Adaptive Robust Nonlinear Active Suspension Control Using an Observer-Based Modified Sliding Mode Interval Type-2 Fuzzy Neural Network

Ride comfort and road holding are two substantial performance criteria related to the suspension system of road vehicles. These performance criteria are critical in controller design and chassis stability of automated driving vehicles. Control demands in terms of accuracy, quick response and robustness to matched and mismatched uncertainties are suggestive of employing adaptive robust controllers. Herein, a state observer-based modified sliding mode interval fuzzy type-2 neural network (FT2NN) controller is designed to suppress the vibrations from a typical rough terrain imposed to the nonlinear suspension system of the vehicles. The nonlinear system dynamics are estimated using the universal approximation capacity of the neuro-fuzzy type-2 approach and the states are obtained by the adaptive robust state observer. The membership functions (MFs) of the fuzzy type-2 system are employed to deal with the uncertainties through variable mean and variances for upper and lower MFs. Furthermore, the proposed controller has the advantage of relaxing the condition for the approximation error boundaries while the estimation step is utilized to observe the boundaries adaptively. A new adaptive compensator is employed to withstand the effect of the external disturbance, the approximation errors related to the unknown nonlinear functions and state estimations. The results obtained from the proposed controller are suggestive of the higher effectiveness of the proposed controller compared to the tested Neuro-PID controller, and also the passive suspension system. The high-fidelity MSC ADAMS based co-simulations were implemented to validate the practicality of the proposed controller.