  In this paper, a novel full form dynamic linearization (FFDL) data-driven
model-free adaptive predictive control (MFAPC) method is proposed for a class
of discrete-time single-input single-output nonlinear systems. The novelty of
MFAPC is that preliminary physical model and the Lyapunov stability theory are
not required for the controller design and theoretical analysis. Instead, the
proposed MFAPC only uses a new dynamic linearization method called
pseudo-gradient (PG) vector, which is merely related to the input/output (I/O)
measurement data. The main contributions of this paper are: First, a novel
MFAPC with adjustable parameters is proposed; Second, we have proved the
bounded-input bounded-output stability, the monotonic convergence of the
tracking error, and the internal stability of the proposed method. Third, the
proposed MFAPC can be considered as an elegant extension of the current MFAC.
The simulations have been carried out to verify the effectiveness of the
proposed MFAPC.
