\chapter{CONCLUSION AND FUTARE WORK}
\label{ch:conc}
\section{\normalsize INTRODUCTION}
This research has presented a study in mechatronic sysfrom the work described on this thesis. The main contributions to knowledge are presented in Section \ref{sec:73ctk}. Finally the suggestions ideas for the next works are given in Section \ref{sec:74safw}.  

\section{\normalsize CONCLUSIONS}
\label{sec:72conc}
lts in a low-order controller with good performance, but requires more manual fine tuning, since less system information is taken into account directly in the control design. Optimal LQR as well as LQG controllers yield not only a comparable performance, but also better performance than PID. Therefore LQR and LQG are the advised approach for the control design for this system, since it yields the best tradeoff between performance and complexity. However, these techniques showed high actuating power demand especially when the system was time variant model.system has been built by considering the variations of the sprung mass, road profile input, the nonlinear damper and actuator dynamics and the suspension performances. This control design process is different from the other control techniques. Two weighted MMAC schemes, output (PID) and state feedback (LQG), are implemented for the quarter car suspension system. The results showed that LQG weighted MMAC has better performances compared to PID scheme especially in case of real road profile.
he vehicle. Thereby, significant performance enhancements in terms of ride comfort and ride safety can be achieved without violating suspension travel limits.\\


\section{TO KNOWLEDGE}
\label{sec:73ctk}


	One of the contribution of this Thesis has  concepts that enable to exploit the performance potential of mechatronic vehicle suspensions.
	
	
	nt way to handle known parameter uncertainty in a certain region around a nominal model and it constitutes a good underlying design method for adaptive control, but it is not an adaptive control system.
	Adaptive 
	 can improve the performance of a robust control design by providing better information about the nominal model and expanding the uncertainty region for which the desired performances can be guaranteed.
	
	The use of adaptive control is based on the assumption that for any possible values of the plant parameters there is a controller with a fixed structure and complexity such that the desired performances can be achieved with appropriate values of the controller parameters. The task of the adaptation loop is to search for the good values of the controller parameters.



