The primary challenge faced by a neural controller in the dynamic model of a mobile robot lies in its ability to address the inherent complexity of the system dynamics. Given that mobile robots exhibit nonlinear movements and are subject to diverse environmental conditions, they contend with a challenging dynamic environment. The neural controllers must demonstrate the capability to continuously adapt and effectively learn to manage the variability present in the dynamic of the robot. This paper presents two intelligent controllers utilizing neural networks, showcasing their relevance in the field of robotics. The first controller, referred to as the neural PID (PIDN), integrates the traditional PID controller with a neural component. The second controller leverages the dynamic model of a differential robot to improve trajectory tracking, employing a parallel architecture that combines PID with neural networks (PID+NN). Our proposals adhere to a cascading structure, where the outer loop takes the lead in reducing position errors through a kinematic controller, while concurrently, the inner loop is employed to regulate linear and angular velocities through the proposed controllers. The controllers are validated in the CoppeliaSIM simulator, offering a realistic setting for evaluating the behavior of the chosen Pioneer 3-DX robot. To comprehensively assess controller performance, three strategies are examined: PIDN, PID+NN, and the conventional PID. Through a blend of qualitative and quantitative analyses, employing diverse performance metrics, the advantages of our proposed controllers become apparent. Doi: 10.28991/ESJ-2024-08-04-01 Full Text: PDF

This paper describes a detailed methodology that allows gesture recognition performed with the upper limbs of the human body, based on Dynamic Time Warping (DTW) technique using a Kinect in order to control a slide-show. Several involved parameters on the DTW algorithm will be analyzed allowing an accurate recognition, considering variations on the gesture velocity and even if subject's physical shape differ. A software was developed in Microsoft Visual C# to show and test how different parameters affects the performance of the algorithm. Finally an accuracy of 84.07% was obtained in this approach.

This paper presents an optimal fuzzy PD + I controller designed to control the trajectory of a mobile robot. To tune and obtain the best parameters of the controller, the PSO (Particle Swarm Optimization) algorithm is used. This algorithm minimizes an objective function, in this case, the Integral Square Error (ISE) index of the system, parameter that depends on the linear and angular velocity errors for the optimization process. With this algorithm, the parameters to be optimized are the gains and the membership functions of the fuzzy controller in order to obtain an optimal controller and improve its performance. Finally, in order to validate the proposal, a comparative analysis among the results of a classic PID controller, a conventional Fuzzy PD + I controller and the proposed optimal Fuzzy PD + I controller is performed, evaluating trajectories followed by the robot, the position errors and the control actions of each case.

Three-phase induction motors are widely used in the industrial field due to their low cost and robustness; therefore, it is essential to continuously develop new proposals that improve their behavior and response in applications where speed control is required. This paper proposes the development of an intelligent controller programmed in a PLC and interconnected with a three-phase induction motor through a VFD. The novel intelligent controller bases its operation on the LAMDA algorithm, which acts as a decision-making system based on the state of the error with respect to the speed reference and its derivative, obtaining a closed-loop controller. In addition, the VFD receives commands from the PLC to operate the motor at a constant voltage-frequency ratio in which flux remains constant. The proposed controller has been validated in two study cases: i) reference changes and ii) rejection of disturbances. The results obtained are promising and show a good performance of the LAMDA controller when compared qualitatively and quantitatively with the controller most commonly used in industrial systems, such as PID, and controllers with similar characteristics, such as fuzzy, based on Mamdani and Takagi-Sugeno inference. Doi: 10.28991/ESJ-2023-07-03-01 Full Text: PDF

This paper presents a novel soft computing-based machine learning technique designed to enhance the trajectory tracking capabilities of mobile robots through the application of neural networks. The goal of this approach is to enhance the accuracy and overall performance of trajectory tracking without the need for manual gain recalibration, which is a tedious and time-consuming task for the designer when setting up the robot. This improvement is achieved by creating a kinematic controller based on neural networks, which are constructed using the kinematic model of the robot. In the initial phase, the controller requires gains defined by the designer. Subsequently, during the application phase, the backpropagation algorithm is used to dynamically adjust the gains of the neural network, aiming to minimize the closed-loop error. One of the key innovations introduced by this controller is the potential for automatic online gain tuning, thereby eliminating the need for a pre-learning phase, typically required by traditional neural controllers. To validate the effectiveness of this approach, the results are systematically analyzed and compared against those obtained using a conventional kinematic controller. Performance metrics reveal the improved precision in trajectory tracking achieved by the controller, with reduced effort, highlighting the performance enhancements in different trajectories. Doi: 10.28991/ESJ-2023-07-06-01 Full Text: PDF

This paper presents the development and evaluation of a dynamic gain controller utilizing neural networks to enhance trajectory tracking performance in the NAO humanoid robot. The proposed controller employs a differential kinematic model and dynamically adjusts its gains using a backpropagation algorithm, eliminating the need for manual gain tuning and simplifying the robot's setup process. Experimental validation was conducted in a simulated environment using CoppeliaSim, with the NAOqi library facilitating integration. The analysis results demonstrate that the dynamic controller using a neural network provides better trajectory tracking accuracy than the traditional kinematic controller. Adaptability of the dynamic controller, which adjusts gain parameters in real-time, contributes to improved robustness and precision across various trajectory types. These findings demonstrate the potential of dynamic, self-tuning controllers in enhancing the performance, efficiency, and versatility of humanoid robots in complex navigation tasks. Doi: 10.28991/ESJ-2025-09-02-02 Full Text: PDF