This paper presents a new trust-region algorithm based on global sequential quadratic programming (SQP) for reactive power optimization. This method is not only reliable and accurate which is similar to SQP, but is al...
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ISBN:
(纸本)0780392302
This paper presents a new trust-region algorithm based on global sequential quadratic programming (SQP) for reactive power optimization. This method is not only reliable and accurate which is similar to SQP, but is also global convergent to trust-region search method. To guarantee feasible region of this sub problem is not null, inaccurate direction component decomposed method is adopted to compute trust-region sub problem. In order to avoid the Marotos Effect, the penalty parameter is effectively regulated in Merit Function. This example of the computation shows that this algorithm has global convergence and is fast, accurate and reliable.
This paper provides a solution to the optimal trajectory planning problem in target localisation for multiple heterogeneous robots with bearing-only sensors. The objective here is to find robot trajectories that maxim...
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ISBN:
(纸本)0780389123
This paper provides a solution to the optimal trajectory planning problem in target localisation for multiple heterogeneous robots with bearing-only sensors. The objective here is to find robot trajectories that maximise the accuracy of the locations of the targets at a prescribed terminal time. The trajectory planning is formulated as an optimal control problem for a nonlinear system with a gradually identified model and then solved using nonlinear Model Predictive Control (MPC). The solution to the MPC optimisation problem is computed through Exhaustive Expansion Tree Search (EETS) plus sequential quadratic programming (SQP). Simulations were conducted using the proposed methods. Results show that EETS alone performs considerably faster than EETS+SQP with only minor differences in information gain, and that a centralised approach outperforms a decentralised one in terms of information gain. We show that a centralised EETS provides a near optimal solution. We also demonstrate the significance of using a matrix to represent the information gathered.
作者:
Biros, GGhattas, ONYU
Courant Inst Math Sci Dept Comp Sci New York NY 10012 USA Carnegie Mellon Univ
Dept Biomed Engn Ultrascale Simulat Lab Pittsburgh PA 15213 USA Carnegie Mellon Univ
Dept Civil & Environm Engn Ultrascale Simulat Lab Pittsburgh PA 15213 USA
Large-scale optimization of systems governed by partial differential equations ( PDEs) is a frontier problem in scientific computation. Reduced quasi-Newton sequential quadratic programming (SQP) methods are state-of-...
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Large-scale optimization of systems governed by partial differential equations ( PDEs) is a frontier problem in scientific computation. Reduced quasi-Newton sequential quadratic programming (SQP) methods are state-of-the-art approaches for such problems. These methods take full advantage of existing PDE solver technology and parallelize well. However, their algorithmic scalability is questionable;for certain problem classes they can be very slow to converge. In this two-part article we propose a new method for steady-state PDE-constrained optimization, based on the idea of using a full space Newton solver combined with an approximate reduced space quasi-Newton SQP preconditioner. The basic components of the method are Newton solution of the first-order optimality conditions that characterize stationarity of the Lagrangian function;Krylov solution of the Karush - Kuhn - Tucker ( KKT) linear systems arising at each Newton iteration using a symmetric quasi-minimum residual method;preconditioning of the KKT system using an approximate state/decision variable decomposition that replaces the forward PDE Jacobians by their own preconditioners, and the decision space Schur complement ( the reduced Hessian) by a BFGS approximation initialized by a two- step stationary method. Accordingly, we term the new method Lagrange - Newton - Krylov - Schur (LNKS). It is fully parallelizable, exploits the structure of available parallel algorithms for the PDE forward problem, and is locally quadratically convergent. In part I of this two- part article, we investigate the effectiveness of the KKT linear system solver. We test our method on two optimal control problems in which the state constraints are described by the steady-state Stokes equations. The objective is to minimize dissipation or the deviation from a given velocity field;the control variables are the boundary velocities. Numerical experiments on up to 256 Cray T3E processors and on an SGI Origin 2000 include scalability and
This paper presents a basic idea for a congestion management system based on an optimal power flow using the sequential quadratic programming method under the consideration of FACTS-devices. The potential for using th...
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This paper presents a basic idea for a congestion management system based on an optimal power flow using the sequential quadratic programming method under the consideration of FACTS-devices. The potential for using this congestion management system in the deregulated market is discussed. An approach for using the balance markets and regulating power markets to resolve congestions is illustrated here. A special focus in the current research is given to the possibility of using FACTS devices included in the OFF as a congestion management system.
A guidance scheme is proposed for orbital motion under continuous outward radial acceleration that is inversely proportional to the square of the radial distance from the sun. Such an acceleration regime would be real...
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A guidance scheme is proposed for orbital motion under continuous outward radial acceleration that is inversely proportional to the square of the radial distance from the sun. Such an acceleration regime would be realized under the minimagnetospheric plasma propulsion. The maximum attainable radial distance of the outbound trajectory is investigated, and a guidance scheme for achieving this target maximum distance is established under radial acceleration disturbances. The scheme not only provides a control law for continuous radial acceleration but also yields the amount and timing of impulsive maneuvers required to satisfy the guidance requirement at the terminal point.
This paper presents a Nonlinear Model Based Predictive Controller (NMBPC) of a buck-boost converter (BBC). The NMBPC uses a nonlinear prediction of the system outputs based on a discretization of the average continuou...
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This paper presents a Nonlinear Model Based Predictive Controller (NMBPC) of a buck-boost converter (BBC). The NMBPC uses a nonlinear prediction of the system outputs based on a discretization of the average continuous model of the BBC, assuming the duty ratio as the control variable. A classical quadratic cost function J is minimized at each sample time using a sequential quadratic programming (SQP) optimization algorithm that guarantees that the obtained control action gives a local optimal value of J . The tuning of the controller parameters is defined to obtain a compromise between performance and robustness. Simulation results in a wide range of the output voltage show that the proposed control strategy yields very fast time responses even under varying load situations.
This paper focuses on the application of stochastic (genetic algorithms, simulated annealing) and deterministic (sequential quadratic programming) optimization methods for the Integrated Design of processes considerin...
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This paper focuses on the application of stochastic (genetic algorithms, simulated annealing) and deterministic (sequential quadratic programming) optimization methods for the Integrated Design of processes considering dynamical non-linear models. Moreover, a hybrid methodology that combines both types of methods is proposed, showing an improvement on performance. Controllability indexes such as disturbance sensitivity gains, the H ∞ norm, and the ISE were considered to obtain optimum disturbance rejection. In order to illustrate and validate our proposal, an activated sludge process with PI schemes is taken. The problem is stated as a multiobjective non-linear optimization problem with non-linear constraints. The application of the mentioned strategies is discussed. The results are encouraging for future application of these techniques to solve synthesis MINLP problems.
A sequential quadratic programming (SQP) algorithm generating feasible iterates is described and analyzed. What distinguishes this algorithm from previous feasible SQP algorithms proposed by various authors is a reduc...
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A sequential quadratic programming (SQP) algorithm generating feasible iterates is described and analyzed. What distinguishes this algorithm from previous feasible SQP algorithms proposed by various authors is a reduction in the amount of computation required to generate a new iterate while the proposed scheme still enjoys the same global and fast local convergence properties. A preliminary implementation has been tested and some promising numerical results are reported.
Free flight is an emerging paradigm in air traffic management. Conflict detection and resolution is the heart of any free-flight concept. The problem of optimal cooperative three-dimensional conflict resolution involv...
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Free flight is an emerging paradigm in air traffic management. Conflict detection and resolution is the heart of any free-flight concept. The problem of optimal cooperative three-dimensional conflict resolution involving multiple aircraft is addressed by the rigorous numerical trajectory optimization methods. The conflict problem is posed as an optimal control problem of finding trajectories that minimize a certain objective function while the safe separation between each aircraft pair is maintained. The initial and final positions of the aircraft are known and aircraft models with detailed nonlinear point-mass dynamics are considered. The protection zone around the aircraft is modeled to be cylindrical in shape. A novel formulation of the cylindrical protection zone is proposed by the use of continuous variables. The optimal control problem is converted to a finite dimensional nonlinear program (NLP) by the use of collocation on finite elements. The NLP is solved by the use of an interior point algorithm that incorporates a novel line search method. A reliable initialization strategy that yields a feasible solution on simple models is also proposed and adapted to detailed models. Several resolution scenarios are illustrated. The practical issue of flyability of the generated trajectories is addressed by the ability of our mathematical programming framework to accommodate detailed dynamic models.
This paper presents a novel and efficient method for solving the economic dispatch problem (EDP), by integrating the particle swarm optimization (PSO) technique with the sequential quadratic programming (SQP) techniqu...
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This paper presents a novel and efficient method for solving the economic dispatch problem (EDP), by integrating the particle swarm optimization (PSO) technique with the sequential quadratic programming (SQP) technique. PSO is the main optimizer and the SQP is used to fine tune for every improvement in the solution of the PSO run. PSO is a derivative free optimization technique which produces results quickly and proves itself fit for solving large-scale complex EDP without considering the nature of the incremental fuel cost function it minimizes. SQP is a nonlinear programming method which starts from a single searching point and finds a solution using the gradient information. The effectiveness of the proposed method is validated by carrying out extensive tests on three different EDP with incremental fuel cost function takes into account the valve-point loadings effects. The proposed method out-performs and provides quality solutions compared to other existing techniques for EDP considering valve-point effects are shown in general. (C) 2004 Elsevier B.V All rights reserved.
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