Online adaptive PID tracking control of an aero-pendulum using PSO-scaled fuzzy gain adjustment mechanism

This article is centered on the development of a robust position control and disturbance compensation strategy for a mechatronic aero-pendulum using the soft computing paradigm. The pendulum arm is rotated about its pivot via the thrust generated by two coaxial contra-rotating motorized propellers installed at its free end. The tracking error in arm’s angular position is fed to a multi-loop feedback controller. The proportional–integral–derivative (PID) controller, in the outer loop, stabilizes the arm at the reference position. The reference current control signals generated by the PID position controller are fed to two PI controllers, in the inner loop, that are responsible for regulating the current consumption of each motorized propeller. Initially, the fixed PID controller gains are evaluated by selecting the optimal value of the system’s closed-loop pole using the particle swarm optimization (PSO) algorithm. However, to mitigate the inefficacies of fixed gain controller and further enhance the system’s robustness against bounded exogenous disturbances and damping against oscillations, the closed-loop pole is dynamically adjusted via fuzzy inference system, after every sampling interval. The fuzzy membership functions are calibrated offline via PSO algorithm. The superior time optimal control behavior rendered by the proposed controller is validated by comparing its performance with fixed gain controller via credible real-time experiments.