In this paper, a Newton-conjugate gradient （CG） augmented Lagrangian method is proposed for solving the path constrained dynamic process optimization problems. The path constraints are simplified as a single final t...In this paper, a Newton-conjugate gradient （CG） augmented Lagrangian method is proposed for solving the path constrained dynamic process optimization problems. The path constraints are simplified as a single final time constraint by using a novel constraint aggregation function. Then, a control vector parameterization （CVP） approach is applied to convert the constraints simplified dynamic optimization problem into a nonlinear programming （NLP） problem with inequality constraints. By constructing an augmented Lagrangian function, the inequality constraints are introduced into the augmented objective function, and a box constrained NLP problem is generated. Then, a linear search Newton-CG approach, also known as truncated Newton （TN） approach, is applied to solve the problem. By constructing the Hamiltonian functions of objective and constraint functions, two adjoint systems are generated to calculate the gradients which are needed in the process of NLP solution. Simulation examlales demonstrate the effectiveness of the algorithm.展开更多
基金supported by the Natural Science Foundation of China (No. 60974039)the National Science and Technology Major Project (No.2008ZX05011)
文摘In this paper, a Newton-conjugate gradient （CG） augmented Lagrangian method is proposed for solving the path constrained dynamic process optimization problems. The path constraints are simplified as a single final time constraint by using a novel constraint aggregation function. Then, a control vector parameterization （CVP） approach is applied to convert the constraints simplified dynamic optimization problem into a nonlinear programming （NLP） problem with inequality constraints. By constructing an augmented Lagrangian function, the inequality constraints are introduced into the augmented objective function, and a box constrained NLP problem is generated. Then, a linear search Newton-CG approach, also known as truncated Newton （TN） approach, is applied to solve the problem. By constructing the Hamiltonian functions of objective and constraint functions, two adjoint systems are generated to calculate the gradients which are needed in the process of NLP solution. Simulation examlales demonstrate the effectiveness of the algorithm.
