Dynamic economic load dispatch is one of the most important roles of power generation’s operation and control. It determines the optimal controls of production of generator units with predicted load demand over a certain period of time. Economic dispatch at minimum production cost is one of the most important subjects in the power network’s operation, which is a complicated nonlinear constrained optimization problem. Since dynamic economic load dispatch was introduced, several intelligent methods have been used to solve this problem. In this paper, an Improved Particle Swarm Optimizer (IPSO) and Water Cycle optimizer (WCO), as swarm-based optimization algorithms, have been proposed to solve dynamic economic load dispatch problem and their results compare with each other. These algorithms are applied to a dynamic economic dispatch problem for 6-unit power systems with a 24-h load demand at each one hour time intervals. The goal of the research is categorized in two parts; first of all, introduction of application of new heuristic method for solving economic load dispatch problem and second, comparison between two swarm-based algorithms. Obtained results show that WCO is very fast and also reach to better results and minimum. تفاصيل المقالة

This paper presents a new observer-based control scheme for a class of nonlinear systems. In the proposed method, nonlinear observer and twisting algorithm controller are employed to realize a sensor-less control strategy for complex systems which makes use of non measurable process information instead of installing as many sensors as possible. Due to lack of availability of the complex system states, controlling of them will be faced with undesirable performance. This deficiency can be solved with adding observer in the control strategy. In order to estimate unavailable states, an adaptive neural network observer is considered in the present article. This observer is tuned online and no exact information of the nonlinear function in the observed system is required. Also, to realize control purpose, 2nd order sliding mode controller called twisting algorithm is located in the close loop structure. This control strategy is implemented on the modified Duffing chaotic system and the simulation results confirm the capability of this method. تفاصيل المقالة

In order to improve the optimal performance of a machining process, a booster to improve the serve control system performance with high stability for EDM is needed. According to precise movement of machining process using electrical discharge (EMD), adaptive control is proposed as a major option for accuracy and performance improvement. This article is done to design adaptive controller based on self-tuning regulator (STR) using adaptive online detection methods of gradient MIT and normalized gradient MIT to adjust machine's movement time in the control process. Process performance after controller design shows that determined Gaps location at different points is appropriate and improves machining rates almost 100%. In order to improve the optimal performance of a machining process, a booster to improve the serve control system performance with high stability for EDM is needed. According to precise movement of machining process using electrical discharge (EMD), adaptive control is proposed as a major option for accuracy and performance improvement. This article is done to design adaptive controller based on self-tuning regulator (STR) using adaptive online detection methods of gradient MIT and normalized gradient MIT to adjust machine's movement time in the control process. Process performance after controller design shows that determined Gaps location at different points is appropriate and improves machining rates almost 100%. تفاصيل المقالة