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Thanks to their interesting optical and electrical properties, conductive polymers are one of the materials to have highest potential utility in technological applications. But theirs usage in practical applications are limited due to poor solubility, cannot be easily processed and lack of good mechanical strength and stability. In this work synthesis of conductive polymer was achieved in environmentally friendly aqueous dispersion of polystyrene sulfonic acid instead of harmful organic solvent (dichloromethane, acetonitrile, chloroform,). Thus, a water-insoluble monomer was polymerized in an aqueous medium instead of organic solvents which are harmful to environment and human health. Also, conductive polymer obtained in aqueous medium has superior optical and electrical properties than the conductive polymer obtained in the presence of organic solvents. This paper has been shown that other conductive polymers synthesized in hazardous organic solvents can be synthesized in aqueous media with superior optic and electrical properties which is important for the technological application.

The effect of the unmodeled dynamics and unknown disturbances prevent the accurate control of the real-time systems. The designed controllers must undertake the effect of these undesired uncertainties. In this paper, adaptive uncertainty modeling based model predictive controller is proposed for the control of uncertain linear systems. The uncertainty modeling structure uses an artificial neural network with adaptive learning rate for fast approximation. The stability of the proposed adaptive uncertainty modeling based model predictive control (UMPC) is shown using Lyapunov candidate function. Conventional MPC and proposed UMPC are applied to the control of a real-time DC/DC buck power converter. The conventional MPC cannot accurately control the DC/DC converter due to the unknown parameters and unmodeled dynamics. However, the proposed UMPC controller can accurately control the system with modeling the uncertainties in controller dynamics. The proposed controller is promising to control uncertain systems in future applications.