Search

This paper is devoted to the robust finite-time output consensus problems of multi-agent systems under directed graphs, where all agents and their communication topologies are subject to interval uncertainties. Distributed protocols are constructed by using iterative learning control (ILC) algorithms, where information is exchanged only at the end of one iteration and learning is used to update the control inputs after each iteration. It is proved that under ILC-based protocols, the finite-time consensus can be achieved with an increasing number of iterations if the communication network of agents is guaranteed to have a spanning tree. Moreover, if the information of any desired terminal output is available to a portion (not necessarily all) of the agents, then the consensus output that all agents finally reach can be enabled to be the desired terminal output. It is also proved that for all ILC-based protocols, gain selections can be provided in terms of bound values, and consensus conditions can be developed associated with bound matrices. Simulation results are given to demonstrate the effectiveness of our theoretical results. [ABSTRACT FROM AUTHOR]

This paper deals with iterative learning control (ILC) design for uncertain time-delay systems. Monotonic convergence of the resulting ILC process is studied, and a sufficient condition within anH∞-based framework is developed. It is shown that under this framework, delay-dependent conditions can be obtained in terms of linear matrix inequalities (LMIs), together with formulas for gain matrices design. A numerical example is provided to illustrate the effectiveness of the robustH∞-based approach to ILC designed via LMIs. [ABSTRACT FROM PUBLISHER]

This paper deals with the problem of varying initial state shifts in continuous-time iterative learning control (ILC) by employing the two-dimensional (2D) analysis approach. Two PD-type ILC algorithms are considered based on an average operator, where the second one is combined with an initial rectifying action. For both ILC algorithms, 2D Roesser systems are applied to develop sufficient conditions to guarantee their convergence. It is shown that the ILC processes converge with the increasing of iteration, and the converged tracking error can be formulated in terms of the mean of the initial output error and the learning parameters. Numerical simulations are given to illustrate that both ILC algorithms are robust against varying initial state errors, and the second one can achieve uniform convergence over any specified time interval with good learning transients. [ABSTRACT FROM PUBLISHER]

This paper aims to address finite-time consensus problems for multi-agent systems under the iterative learning control framework. Distributed iterative learning protocols are presented, which adopt the terminal laws to update the control input and are offline feedforward design approaches. It is shown that iterative learning protocols can guarantee all agents in a directed graph to reach the finite-time consensus. Furthermore, the multi-agent systems can be enabled to achieve a finite-time consensus at any desired terminal state/output if iterative learning protocols can be improved by introducing the desired terminal state/output to a portion of agents. Simulation results show that iterative learning protocols can effectively accomplish finite-time consensus objectives for both first-order and higher order multi-agent systems. [ABSTRACT FROM AUTHOR]