Résumé:
This doctoral thesis proposes different control approaches to enhance the performance
of the neural network model predictive control. For this purpose, a feed
forward neural network model is suggested to build the prediction model for the
model predictive control. Various metaheuristic algorithms are integrated to solve
the nonlinear and non-convex optimization problem of the neural network model
predictive control, such as particle swarm optimization, teaching learning-based
optimization, and driving training-based optimization algorithms.
Four algorithms are introduced and tested on different systems. In the first
algorithm, the teaching learning-based optimization algorithm is used to solve the
neural network model predictive control optimization problem, with the implementation
of a reduction weighting coefficient in the cost function.This algorithm is
applied to control the permanent magnet synchronous motor speed. As for the
second algorithm, a driving training-based optimization algorithm is proposed to
solve the optimization problem of the neural network model predictive control. This
algorithm is used to control the speed of an electric vehicle. Furthermore, a sequence
of reduction weighting coefficients is integrated into the cost function of the previous
algorithm. This algorithm is applied for the tracking control of a two degrees-offreedom
robot manipulator.To ensure a closed-loop stability of the controlled system a Lyapunov function
is incorporated as a constraint in the cost function of the neural network model
predictive control based on the driving training optimization approach. The re-
sulting control algorithm is applied to the speed control of an induction motor,
then compared against a set of control approaches including optimized PID based
on particle swarm optimization algorithm, fuzzy PID, neural network model pre-
dictive control based on different optimization algorithms, such as particle swarm
optimization, teaching learning-based optimization algorithm, and driven training-
based optimization algorithm.
