In most of industrial applications, the dynamics of the system in hand are perturbed by a number of operational conditions. Also the outputs of the sensors always include noise. To alleviate these common problems, this paper presents a novel fuzzy control approach based on the immersion and invariance (I&I) approach under the conditions of unknown dynamics and measurement errors. The adaptation laws for the parameters of the proposed non-singleton type-2 fuzzy neural network (NT2FNN) are derived through a stability analysis based on I&I method. The effectiveness of the proposed membership function (MF) and non-singleton fuzzification is verified by comparison with the conventional Gaussian MF in the presence of measurement errors. The performance of the proposed control method is compared with other techniques and an experimental study is provided to show the capability of the proposed control scheme in real-time applications.
