INVESTIGATIONS ON MODELING AND CONTROL OF NONLINEAR SYSTEMS

Abstract

The operation of nonlinear systems to perform complex tasks in dynamic environments has been a crucial area of control. Further, the technological advancements in autonomy, artificial intelligence, and robotics have broad applications across society, bringing both opportunities and risks. Most of these opportunities are directly related to path tracking, speed control, maneuverability, and balancing control which is highly affected by the complexity and unpredictable dynamics of the surroundings. Besides, efficient path tracking and balancing control are particularly important for the robots, in order to achieve autonomy without any collision and disturbances. Consequently, the parameters of the mechanical and electronic components need to be monitored and optimized for performing multiple tasks and maintaining the reliability of the system. In view of these aspects, this research identified the combination of intelligent approaches and machine learning methods to achieve unprecedented path tracking and balancing control, continuous monitoring, and robustness by relying solely on onboard computing. The approaches are developed based on multiple control algorithms and are implemented with two-degree freedom operation of ball balancer, inverted pendulum, TORA system, and two-degree freedom robotic manipulator. To assess the performance of the various nonlinear systems the different control techniques like sliding mode controller, fuzzy sliding mode controller, and neural sliding mode controller are investigated. A comparative study in terms of setpoint response analysis, convergence analysis, statistical analysis, and trajectory analysis has been done.