Real Time Implementation of Hybrid Control Optimisation Techniques for Inverted Pendulum Using Evolutionary Algorithms

Abstract

The classical inverted pendulum or cart-pole is both unstable and non-linear and is used to test out the methods in control system. This model problem is taken up in this research to demonstrate the optimization of Fuzzy logic controller using compact Genetic Algorithms (CGA), Extended Compact Genetic Algorithm (eCGA), Particle Swarm Optimisation (PSO) and Deep Q learning. The stabilization of the pole on the cart has been studied for moving inverted pendulum. newlineThe fundamental research objective of this work is to enhance the wealth of the robotic benchmark inverted pendulum and provides recent trend developments in nonlinear control theory using rich nonlinear model. newlineand#61623; Stabilize Inverted pendulum by using various simulation methods outlined earlier and the results from these simulations are compared. newlineand#61623; A more in-depth study on how the various parameters and encodings affect GA performance in FLC optimization. newlineand#61623; Investigate more thoroughly if GAs can be applied to FLCs or other types of nonlinear controllers so that they may successfully control completely the inverted pendulum system. This may then lead eventually to the building of a real-life controller. Perhaps by limiting the range of the parameters using heuristics, GAs with faster performance may be found. newlineand#61623; Search for a more sophisticated neural network algorithm for reducing the error of pole angle, position error of inverted pendulum. newlineand#61623; Apply eCGA, PSO techniques for optimization of results achieved by hybrid control methods. newlineand#61623; Deep Learning Methods tested using apt algorithms. newline