Data-driven Iterative Learning Formation Control of Non-affine Multi-agent Systems Using Integral Sliding Mode Method

The problem of formation control for unknown nonlinear and non-affine multi-agent systems with external perturbations is addressed in this study based on a data-driven robust learning-based algorithm. Firstly, by using the iterative dynamic-linearization concept and pseudo-partial-derivatives, virtual linearized data models are established to represent the agents' unknown dynamics. Subsequently, an iteration-dependent integral sliding variable is considered to design the proposed data-based iterative-learning integral sliding-mode formation control which is solely based upon the input/output information of the agents. Finally, the mathematical analysis proves the stability of the closed loop plant. A demonstrative example is also conducted to verify the proficiency of the designed method.