The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Jun Wang - One of the best experts on this subject based on the ideXlab platform.
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Sequential Convex Programming for nonlinear optimal control problem in UAV path planning
2017 American Control Conference (ACC), 2017Co-Authors: Zhe Zhang, Jianxun Li, Jun WangAbstract:Usually, a UAV (unmanned aerial vehicle) path-planning problem is abstracted into a nonlinear optimal control model, i.e. a nonlinear Programming problem. However, this kind of problem is always difficult to obtain stable solutions quickly. In this paper, a sequential Convex Programming whose cost function and inequality constraints are Convex is proposed to approximate the nonlinear Programming to obtain the solution. Under mild condition, the solution sequence generated by the sequential Convex Programming is convergent to the KKT(Karush-Kuhn-Tucker) point of the origin problem, which has been verified by a rigorous theoretical proof in this paper. Thus, a nonlinear Programming can be solved by a series of sequential Convex Programming. The effectiveness of the proposed method is verified by a UAV path-planning application.
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A one-layer recurrent neural network for Convex Programming
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008Co-Authors: Jun WangAbstract:This paper presents a one-layer recurrent neural network for solving Convex Programming problems subject to linear equality and nonnegativity constraints. The number of neurons in the neural network is equal to that of decision variables in the optimization problem. Compared with the existing neural networks for optimization, the proposed neural network has lower model complexity. Moreover, the proposed neural network is proved to be globally convergent to the optimal solution(s) under some mild conditions. Simulation results show the effectiveness and performance of the proposed neural network.
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A recurrent neural network for nonlinear Convex Programming
Proceedings of the 2003 International Symposium on Circuits and Systems 2003. ISCAS '03., 2003Co-Authors: Jun WangAbstract:This paper presents a novel recurrent neural network for nonlinear Convex Programming. Under the condition that the objective function is Convex and the constraint set is strictly Convex or that the objective function is strictly Convex and the constraint set is Convex, the proposed neural network is proved to be stable in the sense of Lyapunov and globally convergent to an exact solution. Compared with the existing neural networks for solving such nonlinear optimization problems, the proposed neural network does not require an additional condition on the objective function and has a simple structure for implementation. Simulation results are given to illustrate further the global convergence and performance of the proposed neural network for constrained nonlinear optimization.
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A deterministic annealing neural network for Convex Programming
Neural Networks, 1994Co-Authors: Jun WangAbstract:Abstract A recurrent neural network, called a deterministic annealing neural network, is proposed for solving Convex Programming problems. The proposed deterministic annealing neural network is shown to be capable of generating optimal solutions to Convex Programming problems. The conditions for asymptotic stability, solution feasibility, and solution optimality are derived. The design methodology for determining design parameters is discussed. Three detailed illustrative examples are also presented to demonstrate the functional and operational characteristics of the deterministic annealing neural network in solving linear and quadratic programs.
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A time-varying recurrent neural system for Convex Programming
IJCNN-91-Seattle International Joint Conference on Neural Networks, 1991Co-Authors: Jun WangAbstract:The asymptotic stability of a recurrent neural network with monotonically time-varying penalty parameter for optimization is theoretically justified. The conditions of feasibility of solutions generated by the recurrent neural networks are characterized. The conditions of optimality of solutions to Convex Programming problems generated by the recurrent neural networks are characterized. The design methodology of the operating characteristics of the recurrent neural networks are presented by illustrative examples.
Fred Y Hadaegh - One of the best experts on this subject based on the ideXlab platform.
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model predictive control of swarms of spacecraft using sequential Convex Programming
Journal of Guidance Control and Dynamics, 2014Co-Authors: Daniel Morgan, Soonjo Chung, Fred Y HadaeghAbstract:DOI: 10.2514/1.G000218 This paper presents a decentralized, model predictive control algorithm for the optimal guidance and reconfiguration of swarms of spacecraft composed of hundreds to thousands of agents with limited capabilities. In previous work, J2-invariantorbitshavebeenfoundtoprovidecollision-freemotionforhundredsoforbitsinalowEarthorbit. This paper develops real-time optimal control algorithms for the swarm reconfiguration that involve transferring from one J2-invariant orbit to another while avoidingcollisions and minimizing fuel. The proposedmodel predictive control-sequential Convex Programming algorithm uses sequential Convex Programming to solve a series of approximate path planning problems until the solution converges. By updating the optimal trajectories during the reconfiguration, the model predictive control algorithm results in decentralized computations and communication between neighboring spacecraft only. Additionally, model predictive control reduces the horizon of the Convex optimizations, which reduces the run time of the algorithm. Multiple time steps, time-varying collision constraints, and communication requirements are developed to guarantee stability, feasibility, and robustness of the model predictive control-sequential Convex Programming algorithm.
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Probabilistic guidance of distributed systems using sequential Convex Programming
2014 IEEE RSJ International Conference on Intelligent Robots and Systems, 2014Co-Authors: Daniel Morgan, Soonjo Chung, Giri Prashanth Subramanian, Saptarshi Bandyopadhyay, Fred Y HadaeghAbstract:In this paper, we integrate, implement, and validate formation flying algorithms for a large number of agents using probabilistic guidance of distributed systems with inhomogeneous Markov chains and model predictive control with sequential Convex Programming. Using an inhomogeneous Markov chain, each agent determines its target position during each iteration in a statistically independent manner while the distributed system converges to the desired formation. Moreover, the distributed system is robust to external disturbances or damages to the formation. Once the target positions are assigned, an optimal control problem is formulated to ensure that the agents reach the target positions while avoiding collisions. This problem is solved using sequential Convex Programming to determine optimal, collision-free trajectories and model predictive control is implemented to update these trajectories as new state information becomes available. Finally, we validate the probabilistic guidance of distributed systems and model predictive control algorithms using the formation flying testbed.
Moritz Diehl - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Sequential Convex Programming for Optimal Control Applications
arXiv: Optimization and Control, 2011Co-Authors: Tran Dinh Quoc, Carlo Savorgnan, Moritz DiehlAbstract:This paper proposes real-time sequential Convex Programming (RTSCP), a method for solving a sequence of nonlinear optimization problems depending on an online parameter. We provide a contraction estimate for the proposed method and, as a byproduct, a new proof of the local convergence of sequential Convex Programming. The approach is illustrated by an example where RTSCP is applied to nonlinear model predictive control.
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local convergence of sequential Convex Programming for nonConvex optimization
2010Co-Authors: Quoc Tran Dinh, Moritz DiehlAbstract:This paper introduces sequential Convex Programming (SCP), a local optimzation method for solving nonConvex optimization problems. A full-step SCP algorithm is presented. Under mild conditions the local convergence of the algorithm is proved as a main result of this paper. An application to optimal control illustrates the performance of the proposed algorithm.
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Distributed nonlinear optimal control using sequential Convex Programming and smoothing techniques
Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009Co-Authors: Ion Necoara, Carlo Savorgnan, Dinh Quoc Tran, Johan Suykens, Moritz DiehlAbstract:We regard a network of coupled nonlinear dynamical systems that we want to control optimally. The cost function is assumed to be separable and Convex. The algorithm we propose to address the numerical solution of this problem is based on two ingredients: first, we exploit the Convex problem structure using a sequential Convex Programming framework that linearizes the nonlinear dynamics in each iteration. Second, we use distributed dual decomposition methods to address the decomposable Convex subproblems, that allow efficient parallel implementation. We analyze the convergence of the algorithm towards a local solution.
Hongmei Xu - One of the best experts on this subject based on the ideXlab platform.
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Topology Optimization of Compliant Mechanisms Using Sequential Convex Programming
2006 IEEE RSJ International Conference on Intelligent Robots and Systems, 2006Co-Authors: Hua Ying, Hongmei XuAbstract:This paper has presented a novel multi-criteria formulation for the topology optimization design of compliant mechanisms by using sequential Convex Programming approach. Both structural strain energy and mechanical efficiency are shaped together through compromise Programming method to develop a new combined formulation to meet the optimal design according to the demands of minimizing structural compliance and maximizing mechanical function specification. The artificial spring model is resorted to reveal the relationship between the work-piece and the compliant mechanism, and the dummy load method is used to further engrave the formulation pattern by superposition of two load cases. Displacement limitation and material usage are imposed as external constraints to narrow the design domain. A sequential Convex Programming using the approximations of the method of moving asymptotes (MMA) is applied to solve the optimization problem. SIMP interpolation scheme is used to indicate the dependence between the elastic modulus upon regularized element densities. Numerical examples have been applied to demonstrate the validation of the proposed methods
Daniel Morgan - One of the best experts on this subject based on the ideXlab platform.
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model predictive control of swarms of spacecraft using sequential Convex Programming
Journal of Guidance Control and Dynamics, 2014Co-Authors: Daniel Morgan, Soonjo Chung, Fred Y HadaeghAbstract:DOI: 10.2514/1.G000218 This paper presents a decentralized, model predictive control algorithm for the optimal guidance and reconfiguration of swarms of spacecraft composed of hundreds to thousands of agents with limited capabilities. In previous work, J2-invariantorbitshavebeenfoundtoprovidecollision-freemotionforhundredsoforbitsinalowEarthorbit. This paper develops real-time optimal control algorithms for the swarm reconfiguration that involve transferring from one J2-invariant orbit to another while avoidingcollisions and minimizing fuel. The proposedmodel predictive control-sequential Convex Programming algorithm uses sequential Convex Programming to solve a series of approximate path planning problems until the solution converges. By updating the optimal trajectories during the reconfiguration, the model predictive control algorithm results in decentralized computations and communication between neighboring spacecraft only. Additionally, model predictive control reduces the horizon of the Convex optimizations, which reduces the run time of the algorithm. Multiple time steps, time-varying collision constraints, and communication requirements are developed to guarantee stability, feasibility, and robustness of the model predictive control-sequential Convex Programming algorithm.
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Probabilistic guidance of distributed systems using sequential Convex Programming
2014 IEEE RSJ International Conference on Intelligent Robots and Systems, 2014Co-Authors: Daniel Morgan, Soonjo Chung, Giri Prashanth Subramanian, Saptarshi Bandyopadhyay, Fred Y HadaeghAbstract:In this paper, we integrate, implement, and validate formation flying algorithms for a large number of agents using probabilistic guidance of distributed systems with inhomogeneous Markov chains and model predictive control with sequential Convex Programming. Using an inhomogeneous Markov chain, each agent determines its target position during each iteration in a statistically independent manner while the distributed system converges to the desired formation. Moreover, the distributed system is robust to external disturbances or damages to the formation. Once the target positions are assigned, an optimal control problem is formulated to ensure that the agents reach the target positions while avoiding collisions. This problem is solved using sequential Convex Programming to determine optimal, collision-free trajectories and model predictive control is implemented to update these trajectories as new state information becomes available. Finally, we validate the probabilistic guidance of distributed systems and model predictive control algorithms using the formation flying testbed.
