Parliamentary System and Framing of the 1973 Constitution: Contest between Government and Opposition inside the National Assembly

It is generally believed that the Constitution of 1973 was passed unanimously by the parliament of Pakistan and was equally acceptable for all the federating units. While studying the processes of the approval of the said constitution inside the assembly, it becomes evident that the reality was quite different. There exists an argument that most of the Opposition members were not allowed to join the parliament’s session while the final approval of the constitution was processed. The present paper is an effort to analyse the developments that took place inside the National Assembly to pass this document which was to serve as the fundamental document of the state system in forthcoming years. In other words, the present article analyses the course of action through which the Constitution was framed. This is an analytical study primarily based on the National Assembly debates supported by the secondary sources, biographies, and autobiographies of the contemporary politicians to understand how far the amendments suggested by the then opposition were accommodated by the ruling party. Furthermore, this paper analyses the reasons for which each government has to amend the basic structure of the constitution to make it more practical and acceptable for its units. For instance the Eighteenth Amendment removed the concurrent list of the constitution but now the following governments are facing issues to implement the Amendment in detail.

Analysis and Design of Neural Network Controller of Single Active Bridge Converter

The power electronic researchers and designers are involved since last two decades in designing converter topologies for their effective use in high power applications. Recently, developed SAB converter being an advanced topology is gaining popularity because of its many attractive features. Despite of its attractive characteristics and merits it faces a serious problem of the generation of oscillations. These oscillations create disturbances in the systems network and also badly affect the performance of the converter itself. Such topologies with conventional controllers are also produce power quality problems such as; variation in frequency, voltage dips and swells, which lead to interruptions and transients in system networks. As it is a well known fact, that SAB converter is a variable structure system due to its switching operation and contains nonlinearities. Because of the high switching speed of SAB converter, i.e. in nano seconds, it is difficult to control it with linear controllers. In previous research the Proportional plus Integral (PI) controllers were used which were unable to control the SAB converter under nonlinear situations and ultimately this will lead to undesired results. Considering the deleterious effects and limitations of the conventional controllers in this research the Neural Network based controller is designed to handle the non-linear situations. Neural Network Controller (NNC) is recently found various applications in power electronic converters because of its good dynamic behavior and robustness. Designing of the Neural Network Controller for SAB converter is the main contributions in this research work. The NNC type cascade controller for SAB converter is successfully developed in MATLAB. The performance of single active bridge topology with NNC is tested under steady state, transient and dynamic region by considering the, line, duty ratio and reference voltage variations. Simulation results show improvements in transients and reference variation in the range of 26% to 38% respectively and 6 time improvement in line variation and 30 time improvement in duty ratio variation. The designed controller after through analysis, testing and comparison with previous designed controllers depicts that the neural network based controller is quite effective, efficient and has good transient and steady-state properties. Keywords: DC/DC converters, Single-Active Bridge converter, Neural Network Controller, Feed Forward Neural Network.