Sliding mode control of multi-agent systems with switching topologies and input constraints via Interval Type-2 fuzzy neural network

The research analyzes nonlinear fractional-order multi-agent systems (MASs) with time delays and unsure dynamics under input saturation constraints by creating an adaptive control protocol which effectively solves these difficulties. Real-time control parameter updates within an Robust General Type-2 Fuzzy Neural Network (RGT2FNN) create estimates of unknown non-linear functions to produce stability-assuring control signals during system operation. A novel advanced Biogeography-Based Optimization (BBO) algorithm handles parameter estimation for RGT2FNN fractional information along with the system fractional orders since their values remain unidentified in the control design process. The control framework incorporates a robustness-enhancing Linear Matrix Inequality (LMI)-based compensator that guarantees stability under uncertainty conditions as well as time-varying delays and input saturation constraints since these realistic limitations are directly accounted for through formal prevention strategies. The proposed approach showcases its practical capabilities when used with unknown dynamics, unknown fractional orders, and input saturation in a simulation where it demonstrates superior performance in bounded control applications. Simulation results confirm that the proposed control strategy achieves faster convergence, reduced tracking error, and significant suppression of chattering compared to conventional Interval Type-2 Fuzzy Neural Network (IT2FNN)–Sliding Mode Control (SMC) methods, ensuring smooth and stable formation performance under switching topologies and input constraints.