Adaptive Intelligent H-Infinity Controller Design for MIMO Nonlinear Systems Under Asymmetric Input Saturation

This paper details the design of a robust adaptive intelligent guaranteed cost H-infinity controller, tailored for MIMO nonlinear systems subject to external disturbances, parametric uncertainties, and asymmetric input saturation constraints. The adaptive intelligent part of the proposed controller consists of a Radial Basis Function Neural Network (RBFNN) with bounded output to respect saturation constraints and a robust adaptive law for RBFNN weights estimation. This part serves to estimate the system's uncertain nonlinearities and external disturbances. The guaranteed cost H-infinity is designed based on Linear Matrix Inequalities (LMIs) to address the system parameter uncertainties and bounded RBFNN estimation errors, as well as account for input asymmetric constraints. A reference model is utilized to define the tracking error dynamics. Lyapunov stability analysis is used to establish the ultimate boundedness of the tracking error trajectories. The Equilibrium Optimizer (EO) algorithm is used to optimize the proposed controller parameters. The simulation results demonstrate that the proposed controller accurately tracks the reference model output and maintains stability under saturation despite parameter uncertainties, nonlinearities, and external disturbances.