Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Slice Genetic Algorithm


Development of a Swing-Tracking Sliding Mode Controller Design for Nonlinear Inverted Pendulum System via Bees-Slice Genetic Algorithm

Ahmed Sabah Al-Araji

Engineering and Technology Journal, 2016, Volume 34, Issue 15, Pages 2897-2910

A new development of a swing-tracking control algorithm for nonlinear inverted pendulum system presents in this paper. Sliding mode control technique is used and guided by Lyapunov stability criterion and tuned by Bees-slice genetic algorithm (BSGA). The main purposes of the proposed nonlinear swing-tracking controller is to find the best force control action for the real inverted pendulum model in order to stabilize the pendulum in the inverted position precisely and quickly. The Bees-slice genetic algorithm (BSGA) is carried out as a stable and robust on-line auto-tune algorithm to find and tune the parameters for the sliding mode controller. Sigmoid function is used as signum function for sliding mode in order to eliminate the chattering effect of the fast switching surface by reducing the amplitude of the function output. MATLAB simulation results and LabVIEW experimental work are confirmed the performance of the proposed tuning swing-tracking control algorithm in terms of the robustness and effectiveness that is overcame the undesirable boundary disturbances, minimized the tracking angle error to zero value and obtained the smooth and best force control action for the pendulum cart, with fast and minimum number of fitness evaluation.

A Nonlinear Neural Controller Design for the Single Axis Magnetic Ball Levitation System Based on Slice Genetic Algorithm

Ahmed Sabah Al-Araji; Ahmed Ibraheem Abdulkareem

Engineering and Technology Journal, 2016, Volume 34, Issue 1, Pages 22-32

This paper presents a ball position tracking control tuning algorithm for single axis magnetic levitation system using slice genetic optimization technique based nonlinear neural controller. As simple and fast tuning technique, slice genetic optimization algorithm is used to tune the nonlinear neural controller's parameters in order to get the best control action for the magnetic levitation system through the tracking of pre-defined location of the steel ball. Pollywog wavelet activation function is used in the structure of the nonlinear neural controller. The obtained results (using MATLAB program) show that the effectiveness of the proposed controller in minimizing the tracking error to zero value and also, in the softness of the control action with the lowest amount of fitness evaluation number.

A Cognitive PID Neural Controller Design for Mobile Robot Based on Slice Genetic Algorithm

Ahmed Sabah Al-Araji

Engineering and Technology Journal, 2015, Volume 33, Issue 1, Pages 208-222

The main core of this paper is to design a trajectory tracking control algorithm for mobile robot using a cognitive PID neural controller based slice genetic optimization in order to follow a pre-defined a continuous path. Slice Genetic Optimization Algorithm (SGOA) is used to tune the cognitive PID neural controller's parameters in order to find best velocities control actions of the right wheel and left wheel for the mobile robot. Pollywog wavelet activation function is used in the structure of the cognitive PID neural controller. Simulation results and experimental work show the effectiveness of the proposed cognitive PID neural tuning control algorithm; This is demonstrated by the minimized tracking error and the smoothness of the velocity control signal obtained, especially with regards to the external disturbance attenuation problem.