Multi Strategy SA-PSO for Inverted Pendulum Identification Combined with Explicit Model Predictive Control

In this paper, the inverted pendulum is controlled by Quanser universal benchtop equipment-direct current (QUBE DC) motor, and the motor inverted pendulum system is identified when the sinusoidal signal is input. However, there is a large error when the angle of the pendulum is pre-identified by numerical algorithms for subspace state space system identification algorithm. Therefore, this paper designs a multi strategy optimization simulated annealing particle swarm optimization algorithm, which can accurately identify the complex system with sinusoidal input signal. In the identification experiments of sinusoidal signals with multiple frequencies and amplitudes, this paper found that the algorithm performs relatively best at a frequency of 8 rad/s. Moreover, at a frequency of 8 rad/s, the algorithm can quickly reduce the error to 1.69% within 100 generations. Finally, based on the hardware model identified by particle swarm optimization, this paper designs an explicit model predictive control method to control the inverted pendulum, and tests the constraint processing and anti-disturbance performance of the system under the voltage pulse interference with different duty cycles, which realizes the inverted pendulum balance and has robustness under the interference.