On iterative learning algorithms for the formation control of nonlinear multi-agent systems

Initial states iterative learning for three-dimensional ballistic endpoint control

“…To achieve the convergence in Z direction, the bounds of D z,v 0 and D z,θ are required. Note that the gradient in (15) or (26) is calculated under the situation that v 0 or θ is fixed. However, in combined learning, v 0 and θ are tuned together, which will lead to the variation of the bounds of D z,v 0 and D z,θ .…”

“…ILC is a technique that uses information from previous trials of the task in the construction of the control input in the next trials with the aim of successively improving tracking accuracy [12][13][14][15]. Note that the problem considered in this paper is a specific kind of ILC problem-ballistic learning control (BLC).The final states are defined according to a spatial quantity instead of a specific time, and the control inputs are only the initial states instead of continuous control profile throughout the duration.…”

A Preconditioning Based Iterative Learning Control for Systems with Unknown Initialization

“…In Figure 7, L = 0.5 and the system is convergent to a very small constant, ∥ e 10 (t) ∥ 2 = 0.0303. In Figure 8, the system is monotonically convergent to a very small constant with better convergence performance, where ∥ e 10 …”

H∞Consensus for Discrete-Time Multiagent Systems

“…In 2003, Jadbabaie et al explained the phenomenon mentioned in [2] by theoretical techniques like undirected graph theory, algebraic theory, and the special properties of stochastic matrices in [3]. The distributed complete consensus problems for continuoustime have been intensively investigated [4][5][6][7][8][9][10][11][12][13][14][15], since the theoretical framework of consensus problems for first-order multiagent networks was proposed and solved by OlfatiSaber and Murray in [16]. They presented the conditions on consensus in terms of graphs for three cases.…”