A Study of Optimized-LQR Control for Rotary Inverted Pendulum by Particle Swarm Optimization

Rotary Inverted Pendulum (RIP) is a classical but effective model in testing control algorithms. Besides designing controllers, it can also be a model for testing the evolution algorithms (EAs) in optimizing control parameters. In this paper, we apply particle swarm optimization (PSO), which is an EA, to optimize the parameters of the LQR controller for this model. In the study, an experimental model in which system parameters are already measured and identified in former studies is used. The LQR control method is inherited from former results, and the weighing matrices (Q and R) are optimized by the PSO method. In each case, the control matrix K is obtained from Q and R to apply for RIP. Through both simulation and experiment, LQR control parameters are found better through generations by using PSO. The responses of RIP, in which controllers are designed under optimized Q and R in later generations, are better in quality, and values of the fitness function also supports that opinion. Thence, through this study, beside genetic algorithm (GA), this study proves that PSO is a suitable searching algorithm that can be applied for balancing this single input- multi output (SIMO) system. Also, the experimental platform of RIP in this research confirms its ability to control tests.