Abstract
This paper presents an adaptive particle swarm optimization (APSO) based
LQR controller for optimal tuning of state feedback controller gains for a
class of under actuated system (Inverted pendulum). Normally, the weights
of LQR controller are chosen based on trial and error approach to obtain
the optimum controller gains, but it is often cumbersome and tedious to
tune the controller gains via trial and error method. To address this
problem, an intelligent approach employing adaptive PSO (APSO) for optimum
tuning of LQR is proposed. In this approach, an adaptive inertia weight
factor (AIWF), which adjusts the inertia weight according to the success
rate of the particles, is employed to not only speed up the search process
but also to increase the accuracy of the algorithm towards obtaining the
optimum controller gain. The performance of the proposed approach is
tested on a bench mark inverted pendulum system, and the experimental
results of APSO are compared with that of the conventional PSO and GA.
Experimental results prove that the proposed algorithm remarkably improves
the convergence speed and precision of PSO in obtaining the robust
trajectory tracking of inverted pendulum.
Go to article