Stratified Sampling is one of the most widely used sampling techniques as it increases the precision of the estimate of the survey variable. On the other hand, calibration estimation is a method of adjusting the origi...
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ISBN:
(纸本)9781479919550
Stratified Sampling is one of the most widely used sampling techniques as it increases the precision of the estimate of the survey variable. On the other hand, calibration estimation is a method of adjusting the original design weights to improve survey estimates by using auxiliary information such as the known population total ( or mean) of the auxiliary variables. A calibration estimator uses calibrated weights that are determined to minimize a given distance measure to the original design weights while satisfying a set of constraints related to the auxiliary information. In this paper, a new calibration estimator of population mean in stratified sampling design is proposed, which incorporates not only the population mean but also the variance stratified mean available for the auxiliary variable. The problem of determining the optimum calibrated weights is formulated as a nonlinear programming problem (NLPP) that is solved using Lagrange multiplier technique. The computational details of the procedure are illustrated in the presence of one auxiliary variable. A numerical example is presented and a simulation study is carried out to illustrate the computational details and the performance of the proposed calibration estimator. The results reveal that the proposed calibration estimator is more efficient than the other calibration estimators of the population mean.
Optimizing the configuration and overall performance of synchronized modular systems is considered in this paper. The synchronized modules can be considered as a hybrid system, including continuous-time dynamics of lo...
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ISBN:
(纸本)9781479948451
Optimizing the configuration and overall performance of synchronized modular systems is considered in this paper. The synchronized modules can be considered as a hybrid system, including continuous-time dynamics of local moving devices, combined with high-level discrete event sequences. The continuous-time trajectories are approximated by the Gauss pseudospectral method, resulting in a nonlinearprogramming (NLP) problem. The optimal configuration generates the maximal production rate subject to dynamic constraints. A complete design procedure is presented and applied to a case study of a packaging machine, where an alternative optimal configuration is achieved compared to current industrial practices.
In order to improve smoothness of task trajectories,this paper describe algorithms to generate smooth-optimal trajectories based on the differential flatness of quadrotor.B-spline is used to parameterize the selected ...
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ISBN:
(纸本)9781479946983
In order to improve smoothness of task trajectories,this paper describe algorithms to generate smooth-optimal trajectories based on the differential flatness of quadrotor.B-spline is used to parameterize the selected flat outputs,and the dynamic constraints or kinematical constraints on quadrotor are transferred to the control point constraints of *** trajectory generation is then considered an optimal control problem by adopting the integral of squared *** then the optimal solution is solved by transforming the optimal control problem into a nonlinearprogramming *** last the smoothness-optimal trajectories are *** results show that,this method can smooth out movements and minimize the accumulative jerk which can decrease trajectory tracking errors effectively.
the time-optimal attitude maneuver problem of a rigid spacecraft with only two control torques along its principal axes is studied using Radau Pseudospectral Method (RPM).The asymmetric and symmetric spacecraft have b...
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ISBN:
(纸本)9781479946983
the time-optimal attitude maneuver problem of a rigid spacecraft with only two control torques along its principal axes is studied using Radau Pseudospectral Method (RPM).The asymmetric and symmetric spacecraft have been considered in ***,we establish the attitude dynamical models of asymmetric and symmetric spacecraft with two controls,and use (w,z) parameter to describe the attitude ***,according to the general optimal control problem like Bloza form,the maneuver time minimum as the performance index is selected to be optimized,simultaneously considered the control inputs and angular velocities boundary in the practical application of ***,the optimal maneuver problem is discretized based on the Radau Pseudospectral Method and then converted into a nonlinearprogramming *** for asymmetric and symmetric system are carried out based on GPOPS toolbox and the analysis results illustrate the feasibility the proposed algorithm.
The performance comparison is performed among Legendre (LPM),Gauss (GPM),and Radau (RPM) pseudospectral methods,which are utilized to optimize the low-thrust Earth-Mars rendezvous *** order to provide a fair compariso...
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ISBN:
(纸本)9781479946983
The performance comparison is performed among Legendre (LPM),Gauss (GPM),and Radau (RPM) pseudospectral methods,which are utilized to optimize the low-thrust Earth-Mars rendezvous *** order to provide a fair comparison condition,a shape-based approach is implemented to provide equivalent initial guesses for all the next accurate optimization,and the SQP algorithm is used to solve the resulting nonlinear programming problem for each of the *** behavior and performance of the three pseudospectral methods are compared on the mission of Earth-Mars Rendezvous in 2013.
This work deals with the design of robust fixed-structure controllers for uncertain systems based on a finite set of measured data. This set of measurements is given in the frequency domain. In the current control des...
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ISBN:
(纸本)9781479901784
This work deals with the design of robust fixed-structure controllers for uncertain systems based on a finite set of measured data. This set of measurements is given in the frequency domain. In the current control design approaches, controllers are usually designed based on plant models obtained on the basis of measured data. However, due to various forms of uncertainties such as: plant parameter variations, external disturbances, measurement noise, etc, such models are unable to perfectly describe the behavior of the physical system. Hence, degradation in the controller performance is expected due to such uncertainties and errors associated with the identification process. For that, we propose a new control technique that uses the uncertain measurements to directly design robust controllers, for a class of uncertainties, without going through the use of identification process. In such a proposed design method, interval techniques are introduced to bound plant uncertainties. Its main principle is to find the set of admissible values of the controller parameters so that the family of all possible frequency responses of the closed-loop system lies between an upper and lower bounds of a desired frequency response. This problem is formulated as a nonlinear programming problem which can easily be solved to characterize the solution set of the controller parameters. The main feature of our proposed approach is that it enables to design robust fixed-structure controllers by taking into account the plant uncertainties. Moreover, since no mathematical model is needed in the controller synthesis, the design process does not depend on the increasing order and complexity of the system. A simulation example is presented to illustrate and validate the efficacy of the proposed method.
In this paper, a nonlinear model predictive controller is presented for idle speed control (ISC) problem of spark ignition (SI) engine. The objective is to maintain the engine speed at a prescribed set-point through a...
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In this paper, a nonlinear model predictive controller is presented for idle speed control (ISC) problem of spark ignition (SI) engine. The objective is to maintain the engine speed at a prescribed set-point through actuating a electronic throttle, and minimize the effects of load torque disturbances and model uncertainties. The nonlinearprogramming (NLP) problem formed by nonlinear model predictive control (NMPC) is solved by using particle swarm optimization (PSO) algorithm. Simulation results show that the designed nonlinear model predictive controller can achieve satisfactory performance for ISC.
This work deals with the design of robust fixed-structure controllers for uncertain systems based on a finite set of measured data. This set of measurements is given in the frequency domain. In the current control des...
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ISBN:
(纸本)9781479901777
This work deals with the design of robust fixed-structure controllers for uncertain systems based on a finite set of measured data. This set of measurements is given in the frequency domain. In the current control design approaches, controllers are usually designed based on plant models obtained on the basis of measured data. However, due to various forms of uncertainties such as: plant parameter variations, external disturbances, measurement noise, etc, such models are unable to perfectly describe the behavior of the physical system. Hence, degradation in the controller performance is expected due to such uncertainties and errors associated with the identification process. For that, we propose a new control technique that uses the uncertain measurements to directly design robust controllers, for a class of uncertainties, without going through the use of identification process. In such a proposed design method, interval techniques are introduced to bound plant uncertainties. Its main principle is to find the set of admissible values of the controller parameters so that the family of all possible frequency responses of the closed-loop system lies between an upper and lower bounds of a desired frequency response. This problem is formulated as a nonlinear programming problem which can easily be solved to characterize the solution set of the controller parameters. The main feature of our proposed approach is that it enables to design robust fixed-structure controllers by taking into account the plant uncertainties. Moreover, since no mathematical model is needed in the controller synthesis, the design process does not depend on the increasing order and complexity of the system. A simulation example is presented to illustrate and validate the efficacy of the proposed method.
The problem of planning flight trajectories is studied for multiple unmanned combat aerial vehicles(UCAVs)performing a cooperated air-to-ground target attack(CA/GTA)*** constraints including individual and cooperative...
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The problem of planning flight trajectories is studied for multiple unmanned combat aerial vehicles(UCAVs)performing a cooperated air-to-ground target attack(CA/GTA)*** constraints including individual and cooperative constraints are modeled,and an objective function is ***,the cooperative trajectory planning problem is formulated as a cooperative trajectory optimal control problem(CTP-OCP).Moreover,in order to handle the temporal constraints,a notion of the virtual time based strategy is ***,a planning algorithm based on the differential flatness theory and B-spline curves is developed to solve the ***,the proposed approach is demonstrated using a typical CA/GTA mission scenario,and the simulation results show that the proposed approach is feasible and effective.
In a multivariate stratified sampling more than one characteristic are defined on every unit of the population. An optimum allocation which is optimum for one characteristic will generally be far from optimum for othe...
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In a multivariate stratified sampling more than one characteristic are defined on every unit of the population. An optimum allocation which is optimum for one characteristic will generally be far from optimum for others. A compromise criterion is needed to work out a usable allocation which is optimum, in some sense, for all the characteristics. When auxiliary information is also available the precision of the estimates of the parameters can be increased by using it. Furthermore, if the travel cost within the strata to approach the units selected in the sample is significant the cost function remains no more linear. In this paper an attempt has been made to obtain a compromise allocation based on minimization of individual coefficients of variation of the estimates of various characteristics, using auxiliary information and a nonlinear cost function with fixed budget. A new compromise criterion is suggested. The problem is formulated as a multiobjective all integer nonlinear programming problem. A solution procedure is also developed using goal programming technique.
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