The magnetorheological fluid semi-active suspension system has been widely employed in vehicles. In general, the output force of the magnetorheological fluid damper varies with service time and temperature, which will cause the controller performance to degrade or even fail. To conquer this problem, this study proposes a compensatory backstepping strategy for the magnetorheological fluid suspension system. First, the normalized phenomenological model is introduced to simulate the nonlinear characteristics of the magnetorheological fluid damper. Second, the adaptive radial-basis function neural network is employed to construct the inverse model of the magnetorheological fluid damper. Next, the reference state is calculated by the filter performance function to trade off the vehicle handling stability and ride comfort. Finally, the voltage-force compensator is constructed to improve the robustness of the controller under input time delay, external noise disturbance, and actuator failures. Furthermore, the performances of the magnetorheological fluid damper, inverse magnetorheological fluid model, and compensatory backstepping controller are simulated under bump and random excitations. It is found that the proposed compensatory backstepping controller shows good improvement in the semi-active suspension system with time delay, noise disturbance, and actuator failure.