Modified Integral Sliding Mode Controller Design based Neural Network and Optimization Algorithms for Two Wheeled Self Balancing Robot

Abstract

Two-wheeled Self-balancing (TWSB) mobile robot is considered to be highly nonlinear and unstable dynamic system. Unstable means that the robot is free to advance forward or backward without any forces applied. It must, therefore, be controlled. The purpose of this work is to design an intelligent nonlinear Modified Integral Sliding Mode Controller (MISMC) based on simple Adaline neural network for balancing a two-wheeled selfbalancing mobile robot, in addition to improve the performance of this robot in tracking the desired trajectory. The simple Adaline neural network is used to enhance the performance of the conventional Integral Sliding Mode Controller (ISMC) which is an effective and powerful technique because it has a high performance. Also, in this work, a Modified Particle Swarm Optimization (MPSO) and Modified Cuckoo Search (MCS) algorithms have been proposed to find and tune the best MISMC parameters and hence enhance the performance characteristics of the robot system by reducing the processing time as well as improving the response accuracy through minimizing the tracking error of the mobile robot. The Integral Square Error (ISE) method has been used as a performance index for the two algorithms (MPSO, MCS) to measure the performance of the proposed controller. Numerical simulations show the efficiency of the suggested controller by handling the balance and tracking problems of the two-wheeled selfbalancing mobile robot. Index Terms-Modified Integral sliding mode controller, Two-wheeled self-balancing mobile robot, modified PSO, modified CS, optimization algorithms, neural network.

Robust Navigational Control of a Two-Wheeled Self-Balancing Robot in a Sensed Environment

“…Their proposed adaptation fuzzy controller was not optimized based on the analysis of dynamic characteristics as shown in [7] to improve the performance of the robot. Karam et al in [8] modified the Integral Sliding mode controller based on Neural Network and Optimization. The work designed an intelligent nonlinear Modified Integral Sliding Mode Controller (MISMC) based on simple Adaline neural network for balancing a twowheeled self-balancing mobile robot and tried to improve the performance of their robot in tracking the desired trajectory.…”

A neural network combined with sliding mode controller for the two-wheel self-balancing robot

“…There are many effective control methods available for the TWSB robot with nonlinear kinetics, such as the method using a proportional integral derivative (PID) controller [2], the optimal controller based on the linear-quadratic regulator (LQR) [3], [4], the robust-adaptive control [5], the backstepping control [6], [7], the sliding mode control [8], the fuzzy logic control structure [9] and the control system based on a neural network [10]. However, there is a lot of noise during outdoor operation that negatively affects the control system's quality.…”