Model-Free Adaptive Control Based on Pattern Class Variables for a Class of Unknown Non-Affine Nonlinear Discrete-Time Systems

This paper is concerned with the problem of a full formal dynamic linearized model-free adaptive control scheme based on pattern class variable (P-FFDL-MFAC) for a class of unknown non-affine nonlinear discrete-time systems. The concept of pattern class variable is defined as dynamic operating variables rather than state variables or output variables. The pattern classes is utilized as the system output conditions, and the purpose of the control is to ensure that the system output belongs to a certain pattern class or some desired pattern classes. The scheme of P-FFDL-MFAC mainly consists of an improved tracking control law, a bias estimation algorithm, and a pseudo-gradient vector estimation algorithm. Furthermore, based on the contraction mapping theorem, the bounded convergence of tracking error has been proved. Finally, numerical examples and the actual sintering process data are used, respectively, to verify the effectiveness of the proposed design techniques and are compared with the traditional MFAC method. The results are better than the traditional method.