R-L-Based Distributed Sliding Mode Control for Isomorphic Complex Network

In this paper, we discuss the distributed optimal sliding mode control (SMC) issue for the isomorphic complex network in view of learning theory. Firstly, a dynamic distributed SMC strategy is introduced innovatively by virtue of reinforcement learning (R-L) theory, and on this basis, the overall tracking error closed-loop system is formulated between the complex network and the target system. For the overall error system, we provide the detail schemes for stability analysis and SMC control synthesis. The corresponding criteria and SMC algorithm are proposed via the solvable inequality constraints. Subsequently, the reachability issue is also analyzed for the pre-designed sliding mode surface. To conclude the paper, we check some unmanned aerial vehicles (UAVs) as the example under the complex network framework, and then, verify the effectiveness of the conclusions and algorithms for UAVs in this paper.