Volume 1 Issue 1
Authors: Fatemeh Daneshfar
Abstract: In practice, load-frequency control (LFC) systems use proportional-integral (PI) controllers that are designed using a linear model. These controllers are incapable to gain good dynamical performance for a wide range of operating conditions especially in deregulated environments. Also the order of robust controllers is as high as the plant. This gives rise to complex structure of such controllers and there is some reluctancy in industry toward the use of high-order and complex controllers. In this paper, a simple intelligent approach based on reinforcement learning (RL) is proposed for the LFC problem in deregulated environments. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objectives. The new intelligent solution performance has been compared with a powerful robust ILMI- based controller. The resulting controller is shown to minimize the effect of disturbances for a wide range of load changes in the presence of system nonlinearities and has good ability to track the contracted and non-contracted demands.
Keywords: Load Frequency Control; Deregulated Environment; Reinforcement Learning
Authors: Heydar Toosian Shandiz; Sajjad Shoja Majidabad
Abstract: The objective of this paper is to design a terminal sliding mode controller for Lorenz system in discrete-time. First, a discrete model is derived through Taylor series expansion. In the next step, a discrete terminal sliding mode controller (DTSMC) is developed to reach a fast and high precision control. The stability analysis of DTSMC is presented in the presence of external disturbance. Numerical simulations of Lorenz system are shown and compared to illustrate the effectiveness of the proposed control scheme. Finally, the sampling frequency effects on the closed loop system convergence are discussed.
Keywords: Chaotic System; Terminal Sliding Mode; Discrete-Time Sliding Mode
Authors: Heydar Toosian Shandiz; Sajjad Shoja Majidabad
Abstract: This paper deals with the design of robust and model-free controller for an n-link robot manipulator in discrete-time domain. At the first step, a robust and model-based discrete-time sliding mode controller (DSMC) is developed. Afterwards, a discrete-time neuro-fuzzy sliding mode controller (DNFSMC) is proposed to remove the drawbacks of DSMC. In this technique, a neural network (NN) parallel with fuzzy blocks is utilized to approximate the equivalent and switching terms of DSMC. NN weights are updated adaptively with back-propagation algorithm by using the fuzzy system output. Simulation results are illustrated to compare performances of DSMC and DNFSMC in the control of a three-link SCARA robot. Moreover, the sampling time effects on DNFSMC are discussed.
Keywords: SMC; DSMC; DNFSMC; Neural Network (NN); Fuzzy Logic; Sampling Time