Wireless networked learning control system based on Kalman filter and biogeography-based optimization method

Abstract

This paper proposes a biogeography-based optimization (BBO) method augmented with a Kalman filter, which is called KFBBO, for PID parameter tuning in a wireless networked learning control system (WNLCS). Because of unreliable transmission of data and commands in wireless networks, the control system is noisy and prone to errors, which results in poor performance by the conventional PID method for wireless networked control in real-world applications. BBO as a new evolutionary optimization is proposed to solve this problem by dynamically optimizing the PID control parameters. Because the wireless network environment is noisy, we also use a Kalman filter to counteract the negative effects of noise and to improve the optimization ability of BBO. Simulation experiments are conducted to evaluate our proposed KFBBO, and the results indicate that the control performance obtained by the improved PID method is better than the conventional PID method. Furthermore, this proposed method is applied to a steam turbine power generation system based on a WNLCS, and the results show its feasibility and effectiveness.