Design of a nonlinear fractional order PID neural controller for mobile robot based on particle swarm optimization

Abstract EN

The goal of this paper is to design a proposed non-linear fractional order proportional-integral-derivative neural (NFOPIDN) controller by modifying and improving the performance of fractional order PID (FOPID) controller through employing the theory of neural network with optimization techniques for the differential wheeled mobile robot multi-input multi-output (MIMO) system in order to follow a desired trajectory.

The simplicity and the ability of fast tuning are important features of the particle swarm optimization algorithm (PSO) attracted us to use it to find and tune the proposed non-linear fractional order proportional-integral-derivative neural controller’s parameters and then find the best velocity control signals for the wheeled mobile robot.

The simulation results show that the proposed controller can give excellent performance in terms of compared with other works (minimized mean square error equal to 0.131 for Eight-shaped trajectory and equal to 0.619 for Lissajous- curve trajectory as well as minimum number of memory units needed for the structure of the proposed NFOPIDN controller (M=2 for Eight-shaped trajectory and M=4 for Lissajous- curve trajectory) with smoothness of linear velocity signals obtained between (0 to 0.5) m/sec