Two updating schemes of iterative learning control for networked control systems with random data dropouts

Distributed adaptive iterative learning control for nonlinear multiagent systems with state constraints

“…Since the iterative updating laws (30) and (31) are identical to Equations 13 and 14, the differentials of V 2 j,k and V 3 j,k keep the same form to those given in the proof of Theorem 1. As a consequence, there is an additional term appended to Equation A19, which becomesĖ…”

“…After three decades of developments, ILC has shown its distinct advantages in handling with high-nonlinearity, complexity, and high-precision tracking problems. [24][25][26][27][28][29][30][31] Some pioneering works have been reported on ILC for MAS. The first result on this topic was given in the work of Ahn and Chen, 32 where the authors considered the formation control problem.…”

Iterative Learning Control for Nonlinear Systems with Data Dropouts at Both Measurement and Actuator Sides

“…For example, unmanned aerial vehicles (UAVs) can be used for surveillance of some specified area and the surveillance routine is usually repeatable. Several papers have been dedicated to the design and analysis of ILC algorithm under random data dropouts environments [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Another similar example is the trajectory-keeping control in satellite formation flying [9].…”

“…The data dropout is assumed to occur only at the measurement side in [12][13][14][15][16][17][18][19][20][21], that is, only the output data might be lost during the transmission from the plant to the controller, while the network from the controller to the plant is assumed to work well. The inherent principle in these papers is that, if the tracking information is successfully transmitted back to the controller, then the algorithm updates its input signal, otherwise the algorithm stops updating and retains its previous signal.…”

“…The inherent principle in these papers is that, if the tracking information is successfully transmitted back to the controller, then the algorithm updates its input signal, otherwise the algorithm stops updating and retains its previous signal. The convergence was established in the mean square sense [12][13][14], mathematical expectation sense [10,[15][16][17], and almost sure sense [18][19][20][21]. Additionally, in [17,20,21], the authors also provided an alternative compensation scheme for the lost measurement, i.e., substituting the dropped packet with the synchronous one from its previous iteration.…”