Optimal Design of Linear Quadratic Regulator Controller Based on Adaptive Cuckoo Search Algorithm
Xuexin Chi, Rui Chi · 2020
In order to quickly and effectively determine the weighted matrix Q and R in the linear quadratic regulator (LQR) controller of active suspension, this paper presents adaptive cuckoo search algorithm (ACS) for LQR optimization design. ACS improves step size control and discovery probability in standard cuckoo search algorithm (CS) based on adaptive dynamic adjustment strategy. Compared with other test algorithms, for optimizing active suspension LQR controller, experimental results show that the convergence speed of ACS is faster, the solution accuracy is higher, the stability is stronger, and the performance indexes of active suspension are significantly improved.