Discretization-Based Semi-Active Suspension Control Using Road Preview Data

While semi-active suspensions help improve the ride comfort and road-holding capacity of the vehicle, they tend to be reactive and thus leave a lot of room for improvement. Incorporating road preview data allows these suspensions to become more proactive rather than reactive and helps achieve a higher level of performance. A lot of preview-based control algorithms in literature tend to require high computational effort to arrive at the optimal parameters thus making it difficult to implement in real time. Other algorithms tend to be based upon lookup tables, which classify the road input into different categories and hence lose their effectiveness when mixed types of road profiles are encountered that are difficult to classify. Thus, a novel MPC (model predictive control)-based algorithm is developed which is easy to implement online and more responsive to the varying road profiles that are encountered by the vehicle. The efficacy of the algorithm is tested against a numerical methods-based control algorithm that can determine the maximum possible ride comfort achieved using semi-active dampers capable of altering their damping characteristics every 0.01 s. Results indicated that the proposed strategy is quite effective in providing holistic improvement in the sprung mass motion, achieving on average 69% of the maximum ride comfort possible with a fraction of the computational effort.