Abstract We present a new proof of the turnpike property for nonlinear optimal control problems, when running target is steady control-state pair underlying system. Our strategy combines construction quasi-turnpike controls via controllability, and bootstrap argument, does not rely on analyzing optimality system or linearization techniques. This in turn allows us to address several problems fin...

Context This work is in the line of research with the following issue: how to develop new convex optimization techniques based on semidefinite programming (SDP) and real algebraic geometry to solve optimal control problems (OCP) in a nonlinear setting. Recently, several research efforts allowed to solve numerically certain optimal control problems with polynomial data. The general idea is to re...

This paper presents a new method for solving a class of infinite horizon nonlinear optimal control problems. In this method, first the original optimal control problem is transformed into a nonlinear two-point boundary value problem (TPBVP) via the Pontryagin’s maximum principle. Then, using the homotopy perturbation method and introducing a convex homotopy in topologic space, the nonlinear TPB...

In this paper, we are going to consider a nonlinear optimal control problem (NOC). First we change the (NOC) problem to an optimal differential inclusion problem (ODI), then by defining new control variables, (ODI) problem is converted to an optimal control problem where it is linear in term of control variable and we determine the approximation of this control problem, then by using measure th...

the linear multiobjective transportation problem is a special type of vector minimum problem in which constraints are all equality type and the objectives are conicting in nature. this paper presents an application of fuzzy goal programming to the linear multiobjective transportation problem. in this paper, we use a special type of nonlinear (hyperbolic and exponential) membership functions to ...

The non-convex behavior presented by nonlinear systems limits the application of classical optimization techniques to solve optimal control problems for these kinds of systems. This paper proposes a hybrid algorithm, namely BA-SD, by combining Bee algorithm (BA) with steepest descent (SD) method for numerically solving nonlinear optimal control (NOC) problems. The proposed algorithm includes th...

Abstrucf-We formulate and solve a dynamic stochastic optimization problem of a nonstandard type, whose optimal solution features active learning. The proof of optimality and the derivation of the corresponding control policies is an indirect one, which relates the original single-person optimization problem to a sequence of nested zero-sum stochastic games. Existence of saddle points for these ...

This paper considers the in"nite horizon nonlinear quadratic optimal control problem for a singularly perturbed system which is nonlinear in both, the slow and the fast variables. The relationship between this problem and the analogous one for a descriptor system is investigated. Parameter-independent controllers are constructed that solve the problem for the descriptor system and lead the full...

The paper proposes two Nonlinear Model Predictive Control schemes that uncover a synergistic relationship between on-line receding horizon style computation and Particle Swarm Optimization, thus benefiting from both the performance advantages of on-line computation and the desirable properties of Particle Swarm Optimization. After developing these techniques for the unconstrained nonlinear opti...