Iterative learning-based consensus tracking control for conformable multi-agent systems

This paper considers the consensus tracking control problem for conformable multi-agent systems with linear and nonlinear dynamics by designing P-type and PDα-type iterative learning control law with initial learning mechanisms. Conformable derivative is well-behaved and can characterize a different step in real data sampling. The initial learning mechanism relaxes the initial value condition and improves the performance of the protocol to achieve consensus tracking. A distributed iterative learning scheme is proposed to realize the finite-time consensus by repeating the control attempt of the same trajectory and correcting the unsatisfactory control signal with the tracking error under the assumption of repeatable operation environments as well as a directed communication topology. The asymptotical convergence of the proposed P-type and the PDα-type distributed iterative learning protocol for all agents is strictly proved as the iteration number increases. Two numerical examples are simulated to verify the effectiveness of the protocols. © 2022 South China University of Technology. All rights reserved.