We solve the linearly constrained linear-quadratic LQ and linear-quadratic-Gaussian LQG optimal control problems. Closed form expressions of the optimal controls are derived, and the Separation Theorem is generalized.

In this paper, we give an analytical approximate solution for non-linear quadratic optimal control problems and optimal control of linear systems using the homotopy perturbation method (HPM). Applying the HPM, the non-linear two-point boundary-value problem (TPBVP) and linear systems, derived from the Pontryagin’s maximum principle, are transformed into a sequence of linear time-invariant TPBVP...

This paper considers solving optimization problem for linear discrete time systems such that closed-loop discrete-time system is positive (i.e., all of its state variables have non-negative values) and also finite-time stable. For this purpose, by considering a quadratic cost function, an optimal controller is designed such that in addition to minimizing the cost function, the positivity proper...

Inverse optimal control is a classical problem of control theory. It was first posed by Kalman in the early sixties. The problem, as addressed in literature, answers to the following two questions: (a) Given system matrices A,B and a gain matrix K, find necessary and sufficient conditions for K to be the optimal of an infinite time LQ problem. (b) Determine all weight matrices Q, R and S which ...

This paper presents a global optimization approach to solving linear non-quadratic optimal control problems. The main work is to construct a differential flow for finding a global minimizer of the Hamiltonian function over a Euclid space. With the Pontryagin principle, the optimal control is characterized by a function of the adjoint variable and is obtained by solving a Hamiltonian differentia...

in this paper, solution of nonlinear optimal control problems and the controlled duffing oscillator, as a special class of optimal control problems, are considered and an efficient algorithm is proposed. this algorithm is based on state parametrization as a polynomial with unknown coefficients. by this method, the control and state variables can be approximated as a function of time. also, the ...