Adaptive dynamic programming discrete‐time LQR control on electromagnetic levitation system

Adaptive Robust Dynamic Surface Control for a Magnetic Levitation System

This paper considers the position tracking problem of a magnetic levitation system in the presence of uncertainties of physical parameters. The dynamic surface control (DSC) technique is modified and applied to the system under study, to overcome the problem of “explosion of terms” associated with the backstepping design procedure. Input-to-state stability (ISS) property is ensured by the robus...

Electromagnetic Formation Flight Control Using Dynamic Programming

Electromagnetic formation flight (EMFF) is an enabling technology for a number of spacecraft mission architectures. The RINGS program will be the first time EMFF is demonstrated in a microgravity environment. Nonlinearities due to magnetic field interactions preclude linear feedback controllers from being used to control the RINGS system. Approximate dynamic programming is explored in this pape...

Adaptive Inverse Optimal Control of a Magnetic Levitation System

On Approximate Dynamic Programming with Multivariate Splines for Adaptive Control

We define a SDP framework based on the RLS TD algorithm and multivariate simplex B-splines. We introduce a local forget factor capable of preserving the continuity of the simplex splines. This local forget factor is integrated with the RLS TD algorithm, resulting in a modified RLS TD algorithm that is capable of tracking time-varying systems. We present the results of two numerical experiments,...

Solution of the input-constrained LQR problem using dynamic programming

The input-constrained LQR problem is addressed in this paper; i.e., the problem of finding the optimal control law for a linear system such that a quadratic cost functional is minimised over a horizon of length N subject to the satisfaction of input constraints. A global solution (i.e., valid in the entire state space) for this problem, and for arbitrary horizon N, is derived analytically by us...