Experimental Validation of LQR Weight Optimization Using Bat Algorithm Applied to Vibration Control of Vehicle Suspension System

To deal with multiple constraints of vehicle active suspension system (ASS) including road handling and passenger safety, this paper presents an optimal linear quadratic regulator (LQR) approach which employs bat algorithm (BA) for selection of optimal state and input penalty matrices of LQR. We formulate the conflicting control objectives of ASS, namely, ride comfort and passenger safety as a multi-constraint optimization problem and employ the BA for weight selection of LQR. The key advantage of the proposed approach is that the local optima problem is avoided by utilizing the frequency tuning and random walk technique in BA. The performance of the proposed approach is experimentally tested using hardware in loop (HIL) testing on a quarter car ASS for realistic road profiles. Moreover, the performance is benchmarked against grey wolf optimization tuned LQR. Experimental results assessed based on ISO 2631 standards highlight the significant improvement in the ride comfort and passenger safety.