  This paper introduces an indirect adaptive fuzzy model predictive control
strategy for a nonlinear rotational inverted pendulum with model uncertainties.
In the first stage, a nonlinear prediction model is provided based on the fuzzy
sets, and the model parameters are tuned through the adaption rules. In the
second stage, the model predictive controller is designed based on the
predicted inputs and outputs of the system. The control objective is to track
the desired outputs with minimum error and to maintain closed-loop stability
based on the Lyapunov theorem. Combining the adaptive Mamdani fuzzy model with
the model predictive control method is proposed for the first time for the
nonlinear inverted pendulum. Moreover, the proposed approach considers the
disturbances predictions as part of the system inputs which have not been
considered in the previous related works. Thus, more accurate predictions
resistant to the parameters variations enhance the system performance using the
proposed approach. A classical model predictive controller is also applied to
the plant, and the results of the proposed strategy are compared with the
results from the classical approach. Results proved that the proposed algorithm
improves the control performance significantly with guaranteed stability and
excellent tracking. Keywords: Indirect adaptive fuzzy; Model predictive
control; Nonlinear rotational inverted pendulum; Model uncertainties; Lyapunov
stability theorem.
