Stable multi-input multi-output adaptive fuzzy control

A stable indirect adaptive controller is presented which uses Takagi-Sugeno fuzzy systems, conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal vector for a class of continuous time multi-input multi-output (MIMO) square nonlinear plants with poorly understood dynamics. The adaptive scheme allows for the inclusion of a priori knowledge about the plant dynamics in terms of exact mathematical equations or linguistics. We prove that with or without such knowledge the adaptive scheme can "learn" how to control the plant, provide for bounded internal signals, and achieve asymptotically stable tracking of the reference inputs. We do not impose any initialization conditions on the controller, and guarantee convergence of the tracking error to zero.