This thesis presents theoretical and practical issues of local optimal control, which is one of the advanced control methods. It can be counted as an optimal modelbased multivariable control technique. The main contributions of this work can be summarized as follows. • A comparative robustness study of local optimal controller with other conventional controllers is performed for gas turbine engine as a multivariable system. • As the original local optimal control is incapable to deal with non-minimum phase systems, a modified local optimal control is proposed to deal with non-minimum phase systems as well as minimum phase systems. • The local optimal controller performance is investigated for reduced order models. Because of its effectiveness, genetic algorithm is used with certain predefined controller structures as an alternative method to estimate the controller parameters without obtaining the model parameters. • A new tuning technique of digital PID controller is introduced for both multivariable and single-input single-output systems based on the relations deduced with the local optimal controller. As such, the PID controller is turned into model-based controller. • As tlie PIO and the local optimal controller are model based multivariable controllers, their parameters can be tuned online based on online identification techniques. The recursive lease squares algorithm is used as an online . closed loop identification technique to achieve such online tuning of those controller parameters. • Local optimal controller is generalized to deal with non-linear systems as a non-linear controller. • Most of the above techniques are tested on a laboratory-based test rig.