft systems. Objective function of the problem is the mass per unit area of the panel. Constraints are formulated based on the simplified analytical solutions of structural mechanics and heat transfer problems, which are suitable for the preliminary design considerations. The set of design variables includes the geometric parameters of the panel and additional microstructural parameter-porosity of the heat protection material. Direct random search and simulated annealing method are applied to solve considered problem. Change of limit states and optimal configurations of the panel are studied for different levels of the mechanical loading.
The purpose of the present paper is to study the global convergence of a practical Augmented Lagrangian model algorithm that considers non-quadratic Penalty-Lagrangian functions. We analyze the convergence of the mode...
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The purpose of the present paper is to study the global convergence of a practical Augmented Lagrangian model algorithm that considers non-quadratic Penalty-Lagrangian functions. We analyze the convergence of the model algorithm to points that satisfy the Karush-Kuhn-Tucker conditions and also the weak second-order necessary optimality condition. The generation scheme of the Penalty-Lagrangian functions includes the exponential penalty function and the logarithmic-barrier without using convex information.
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