Active force control with iterative learning control algorithm for a vehicle suspension - Rosmazi, Rosli (2013)
The research focuses on the application of an active force control (AFC) strategy with iterative learning control (ILC) algorithms to compensate for the various introduced road profiles or 'disturbances' in a quarter car suspension system as an improvement to ride comfort performance. ILC algorithm is implemented into AFC-based control scheme to reduce its complexity and hence faster response, by replacing the use of artificial intelligence (Al) method as proposed by previous researcher. The new control scheme named active force control with iterative learning control algorithm (AFCIL) is complemented by the classic proportionalintegral-derivative (PID) control incorporated and designed as the outermost control loop. The PID controller was first designed and tested prior to developing the AFC which was directly cascaded with the YIDdoop. A number of ILC algorithms were explicitly employed to compute the estimated mass in the AFC loop that is necessary to trigger the control action. The AFC with ILC (AFCIL) suspension system was experimented both through simulation and practical experimentation considering various ILC learning parameters, differenti operating conditions and a number of external disturbances to test and verify the system robustness. The simulation was conducted using MATLAB/Simulink software package whilstthe experimental study utilized the existing experimental rig with a hardware-in-the-loop simulation (HILS) configuration with the proposed ILC algorithms incorporated as the new research contribution. The results obtained, via various control schemes in, the form of PID, AFCIL and passive systems were rigorously, compared and analyzed.to ascertain the system performance in terms of its, ability, to improve riding comfort characteristics. The results imply that the proposed AFC-based scheme produces the best response with an approximately 50% improvement I in comparison to the 'PID and passive counterparts.