The PFDL-Model-Free Adaptive Predictive Control for a Class of Discrete-Time Nonlinear Systems

TL;DR

This paper introduces a new data-driven, model-free adaptive predictive control method for discrete-time nonlinear systems, combining MPC and MFAC concepts, with proven stability and verified effectiveness through simulations and experiments.

Contribution

It proposes a novel PFDL-MFAPC method that integrates MPC with MFAC, providing stability guarantees and practical validation.

Findings

Proven BIBO stability of the control method

Demonstrated monotonic convergence of tracking error

Validated effectiveness through simulations and experiments

Abstract

In this paper, a novel partial form dynamic linearization (PFDL) data-driven model-free adaptive predictive control (MFAPC) method is proposed for a class of discrete-time single-input single-output nonlinear systems. The main contributions of this paper are that we combine the concept of MPC with MFAC together to propose a novel MFAPC method. We prove the bounded-input bounded-output stability and tracking error monotonic convergence of the proposed method; Moreover, we discuss the possible relationship between the current PFDL-MFAC and the proposed PFDL-MFAPC. The simulation and experiment are carried out to verify the effectiveness of the proposed MFAPC.