Control parametrization: a unified approach to optimal control problems with general constraints
Automatica (Journal of IFAC)
Optimal control drug scheduling of cancer chemotherapy
Automatica (Journal of IFAC)
A new computational algorithm for functional inequality constrained optimization problems
Automatica (Journal of IFAC)
Solar Cars and Variational Problems Equivalent to Shortest Paths
SIAM Journal on Control and Optimization
SIAM Journal on Control and Optimization
Journal of Optimization Theory and Applications
Journal of Computational and Applied Mathematics - Special issue on SQP-based direct discretization methods for practical optimal control problems
SIAM Journal on Optimization
Automatica (Journal of IFAC)
A nonsmooth Newton's method for control-state constrained optimal control problems
Mathematics and Computers in Simulation
Optimal control of container cranes
Automatica (Journal of IFAC)
Brief Control parametrization enhancing technique for optimal discrete-valued control problems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Optimal switching control of a fed-batch fermentation process
Journal of Global Optimization
A simulation-and-regression approach for stochastic dynamic programs with endogenous state variables
Computers and Operations Research
Minimizing control variation in nonlinear optimal control
Automatica (Journal of IFAC)
Optimal control of switched systems and its parallel optimization algorithm
Journal of Computational and Applied Mathematics
Hi-index | 22.15 |
We consider an optimal control problem with a nonlinear continuous inequality constraint. Both the state and the control are allowed to appear explicitly in this constraint. By discretizing the control space and applying a novel transformation, a corresponding class of semi-infinite programming problems is derived. A solution of each problem in this class furnishes a suboptimal control for the original problem. Furthermore, we show that such a solution can be computed efficiently using a penalty function method. On the basis of these two ideas, an algorithm that computes a sequence of suboptimal controls for the original problem is proposed. Our main result shows that the cost of these suboptimal controls converges to the minimum cost. For illustration, an example problem is solved.