Neural Networks Application for the Data of PID Controller for Acrobot

Abstract

Acrobots are a system that has levels of operating states in many investigated cases, and they are subjects to many events during operation due to the mechanisms of locomotion processes. These states have been investigated in specific situations. Due to the limited nature of surveying under conditions without the aid of software fine-tuning the desired output values, designers have to create a number of algorithms that control the system most appropriately in a complex working environment of this system. In this study, the author has proposed design problems to suit the working needs. The author has modeled the objects to be investigated, and at the same time, the author has combined simulation of phenomena that have been documented from the theoretical model of this system before. Control an Acrobot system, including Neural Network Application for the data of the PID controller (closed form). Mathematical models, Simulink, are also presented specifically through this study. The simulation parameters have been adjusted to match the set criteria, and the reader’s perception will be more intuitive through simulation interpretations. Based on simulation data, the system performance analysis becomes more accurate than ever. The above suggestions are intended to serve vocational education and scientific research, and at the same time, they also contribute to suggesting new and unique ideas. The ANN is the most intelligent control method currently added in this study to firmly confirm its effectiveness in all problems related to artificial intelligence. Proposing control strategies for different models is also suggested by the author in the conclusion. Neural networks application is a highly applicable method because it works in sync with other methods. In other words, neural networks application can be applied to any situation based on given data. Specifically, in this study, the application of neural networks becomes more flexible and vivid thanks to the data of the PID controller. The purpose of this method is to increase the security of the system against the attack of hackers on facilities of the automatic control system. Simulation is done by Matlab.

Control of a Novel Parallel Mechanism for the Stabilization of Unmanned Aerial Vehicles

“…Recently, soft computing techniques such as neural networks have been successfully used in control systems. The use of artificial intelligence (AI) to replace the traditional PID controller can significantly simplify the tuning process and improve the overall performance and robustness of the control system [27][28][29]. Ghoniem et al [30] replaced a PID controller with a neural network in order to control a new low-cost semi-active vehicle suspension system, and it proved to have good accuracy in terms of vibration reduction and response time.…”

A Survey of LQE and MPC Discrete-Control for Acrobot

“…Reinforcement Learning (RL) to classical PID [2] controllers and Fuzzy Logic [3], have been employed to navigate the multifaceted control landscape of the Acrobot, each yielding its own set of insights and challenges. RL, while notable for its adaptive capabilities, often demands substantial computational resources and training time, whereas PID controllers and Fuzzy Logic, despite their computational efficiency and simplicity, may struggle to maintain stability due to the Acrobot's non-linearities and underactuated dynamics.…”