Semi-Adaptive Control Systems on Self-Balancing Robot using Artificial Neural Networks

A self-balancing type of robot works on the principle of maintaining the balance of the load's position to remains in the center. As a consequence of this principle, the driver can go forward reverse the vehicle by leaning in a particular direction. One of the factors affecting the control model is the weight of the driver. A control system that has been designed will not be able to balance the system if the driver using the vehicle exceeds or less than the predetermined weight value. The main objective of the study is to develop a semi-adaptive control system by implementing an Artificial Neural Network (ANN) algorithm that can estimate the driver's weight and use this information to reset the gain used in the control system. The experimental results show that the Artificial Neural Network can be used to estimate the weight of the driver's body by using 50-ms-duration of tilt sensor data to categorize into three defined classes that have been set. The ANN algorithm provides a high accuracy given by the results of the confusion matrix and the precision calculations, which show 99%.A self-balancing type of robot works on the principle of maintaining the balance of the load's position to remains in the center. As a consequence of this principle, the driver can go forward reverse the vehicle by leaning in a particular direction. One of the factors affecting the control model is the weight of the driver. A control system that has been designed will not be able to balance the system if the driver using the vehicle exceeds or less than the predetermined weight value. The main objective of the study is to develop a semi-adaptive control system by implementing an Artificial Neural Network (ANN) algorithm that can estimate the driver's weight and use this information to reset the gain used in the control system. The experimental results show that the Artificial Neural Network can be used to estimate the weight of the driver's body by using 50-ms-duration of tilt sensor data to categorize into three defined classes that have been set. The ANN algorithm provides a high accuracy given by the results of the confusion matrix and the precision calculations, which show 99%

A Review on the Development of Fuzzy Classifiers with Improved Interpretability and Accuracy Parameters

This review paper of fuzzy classifiers with improved interpretability and accuracy param-eter discussed the most fundamental aspect of very effective and powerful tools in form of probabilistic reasoning, The fuzzy logic concept allows the effective realization of ap-proximate, vague, uncertain, dynamic, and more realistic conditions, which is closer to the actual physical world and human thinking. The fuzzy theory has the competency to catch the lack of preciseness of linguistic terms in a speech of natural language. The fuzzy theory provides a more significant competency to model humans like com-mon-sense reasoning and conclusion making to fuzzy set and rules as good membership function. Also, in this paper reviews discussed the evaluation of the fuzzy set, type-1, type-2, and interval type-2 fuzzy system from traditional Boolean crisp set logic along with interpretability and accuracy issues in the fuzzy system

Design of an Interval Fuzzy Type-2- PID Controller for a Gas Turbine Power Plant

In this paper, an interval fuzzy type-2 PID controllers are designed for speed and Exhaust temperature in a heavy duty Gas Turbine (HDGT) power plant and the model selected is Rowen’s model to present the mechanical behavior of the gas turbine, the work is aimed to improve the system dynamic performance of speed and Exhaust temperature for a 56.6 MW, 50 HZ, simple cycle, single shaft heavy duty gas turbine, all gains for conventional  PID and interval fuzzy type-2 PID are tuned using Social Spider Optimization(SSO) technique, we show the performance improvement for interval fuzzy type -2 PID controller in comparison with conventional PID via simulation

A low-cost didactic module for testing advanced control algorithms

Los módulos comerciales para el aprendizaje de sistemas de control avanzados no son muy comunes y algunos son muy costosos debido a sus sensores, electrónica y licencia. Hay un área abierta para desarrollar y construir módulos didácticos para mejorar el proceso de aprendizaje mediante la experimentación en sistemas físicos reales. En particular, el péndulo invertido de carro es un sistema físico clásico muy utilizado en las últimas décadas. Proponemos un péndulo invertido de carro llamado MoDiCA-X como un módulo didáctico de bajo costo con hardware y software de código abierto. Es un sistema electromecánico factible de construir y fácil de modificar. Las partes mecánicas del módulo son sólidos impresos en 3D y también se pueden replicar fácilmente. En cuanto a la programación, se puede modificar el control aplicado al sistema, ya que utiliza lenguajes de programación C/C++ que son muy utilizados en la comunidad académica. El módulo está equipado con dos sensores muy comerciales, son fáciles de instalar y quitar; ambos adquieren la actitud de péndulo y la posición del coche. Los actuadores son cuatro motores eléctricos de CC acoplados a las ruedas del automóvil para proporcionar la velocidad y el par adecuados a cada eje de forma independiente. Validamos el rendimiento del módulo aplicando un algoritmo regulador cuadrático lineal multivariable (LQR)

Using a Combination of PID Control and Kalman Filter to Design of IoT-based Telepresence Self-balancing Robots during COVID-19 Pandemic

COVID-19 is a very dangerous respiratory disease that can spread quickly through the air. Doctors, nurses, and medical personnel need protective clothing and are very careful in treating COVID-19 patients to avoid getting infected with the COVID-19 virus. Hence, a medical telepresence robot, which resembles a humanoid robot, is necessary to treat COVID-19 patients. The proposed self-balancing COVID-19 medical telepresence robot is a medical robot that handles COVID-19 patients, which resembles a stand-alone humanoid soccer robot with two wheels that can maneuver freely in hospital hallways. The proposed robot design has some control problems; it requires steady body positioning and is subjected to disturbance. A control method that functions to find the stability value such that the system response can reach the set-point is required to control the robot's stability and repel disturbances; this is known as disturbance rejection control. This study aimed to control the robot using a combination of Proportional-Integral-Derivative (PID) control and a Kalman filter. Mathematical equations were required to obtain a model of the robot's characteristics. The state-space model was derived from the self-balancing robot's mathematical equation. Since a PID control technique was used to keep the robot balanced, this state-space model was converted into a transfer function model. The second Ziegler-Nichols's rule oscillation method was used to tune the PID parameters. The values of the amplifier constants obtained were Kp=31.002, Ki=5.167, and Kd=125.992128. The robot was designed to be able to maintain its balance for more than one hour by using constant tuning, even when an external disturbance is applied to it. Doi: 10.28991/esj-2021-SP1-016 Full Text: PD

Design and experimental validation of a piezoelectric actuator tracking control based on fuzzy logic and neural compensation

This work proposes two control feedback-feedforward algorithms, based on fuzzy logic in combination with neural networks, aimed at reducing the tracking error and improving the actuation signal of piezoelectric actuators. These are frequently used devices in a wide range of applications due to their high precision in micro- and nanopositioning combined with their mechanical stiffness. Nevertheless, the hysteresis is one the main phenomenon that degrades the performance of these actuators in tracking operations. The proposed control schemes were tested experimentally in a commercial piezoelectric actuator. They were implemented with a dSPACE 1104 device, which was used for signal generation and acquisition purposes. The performance of the proposed control schemes was compared to conventional structures based on proportional-integral-derivative and fuzzy logic in feedback configuration. Experimental results show the advantages of the proposed controllers, since they are capable of reducing the error to significant magnitude orders.The authors wish to express their gratitude to the Basque Government, through the project EKOHEGAZ (ELKARTEK KK-2021/00092), to the Diputación Foral de Álava (DFA), through the project CONAVANTER, and to the UPV/EHU, through the project GIU20/063, for supporting this work

Incremental motor skill learning and generalization from human dynamic reactions based on dynamic movement primitives and fuzzy logic system

Different from previous work on single skill learning from human demonstrations, an incremental motor skill learning, generalization and control method based on dynamic movement primitives (DMP) and broad learning system (BLS) is proposed for extracting both ordinary skills and instant reactive skills from demonstrations, the latter of which is usually generated to avoid a sudden danger (e.g., touching a hot cup). The method is completed in three steps. First, ordinary skills are basically learned from demonstrations in normal cases by using DMP. Then the incremental learning idea of BLS is combined with DMP to achieve multi-stylistic reactive skill learning such that the forcing function of the ordinary skills will be reasonably extended into multiple stylistic functions by adding enhancement terms and updating weights of the radial basis function (RBF) kernels. Finally, electromyography (EMG) signals are collected from human muscles and processed to achieve stiffness factors. By using fuzzy logic system (FLS), the two kinds of skills learned are integrated and generalized in new cases such that not only start, end and scaling factors but also the environmental conditions, robot reactive strategies and impedance control factors will be generalized to lead to various reactions. To verify the effectiveness of the proposed method, an obstacle avoidance experiment that enables robots to approach destinations flexibly in various situations with barriers will be undertaken

Assessing the new product development process for the industrial decarbonization of sustainable economies

This study aims to find out the significant stages of new product development process for the industrial decarbonization of sustainable economies by using interval type-2 (IT2) fuzzy decision-making trial and evaluation laboratory (DEMATEL). The findings demonstrate that commercialization is the most significant process of new product generation process of industrial decarbonization. Moreover, it is also concluded that with respect to the sub-criteria, cost analysis, and performance evaluation have the highest weights. An effective cost analysis is required in the new product development process. The costs of the product development process can in some cases be much higher than anticipated. This situation eliminates the effectiveness of the newly developed product. In this context, companies need to make the necessary plans for the costs of these new products correctly. On the other hand, it is important to follow the costs in detail during the process. Otherwise, the product that does not provide a cost advantage will not be preferred by industrial companies. This will cause the actions to be taken to reduce carbon emissions to fail.Key Scientific Research Project of Colleges and Universities in Henan Province "Research on the key role of Investment in the Optimization and upgrading of Industrial structure in Henan Province"Soft Science Research Plan Project of Henan Provinc