Soft-Computing-Based Real-Time Control of Two Wheel Mobile Robot (TWMR)

Kharola, Ashwani; Raiwani, Suyashi; Kharola, Shristi

doi:10.4018/978-1-7998-5788-4.ch017

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…So, the general equation of the manipulator arm by introducing parameters PID controller would be equation (10).…”

Section: Derivative Termmentioning

confidence: 99%

The Efficiency of an Optimized PID Controller Based on Ant Colony Algorithm (ACO-PID) for the Position Control of a Multi-articulated System

Baghli,

Lakhal,

Kadi

2023

Journal of Robotics and Control

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In this article, a robot manipulator is controlled by the PID controller in a closed loop system with unit feedback. The difficulty of using the controller is parameter tuning, because the tuning parameters still use the trial and error method to find the PID parameter constants, namely Proportional Gain (Kp), Integral Gain (Ki) and Derivative Gain (Kd). In this case the Ant colony Optimization algorithm (ACO) is used to find the best gain parameters of the PID. The Ant algorithm is a method of combinatorial optimization, which utilizes the pattern of ants search for the shortest path from the nest to the place where the food is located, this concept is applied to tuning PID parameters by minimizing the objective function such that the robot manipulator has improved performance characteristics. This work uses the Matlab Simulink environment, First, after obtaining the system model, the ant colony algorithm is used to determine the proper coefficients 𝐾p, 𝐾i, and Kd in order to minimize the trajectory errors of the two joints of the robot manipulator. Then, the parameters will implement in the robot system. According to the results of the computer simulations, the proposed method (ACO-PID) gives a system that has a good performance compared with the classical PID.

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“…So, the general equation of the manipulator arm by introducing parameters PID controller would be equation (10).…”

Section: Derivative Termmentioning

confidence: 99%

The Efficiency of an Optimized PID Controller Based on Ant Colony Algorithm (ACO-PID) for the Position Control of a Multi-articulated System

Baghli,

Lakhal,

Kadi

2023

Journal of Robotics and Control

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“…This part provides adaptive neural control for TWMR system. An existing controller is required to construct a supervised neural controller [30], [31], [32]. Since the feedback controller (LQRICOSE) has already been created, this controller can be utilized as a reference for neural network (neuro controller).…”

Section: Adaptive Neural Network In Controlmentioning

confidence: 99%

Adaptive Neural Networks Based Robust Output Feedback Controllers for Nonlinear Systems

Abougarair

Aburakhis

Edardar

2022

IJRCS

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The performance of the nonlinear control system that is subjected to uncertainty, can be enhanced by implementing an adaptive approach by using the robust output-feedback control and the artificial intelligence neural network. This paper seeks to utilize output feedback control for nonlinear system using artificial intelligence employing neural network. The Two Wheel Mobile Robot (TWMR) is treated as a multi-body dynamic system. The nonlinear swing-up problem is handled by designing an adaptive neural network, which is trained using a modified conventional controller called Linear Quadratic Optimal State Estimator with Integral Control (LQOSEIC). In this paper, the nonlinear system TWMR is stabilized utilizing a robust output feedback control called LQOSEIC. This controller allows a linearized model to emulate a model reference for the original nonlinear system. However, it works for a limited range of operations and will fail if the plant characteristics are unknown or uncertain. An adaptive neural network is used to overcome this problem. The adaptive neural controller is trained offline using LQOSEIC to obtain the initial weights of neurons for the network's hidden layers. After finishing the training, the LQOSEIC will be replaced by the adaptive neural controller. The main advantage of a neuro-controller is its ability to update the weights online depending on the error signal. If there are any disturbances or uncertainties that arises within the concerned nonlinear system, the neuro-controller will be able to handle it because of online learning that compensates for the effect of unpredictable conditions. The proposed adaptive neural network improves control performance and ensures the robust stability of the closed-loop control system. Finally, numerical simulations are used to demonstrate the efficacy of the proposed controllers.

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