\begin{enAbstract}
suspension deflection. Priority of this thesis is to emphasize the contributions in handing actuator-related challenges and suspension model parameter uncertainty.\\
The challenges in mechatronic suspension system and their actuators are identified as a load dependent variation and actuator uncertainty. System parameters uncertainties and its effects in overall mechatronic suspension system are targeted and analyzed. The variable sprung masses (vehicle loads) are estimated online using adaptive least square algorithm. Therefore, the controller gain value is to be varied according to the vehicle load. Based on that, three low level optimal controllers, namely PID, LQR and LQG, are designed using simple effective optimization method for three deferent load conditions. LQG controller is selected as a benchmark due to its simplicity, cost actively and high performances. By removing the assumptions from the state of the art methods, a robust output-feedback controller design methods, $\mu$-synthesis control, are proposed to design less conservative output-feedback controller existence conditions. It overcomes the challenges brought by generalized time varying system parameter and guarantee asymptotic the stability. On the other hand, weighted MMAC controller design algorithm is developed for mechatronic suspension systems for linear and nonlinear suspension system.\\ 
daptively controlled suspensions, and overcomes the conservatism frequently introduced by time-invariant suspension controllers considering the power require-ments as well.\\
In conclusion,  are proposed for active suspension systems with uncertainty in order to guarantee and improve the  performance. Sthe state of the art methods are provided to evaluate the effectiveness of the research contributions. The thesis shows insights into practical solutions to vehicle active  systems, it is expected that these algorithms will have significant potential in industrial applications and electric vehicles industry.

\end{enAbstract}