tracking-based decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale nonlinear systems

This paper proposes a novel H ∞ tracking-based decentralized hybrid adaptive fuzzy controller via output feedback for a class of large-scale uncertain nonlinear systems. To achieve better adaptation properties, the proposed controller is a weighted combination of indirect and direct adaptive fuzzy controls (IAFC and DAFC) such that both fuzzy descriptions and control rules can be incorporated at the same time. After proper filtering of the observation error dynamics, the developed observer-based feedback and adaptation mechanisms become implementable indeed. By using fuzzy inference systems, a state observer and H ∞ tracking technique, the decentralized combined indirect and direct adaptive fuzzy control algorithm is developed based upon a united H ∞ tracking controller design for the modification of the previous work of Huang et al. The resultant closed-loop systems are guaranteed to be stable and a good H ∞ tracking performance is obtained. Simulation results of interrelated inverted pendulums substantiate that the proposed CAFC system stands out as being better properties than typical IAFC and DAFC systems in that the tracking performance is much better while the control effort is even smaller.