Near optimal tracking control of a class of non‐linear systems and an experimental comparison

Abstract

In this study, near optimal tracking of a class of non-linear systems is addressed. Adaptive (approximate) dynamic programming (ADP) approach is used to calculate the optimal control in closed form. ADP has been widely used to resolve optimal regulation and tracking problems of non-linear control systems. Despite advances in the so called supervised and unsupervised ADP techniques for optimal tracking, they have a main draw back. That is, the optimal controller needs to be recalculated for every particular reference trajectory. The main goal of this work is to address this issue for a class of non-linear systems. Finally, this approach is applied on a Delta robot and the performance of the method is analysed experimentally.

Data‐Driven Optimal Tracking Control of Nonlinear Affine Systems Based on Differential Dynamic Programming

“…Step2: Use the test dataset to obtain the DDP expansion ( 19)- (20) [15][16][17][18][19][20][21] deal with infnite-horizon optimal control problems, where the HJB equation reduces to be time-invariant partial diferential equation, i.e., 0 � min u L(e, u) + ∇V T (e, t)(F(e) + G(e)u) 􏼈 􏼉. Te complexity is greatly simplifed.…”

“…Although ADP algorithms have been extensively investigated in the optimal control problem, there are still some limitations. As we all know, the iteration ADP algorithm requires a stable initial policy [10,12], and the Lyapunov-based ADP algorithm requires infnite-horizon index functions [13][14][15][16][17][18][19][20][21][22][23][24], which restricts the practical applications.…”