Adaptive Fuzzy Decentralized Output‐Feedback Inverse Optimal Control for Nonstrict‐Feedback Interconnected Large‐Scale Systems

ABSTRACT In this article, the problem of adaptive fuzzy decentralized inverse optimal control is investigated for nonstrict‐feedback nonlinear interconnected large‐scale systems. The interconnected large‐scale systems include unmeasurable states, nonstrict‐feedback structures, and unknown nonlinear functions. Fuzzy logic systems are used to approximate unknown nonlinear functions of subsystems, and a fuzzy decentralized state observer is proposed to estimate unmeasured states. By combining the dynamic surface backstepping control technique with inverse optimal control principle, an adaptive fuzzy decentralized inverse optimal control scheme is established. It is proved that all the signals of the controlled nonlinear interconnected large‐scale systems are bounded, the tracking errors converge to a small neighborhood of zero, and the cost function is minimized. The simulation results and comparison results further verify the effectiveness of the proposed decentralized inverse optimal control approach.