An Optimal GA-Based Backstepping Control Scheme for a MIMO Nonlinear System

Abstract

In the presence of external disturbances, a backstepping control scheme based a genetic algorithm (GA) is built with the objective of tracking a desired trajectory of robot manipulators. The nonlinear-coupled higher order dynamic model of an n-link robot manipulator is first briefly presented. Then, a traditional backstepping control system (BSC) creates a two-link robotic manipulator position tracking control. Furthermore, a mix of nonlinear control and artificial intelligence is suggested for manipulator robot control. To determine the optimal control parameters, the backstepping controller is combined with the genetic algorithm, a metaheuristics-based optimization technique. The effectiveness of the suggested optimal control strategy based on backstepping approach and GA in trajectory tracking problems, such as a good angle tracking and a good disturbance rejection capability, is demonstrated by numerical simulations using the dynamic model of a two-link planar rigid robot manipulator.

Robust Speed Control in Nonlinear Electric Vehicles Using H-Infinity Control and the LMI Approach

“…Such challenges, including the introduction of undesirable variables like modeling uncertainty, the influence of measurement noise, and effects stemming from overlooked nonlinear dynamics, have necessitated the development of highly effective synthesis techniques. It has been observed that these techniques necessitate the synthesis of controllers which strike a balance between robust loop system stability and optimal performance [1]. Importantly, this balance is not merely restricted to the nominal operating speed of the system under control but also extends to numerous conditions wherein the synthetic model's parameters can exhibit significant variability.…”