D-type Learning Control Research Articles

D-Type learning control for nonlinear time-varying systems with unknown initial states and inputs

In this paper, D-type iterative learning control schemes, which guarantee that the desired output is tracked precisely through iteration, are presented. Both off-line and on-line schemes are considered for a class of nonlinear systems. In both schemes, the learning gain may be time-varying and thus possesses larger scope for adjustment. Specifically, not only the input but also the initial state is iterated in the algorithms, and the output channel is fully nonlinear. Then, sufficient conditions are presented which guarantee the convergence of the proposed algorithms. The desired input and desired state are not assumed to be known a priori. Finally, the results are applied to a robotic system through simulation, which demonstrates the effectiveness of the method.

Dynamics analysis and learning control for 3-PRPS platform

Employing a mathematical model to represent the dynamics of robot manipulators is a extremely difficult and complicated task. This study presents the dynamic analysis of parallel robot manipulators. The dynamic equations are first formulated in Cartesian space and then transformed into the joint space. Once the dynamic equations are derived, the D-type learning control algorithm is proposed to reduce position-tracking errors. The convergence conditions are also derived to ensure stability of the control systems. Moreover, a PC based, 3-PRPS platform control system is constructed to closely examine the effects of the D-type learning controller.

A discrete-time learning control algorithm for a class of linear time-invariant systems

A discretized version of the D-type learning control algorithm is presented for a MIMO linear discrete-time system. A necessary and sufficient condition for uniform convergence of the proposed learning algorithm is presented. Then, we prove that the same condition is sufficient for the global robustness of the proposed learning algorithm to state disturbances, measurement noise at the output, and reinitialization error are present at each iteration. A numerical example is given to illustrate the results. >