Trajectory tracking of differential drive mobile robots using fractional-order proportional-integral-derivative controller design tuned by an enhanced fruit fly optimization

This work proposes a new kind of trajectory tracking controller for the differential drive mobile robot (DDMR), namely, nonlinear neural network fractional-order proportional integral derivative (NNFOPID) controller. The suggested controller’s coefficients comprise integral, proportional, and gains as well powers. adjustment these turns design proposed NNFOPID control further problematic than conventional proportional-integral-derivative control. To handle this issue, an Enhanced Fruit Fly Swarm Optimization algorithm has been developed in to tune NNFOPID’s parameters. enhancement achieved on standard fruit fly optimization technique lies increased uncertainty values initialized convey broader search space. subsequently, range is varied throughout updating stage by beginning with big radius declines gradually during course searching stage. validated its ability track specific three types continuous trajectories (circle, line, lemniscate) while minimizing mean square error energy. Demonstrations have run under MATLAB environment revealed practicality designed motion controller, where performance compared that Neural Network Proportional Integral Derivative one aforementioned DDMR.