Control parametrization: a unified approach to optimal control problems with general constraints
Automatica (Journal of IFAC)
A Chebyshev polynomial method for optimal control with state constraints
Automatica (Journal of IFAC)
Global optimization approach to nonlinear optimal control
Journal of Optimization Theory and Applications
Application of stochastic global optimization algorithms to practical problems
Journal of Optimization Theory and Applications
Iterative Dynamic Programming
Deterministic Global Optimization: Theory, Methods and (NONCONVEX OPTIMIZATION AND ITS APPLICATIONS Volume 37) (Nonconvex Optimization and Its Applications)
Deterministic global optimization in isothermal reactor network synthesis
Journal of Global Optimization
A Rigorous Global Optimization Algorithm for Problems with Ordinary Differential Equations
Journal of Global Optimization
Journal of Global Optimization
Global Optimization with Nonlinear Ordinary Differential Equations
Journal of Global Optimization
Numerical solution methods for singular control with multiple state dependent forms
Optimization Methods & Software
Optimal design and operation of a wastewater purification system
Mathematics and Computers in Simulation
A review of recent advances in global optimization
Journal of Global Optimization
Towards global bilevel dynamic optimization
Journal of Global Optimization
Generalized McCormick relaxations
Journal of Global Optimization
Smooth functional tempering for nonlinear differential equation models
Statistics and Computing
Optimal switching control of a fed-batch fermentation process
Journal of Global Optimization
A new genetic algorithm for solving optimization problems
Engineering Applications of Artificial Intelligence
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The accurate solution of optimal control problems is crucial in many areas of engineering and applied science. For systems which are described by a nonlinear set of differential-algebraic equations, these problems have been shown to often contain multiple local minima. Methods exist which attempt to determine the global solution of these formulations. These algorithms are stochastic in nature and can still get trapped in local minima. There is currently no deterministic method which can solve, to global optimality, the nonlinear optimal control problem. In this paper a deterministic global optimization approach based on a branch and bound framework is introduced to address the nonlinear optimal control problem to global optimality. Only mild conditions on the differentiability of the dynamic system are required. The implementa-tion of the approach is discussed and computational studies are presented for four control problems which exhibit multiple local minima.