DESIGN AND IMPLEMENTATION OF INTELLIGENT CONTROLLERS ON NONLINEAR SYSTEMS

Abstract

The operation of robotic systems to execute complex tasks within dynamic environments represents a critical and challenging area of modern control systems engineering. As robotics, artificial intelligence and autonomous systems continue to advance, their applications across various sectors of society multiply, offering a wide array of opportunities and introducing significant risks. These opportunities and risks are particularly pronounced in areas such as path tracking, speed control and balance control—factors that are deeply influenced by the inherent complexity and unpredictability of real-world environments. To ensure that robotic systems can operate autonomously without succumbing to collisions, disturbances, or operational failures, it becomes imperative to monitor and optimize the parameters governing both mechanical and electronic components, thereby enhancing system reliability and performance. This research delves into the integration of intelligent control approaches and sophisticated optimization algorithms aimed at achieving advanced path tracking, robust balance control, continuous system monitoring and overall robustness, all while relying solely on on-board computing resources. The focus of this study is on the development and implementation of control strategies that are both adaptive and resilient, capable of handling the uncertainties and nonlinearities that typify real-world environments. These strategies are specifically tailored for two-degree-of-freedom (2- DOF) operations within benchmark systems such as the ball balancer and helicopter systems, which serve as practical examples of the challenges faced by modern control systems. The design of these controllers begins with the application of feedback linearization techniques in conjunction with classical control methodologies. This foundational iv approach is subsequently enhanced by incorporating intelligent controllers designed to improve robustness and adaptability in the face of unpredictable disturbances. The research provides a comprehensive overview of the dynamic equations that govern these systems, offering essential insights for control system designers who seek to understand the physical behaviour underlying the mathematical models. This theoretical foundation is followed by a detailed exposition of the mathematical techniques employed to augment the basic control laws, emphasizing the robust methodologies that make the system more resilient to external and internal perturbations. One of the primary challenges identified in current control practices is the limited capacity of conventional controllers to handle nonlinearities and uncertainties inherent in dynamic environments. To address this, the research proposes an intelligent control approach for both the ball balancer and helicopter systems. This approach utilizes a fuzzy-proportional-integral-derivative (Fuzzy-PID) controller, which is adept at managing the position control of the ball balancer and the trajectory tracking of the helicopter. The fuzzy logic component of the controller enhances its ability to deal with uncertainties, while the PID elements ensure precise and responsive control. To further optimize the performance of this controller, the research introduces a novel teaching-learning-based optimization (TLBO) algorithm. This algorithm improves upon existing methods by addressing transparency issues in the literature, thereby providing a more reliable and effective optimization process. Moreover, the study develops a hybrid optimization algorithm (HGPCTLBO) designed to optimize the parameters of a hybrid controller under conditions of random uncertainty. This hybrid controller combines classical control techniques with intelligent methodologies, resulting in a system that is both robust and flexible. The optimization process is tailored to the unique demands of both the helicopter and ball balancer systems, ensuring that the control parameters are finely tuned to achieve optimal performance under varying operational conditions. The optimization of the constraint parameters for both the classical controller and the hybrid classical-intelligent controller is carried out using a novel hybrid Giza pyramid v construction teaching-learning-based optimization (GPC-TLBO) algorithm. This algorithm is specifically designed to handle the complex constraints and performance criteria associated with the two benchmark systems. The effectiveness of the developed control techniques is rigorously validated through comprehensive simulation studies, as well as real-time analysis conducted on the actual systems. These validation processes demonstrate the superior performance and reliability of the proposed methodologies, highlighting their potential for widespread application in advanced robotic systems. In conclusion, this research makes significant contributions to the field of control systems by offering novel optimization approaches to handling the complexities and uncertainties of dynamic environments. The integration of intelligent control techniques with advanced optimization algorithms represents a promising direction for future developments in robotics and autonomous systems, paving the way for more resilient and capable robotic systems that can operate effectively in a wide range of challenging environments.