Neural Network Assisted Computationally Simple PI^D^ Control of a Quadrotor UAV

The applications of Unmanned Aerial Vehicles (UAVs) require robust control schemes that can alleviate disturbances such as model mismatch, wind disturbances, measurement noise, and the effects of changing electrical variables, e.g., the loss in the battery voltage. Proportional Integral and Derivative (PID) type controller with noninteger order derivative and integration is proposed as a remedy. This paper demonstrates that a neural network can be trained to provide the coefficients of a Finite Impulse Response (FIR) type approximator, that approximates to the response of a given analog PI λ D μ controller having time varying action coefficients and differintegration orders. The results obtained show that the neural network aided FIR type controller is very successful in driving the vehicle to prescribed trajectories accurately. The response of the proposed scheme is highly similar to the response of the target PI λ D μ controller and the computational burden of the proposed scheme is very low.