The Experts below are selected from a list of 89358 Experts worldwide ranked by ideXlab platform
Naeem Faraz - One of the best experts on this subject based on the ideXlab platform.
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study of the effects of the reynolds number on circular porous slider via variational Iteration Algorithm ii
Computers & Mathematics With Applications, 2011Co-Authors: Naeem FarazAbstract:Abstract In this paper the problem of the porous slider where the fluid is injected through the porous bottom is considered. The similarity transformation reduces the governing equations into coupled nonlinear ordinary differential equations. The resulting equations are solved by using He’s variational Iteration Algorithm-II. The resulting series solution contains the well known Reynolds number. The influence of the Reynolds number on the velocity field has been discussed graphically.
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analytical approach to two dimensional viscous flow with a shrinking sheet via variational Iteration Algorithm ii
Journal of King Saud University - Science, 2011Co-Authors: Naeem Faraz, Yasir Khan, Ahmet YildirimAbstract:The purpose of this paper is to employ an analytical approach to a two-dimensional viscous flow with a shrinking sheet. A comparative study of the variational Iteration Algorithm-II (VIM-II) and the Adomian decomposition method (ADM) are discussed. Both approaches have been applied to obtain the solution of a two-dimensional viscous flow due to a shrinking sheet. This study outlines the significant features of the two methods. Comparison is made with the ADM to highlight the significant features of the VIM-II and its capability of handling completely integrable equations. Through careful investigation of the Iteration formulas of the earlier variational Iteration Algorithm (VIM), we find unnecessary repeated calculations in each Iteration. To overcome this shortcoming, we suggest the VIM-II, which has advantages over other Iteration formulas, such as the VIM, and the ADM. Further Iterations can produce more accurate results and decrease the error.
Ding Wang - One of the best experts on this subject based on the ideXlab platform.
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Neural-network-based robust optimal control of uncertain nonlinear systems using model-free policy Iteration Algorithm
2016 International Joint Conference on Neural Networks (IJCNN), 2016Co-Authors: Chao Li, Ding WangAbstract:In this paper, we establish a robust optimal control law for a class of continuous-time uncertain nonlinear systems by using a neural-network-based model-free policy Iteration approach. The robust control law of the original uncertain nonlinear system is derived by adding a feedback gain to the optimal control law of the nominal system. It is proven that this robust control law can achieve optimality under a specified cost function. Then, the neural-network-based model-free policy Iteration Algorithm is developed to solve the Hamilton-Jacobi-Bellman equation corresponding to the nominal system without system dynamics. The actor-critic technique and the least squares implementation method are used to obtain the optimal control policy of the nominal system. A numerical simulation is given to verify the applicability of the present robust optimal control scheme.
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policy Iteration Algorithm for online design of robust control for a class of continuous time nonlinear systems
IEEE Transactions on Automation Science and Engineering, 2014Co-Authors: Ding Wang, Derong Liu, Hongliang LiAbstract:In this paper, a novel strategy is established to design the robust controller for a class of continuous-time nonlinear systems with uncertainties based on the online policy Iteration Algorithm. The robust control problem is transformed into the optimal control problem by properly choosing a cost function that reflects the uncertainties, regulation, and control. An online policy Iteration Algorithm is presented to solve the Hamilton-Jacobi-Bellman (HJB) equation by constructing a critic neural network. The approximate expression of the optimal control policy can be derived directly. The closed-loop system is proved to possess the uniform ultimate boundedness. The equivalence of the neural-network-based HJB solution of the optimal control problem and the solution of the robust control problem is established as well. Two simulation examples are provided to verify the effectiveness of the present robust control scheme.
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Neuro-Optimal Control for Discrete Stochastic Processes via a Novel Policy Iteration Algorithm
IEEE Transactions on Systems Man and Cybernetics: Systems, 1Co-Authors: Mingming Liang, Ding WangAbstract:In this paper, a novel policy Iteration adaptive dynamic programming (ADP) Algorithm is presented which is called ``local policy Iteration ADP Algorithm'' to obtain the optimal control for discrete stochastic processes. In the proposed local policy Iteration ADP Algorithm, the iterative decision rules are updated in a local space of the whole state space. Hence, we can significantly reduce the computational burden for the CPU in comparison with the conventional policy Iteration Algorithm. By analyzing the convergence properties of the proposed Algorithm, it is shown that the iterative value functions are monotonically nonincreasing. Besides, the iterative value functions can converge to the optimum in a local policy space. In addition, this local policy space will be described in detail for the first time. Under a few weak constraints, it is also shown that the iterative value function will converge to the optimal performance index function of the global policy space. Finally, a simulation example is presented to validate the effectiveness of the developed method.
Ahmet Yildirim - One of the best experts on this subject based on the ideXlab platform.
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analytical approach to two dimensional viscous flow with a shrinking sheet via variational Iteration Algorithm ii
Journal of King Saud University - Science, 2011Co-Authors: Naeem Faraz, Yasir Khan, Ahmet YildirimAbstract:The purpose of this paper is to employ an analytical approach to a two-dimensional viscous flow with a shrinking sheet. A comparative study of the variational Iteration Algorithm-II (VIM-II) and the Adomian decomposition method (ADM) are discussed. Both approaches have been applied to obtain the solution of a two-dimensional viscous flow due to a shrinking sheet. This study outlines the significant features of the two methods. Comparison is made with the ADM to highlight the significant features of the VIM-II and its capability of handling completely integrable equations. Through careful investigation of the Iteration formulas of the earlier variational Iteration Algorithm (VIM), we find unnecessary repeated calculations in each Iteration. To overcome this shortcoming, we suggest the VIM-II, which has advantages over other Iteration formulas, such as the VIM, and the ADM. Further Iterations can produce more accurate results and decrease the error.
G. Maggetto - One of the best experts on this subject based on the ideXlab platform.
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Innovative Iteration Algorithm for a vehicle simulation program
IEEE Transactions on Vehicular Technology, 2004Co-Authors: J. Van Mierlo, G. MaggettoAbstract:Resulting from Ph.D. research, a vehicle simulation program is proposed and continuously developed, which allows simulation of the behavior of electric, hybrid, fuel cell, and internal combustion vehicles while driving any reference cycle. The goal of the simulation program is to study power flows in the drivetrains of vehicles and the corresponding component losses, as well as to compare different drivetrain topologies. This comparison can be realized for energy consumption and emissions, as well as for performance (acceleration, range, maximum slope, etc.). The core of this program, consisting of a unique Iteration Algorithm, will be highlighted in this paper. This Algorithm not only allows the calculation of the limits of vehicle acceleration in the function of drivetrain component characteristics, but at the same time is able to develop and evaluate the different power-management strategies of hybrid vehicles, combining combustion engines and electric motors. Furthermore, the comprehensive Iteration Algorithm is demonstrated to be very efficient in handling any type of working limit for all components in different types of drivetrains, which results in an accurate and modular vehicle simulation program with high data flexibility.
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technical note vehicle simulation program a tool to evaluate hybrid power management strategies based on an innovative Iteration Algorithm
Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering, 2001Co-Authors: J. Van Mierlo, G. MaggettoAbstract:AbstractIn the framework of a PhD research programme [1] a vehicle simulation program is developed. It is a modular user-friendly interactive programme that allows the simulation of the behaviour of electric (battery, hybrid and fuel cell) as well as internal combustion (petrol, diesel, CNG, etc.) vehicles. The goal of the simulation program is to study power flows in drivetrains and corresponding component losses as well as to compare different drivetrain topologies. This comparison can be realized at the level of consumption (fuel and electricity) and emissions (CO2, HC, NOx, CO, particles, etc.) as well as at the level of performance (acceleration, range, maximum slope). The main modelling aspects and research ‘trigger points’, including its innovative Iteration Algorithm, will be highlighted in this paper.
Zhengtao Ding - One of the best experts on this subject based on the ideXlab platform.
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Data-driven policy Iteration Algorithm for optimal control of continuous-time Itô stochastic systems with Markovian jumps
IET Control Theory & Applications, 2016Co-Authors: Jun Song, Shuping He, Zhengtao DingAbstract:This studies the infinite horizon optimal control problem for a class of continuous-time systems subjected to multiplicative noises and Markovian jumps by using a data-driven policy Iteration Algorithm. The optimal control problem is equivalent to solve a stochastic coupled algebraic Riccatic equation (CARE). An off-line Iteration Algorithm is first established to converge the solutions of the stochastic CARE, which is generalised from an implicit iterative Algorithm. By applying subsystems transformation (ST) technique, the off-line iterative Algorithm is decoupled into N parallel Kleinman's iterative equations. To learn the solution of the stochastic CARE from N decomposed linear subsystems data, an ST-based data-driven policy Iteration Algorithm is proposed and the convergence is proved. Finally, a numerical example is given to illustrate the effectiveness and applicability of the proposed two iterative Algorithms.
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Online adaptive optimal control for continuous-time Markov jump linear systems using a novel policy Iteration Algorithm
IET Control Theory & Applications, 2015Co-Authors: Shuping He, Jun Song, Zhengtao DingAbstract:This study studies the online adaptive optimal control problems for a class of continuous-time Markov jump linear systems (MJLSs) based on a novel policy Iteration Algorithm. By utilising a new decoupling technique named subsystems transformation, the authors re-construct the MJLSs and a set of new coupled systems composed of N subsystems are obtained. The online policy Iteration Algorithm was used to solve the coupled algebraic matrix Riccati equations with partial knowledge regarding to the system dynamics, and the relevant optimal controllers equivalent to the investigated MJLSs are designed. Moreover, the convergence of the novel policy Iteration Algorithm is also established. Finally, a simulation example is given to illustrate the effectiveness and applicability of the proposed approach.
