A limited-memory BFGS-based differential evolution algorithm for optimal control of nonlinear systems with mixed control variables and probability constraints

Wu, Xiang; Zhang, Kanjian

doi:10.1007/s11075-022-01425-5

Cited by 3 publications

(1 citation statement)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DA involves the trajectory approach, 19 auxiliary function approach, 20 covering approach, 21 etc. Simulated annealing algorithm, 22 genetic algorithm, 23 differential evolution, 24 and particle swarm optimization 25 are several typical algorithms of the PA. Note that most of the optimization problems usually own many local minimizers in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Numerical algorithm for hypersonic vehicle optimal flight control

Zhang

Ding

2023

Measurement and Control

View full text Add to dashboard Cite

We consider a hypersonic vehicle optimal flight control problem. The problem is modeled as an optimal control problem of switched systems (OCPSS), which can become a parameter optimization problem (POP). Following that, to achieve the globally optimal solution of the POP, an improved continuous filled function (CFF) algorithm including one adjusting parameter is proposed based on a penalty function, in which the CFF is differentiable, excludes logarithmic terms or exponential terms, and does not require to minimize the cost function. Numerical results show that the proposed algorithm is effective.

show abstract

Section: Introductionmentioning

confidence: 99%

Numerical algorithm for hypersonic vehicle optimal flight control

Zhang

Ding

2023

Measurement and Control

View full text Add to dashboard Cite

show abstract

The spectral conjugate gradient method in variational adjoint assimilation for model terrain correction III: convergence analysis

Tao,

2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

In the former parts of this paper, the spectral conjugate gradient (SCG) algorithm was proposed to identify the optimal bottom terrain used for predicting the meteorological fields. In this part, the convergence analysis of the SCG method is presented to verify its validity when solving a typical ill-posed inverse problem. The scaled cost function, the scaled gradient norm, the linear convergence rate, and the Hessian approximation condition number are evaluated for convergence analysis. The SCG method reduces the cost functional value and the gradient norm sufficiently. It always performs linear rates of convergence with a quickly reduced condition number of the Hessian matrix. Sometimes it provides very fast linear convergence rates. The SCG code with optimal step size and Andrei’s equation is effective on ill-posed test problems.

show abstract