Artificial Control of Nonlinear Second Order Systems Based on AFGSMC

Abstract

Abstract: Design robust controller for uncertain nonlinear systems most of time can be a challenging work. One of the most active research areas in this field is control of the nonlinear second order system. The control strategies for nonlinear systems are classified in two main groups: classical and non-classical methods, where the classical methods use the conventional control theory and non-classical methods use the artificial intelligence theory. Control nonlinear systems using pure classical controllers are often having lots of problems because most of time these systems have unknown variations in the parameters and have a large uncertainty. Artificial control such as Fuzzy logic, neural network, genetic algorithm and neurofuzzy control have been applied in many applications. Therefore, stable control of nonlinear dynamic systems is challenging because of some mentioned issues. In this paper the intelligent control of nonlinear second order system such as robot manipulator using Adaptive Fuzzy Gain Scheduling Sliding Mode Controller (AFGSMC) and comparison to Adaptive Fuzzy Inference System (AFIS) and various performance indices like the RMS error, Steady state error, trajectory performance, disturbance rejection and chattering are used for test the controller performance. Key words: Uncertain nonlinear systems • classical control • non-classical control • fuzzy logic • intelligent control • robot manipulator • adaptive fuzzy gain scheduling sliding mode controller • adaptive fuzzy inference system • rms error • steady state error • chattering