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Journal articles on the topic 'Neural controller'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Karam, Ekhlas H., Ayam M. Abbass, and Noor S. Abdul-Jaleel. "Design of Hybrid Neural Fuzzy Controller for Human Robotic Leg System." Al-Khwarizmi Engineering Journal 14, no. 1 (2018): 145–55. http://dx.doi.org/10.22153/https://doi.org/10.22153/kej.2018.08.007.

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In this paper, the human robotic leg which can be represented mathematically by single input-single output (SISO) nonlinear differential model with one degree of freedom, is analyzed and then a simple hybrid neural fuzzy controller is designed to improve the performance of this human robotic leg model. This controller consists from SISO fuzzy proportional derivative (FPD) controller with nine rules summing with single node neural integral derivative (NID) controller with nonlinear function. The Matlab simulation results for nonlinear robotic leg model with the suggested controller showed that

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Karam, Ekhlas H., Ayam M. Abbass, and Noor S. Abdul-Jaleel. "Design of Hybrid Neural Fuzzy Controller for Human Robotic Leg System." Al-Khwarizmi Engineering Journal 14, no. 1 (2018): 145–55. http://dx.doi.org/10.22153/kej.2018.08.007.

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In this paper, the human robotic leg which can be represented mathematically by single input-single output (SISO) nonlinear differential model with one degree of freedom, is analyzed and then a simple hybrid neural fuzzy controller is designed to improve the performance of this human robotic leg model. This controller consists from SISO fuzzy proportional derivative (FPD) controller with nine rules summing with single node neural integral derivative (NID) controller with nonlinear function. The Matlab simulation results for nonlinear robotic leg model with the suggested controller showed that

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Passold, Fernando. "Applying RBF Neural Nets for Position Control of an Inter/Scara Robot." International Journal of Computers Communications & Control 4, no. 2 (2009): 148. http://dx.doi.org/10.15837/ijccc.2009.2.2422.

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This paper describes experimental results applying artificial neural networks to perform the position control of a real scara manipulator robot. The general control strategy consists of a neural controller that operates in parallel with a conventional controller based on the feedback error learning architecture. The main advantage of this architecture is that it does not require any modification of the previous conventional controller algorithm. MLP and RBF neural networks trained on-line have been used, without requiring any previous knowledge about the system to be controlled. These approach

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Jiang, Yiming, Chenguang Yang, Min Wang, Ning Wang, and Xiaofeng Liu. "Bioinspired control design using cerebellar model articulation controller network for omnidirectional mobile robots." Advances in Mechanical Engineering 10, no. 8 (2018): 168781401879434. http://dx.doi.org/10.1177/1687814018794349.

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As a learning mechanism that emulates the structure of the cerebellum, cerebellar model articulation controllers have been widely adopted in the control of robotic systems because of the fast learning ability and simple computational structure. In this article, a cerebellar model articulation controller–based neural network controller is developed for an omnidirectional mobile robot. With the powerful learning ability of cerebellar model articulation controller, a cerebellar model articulation controller neural network is constructed to learn the complex dynamics of the omnidirectional mobile

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Joshi, Girisha, and Pinto Pius A J. "ANFIS controller for vector control of three phase induction motor." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (2020): 1177. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1177-1185.

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For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis

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Girisha, Joshi, and Pius A. J. Pinto. "ANFIS controller for vector control of three phase induction motor." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 19, no. 3 (2020): 1177–85. https://doi.org/10.11591/ijeecs.v19.i3.pp1177-1185.

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For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis

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Jung, Seul, and T. C. Hsia. "Neural network inverse control techniques for PD controlled robot manipulator." Robotica 18, no. 3 (2000): 305–14. http://dx.doi.org/10.1017/s0263574799002064.

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In this paper neural network (NN) control techniques for non-model based PD controlled robot manipulators are proposed. The main difference between the proposed technique and the existing feedback error learning (FEL) technique is that compensation of robot dynamics uncertainties is done outside the control loop by modifying the desired input trajectory. By using different NN training signals, two NN control schemes are developed. One is comparable to that in the FEL technique and another has to deal with the Jacobian of the PD controlled robot dynamic system. Performances of both controllers

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Elgargani, T. N., A. A. Hudoud, and S. O. Abid. "IMPROVEMENT AND CONTROL OF THE SPEED RESPONSE OF THE PERMANENT MAGNET SYNCHRONOUS MOTOR DRIVE USING A FUZZY – PI CONTROLLER." Journal of Science and Technology 30, no. 5 (2025): 25–36. https://doi.org/10.20428/jst.v30i5.2812.

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High-speed and high-performance electric motors are designed to reach a high level of demand control. The permanent magnet synchronous motors (PMSMs) drive has a non-linear model that is not easy to deal with using traditional control methods when controlling the three phase motors because of their nature, (intricate highly non-linear model). Therefore, neural networks controllers compared with fuzzy logic controllers (FLCs) are getting more attention among researchers, as they can be used for such systems. The neural networks controller relies on training of this mathematical model, and the f

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Zada, Fatma, Shawket K. Guirguis, and Walied M. Sead. "Hybrid Neural-Fuzzy Controller for Motorized Robot Arm." Advanced Materials Research 403-408 (November 2011): 5068–75. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.5068.

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In this study, a design methodology is introduced that blends the neural and fuzzy logic controllers in an intelligent way developing a new intelligent hybrid controller. In this design methodology, the fuzzy logic controller works in parallel with the neural controller and adjusting the output of the neural controller. The performance of our proposed controller is demonstrated on a motorized robot arm with disturbances. The simulation results shows that the new hybrid neural -fuzzy controller provides better system response in terms of transient and steady-state performance when compared to n

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Nguyen, Tat-Bao-Thien, Teh-Lu Liao, Hang-Hong Kuo, and Jun-Juh Yan. "An Improved Adaptive Tracking Controller of Permanent Magnet Synchronous Motor." Abstract and Applied Analysis 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/987308.

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This paper proposes a new adaptive fuzzy neural control to suppress chaos and also to achieve the speed tracking control in a permanent magnet synchronous motor (PMSM) drive system with unknown parameters and uncertainties. The control scheme consists of fuzzy neural and compensatory controllers. The fuzzy neural controller with online parameter tuning is used to estimate the unknown nonlinear models and construct linearization feedback control law, while the compensatory controller is employed to attenuate the estimation error effects of the fuzzy neural network and ensure the robustness of t

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Silva, Bruno E., and Ramiro S. Barbosa. "Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform." Applied Sciences 11, no. 6 (2021): 2535. http://dx.doi.org/10.3390/app11062535.

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In this article, we designed and implemented neural controllers to control a nonlinear and unstable magnetic levitation system composed of an electromagnet and a magnetic disk. The objective was to evaluate the implementation and performance of neural control algorithms in a low-cost hardware. In a first phase, we designed two classical controllers with the objective to provide the training data for the neural controllers. After, we identified several neural models of the levitation system using Nonlinear AutoRegressive eXogenous (NARX)-type neural networks that were used to emulate the forwar

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Marrero, Dailin, John Kern, and Claudio Urrea. "A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks." Sensors 24, no. 2 (2024): 491. http://dx.doi.org/10.3390/s24020491.

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This paper investigates spiking neural networks (SNN) for novel robotic controllers with the aim of improving accuracy in trajectory tracking. By emulating the operation of the human brain through the incorporation of temporal coding mechanisms, SNN offer greater adaptability and efficiency in information processing, providing significant advantages in the representation of temporal information in robotic arm control compared to conventional neural networks. Exploring specific implementations of SNN in robot control, this study analyzes neuron models and learning mechanisms inherent to SNN. Ba

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Sathishkumar, H., та S. S. Parthasarathy. "Identification of robust controller for 3hp 3Φ induction motor". IAES International Journal of Robotics and Automation (IJRA) 8, № 2 (2019): 125–32. https://doi.org/10.11591/ijra.v8i2.pp125-132.

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This paper deals about the identification of robust controller for 3hp 3Φ induction motor which is used in cable industry (Ravicab cables private limited) at Bidadi. In this cable industry 3hp 3Φ induction motor is used for cable pulling purpose. This industry is using PID (Proportional derivative integral) controller based VFD (Voltage frequency drive) for controlling the speed of this 3hp 3Φ induction motor. This VFD is not functioning well for the non linear load and disturbance environment. Therefore in this paper Neural network based speed controller is proposed as proposed co

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Leal, Hugo M., Ramiro S. Barbosa, and Isabel S. Jesus. "Control of a Mobile Line-Following Robot Using Neural Networks." Algorithms 18, no. 1 (2025): 51. https://doi.org/10.3390/a18010051.

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This work aims to develop and compare the performance of a line-following robot using both neural networks and classical controllers such as Proportional–Integral–Derivative (PID). Initially, the robot’s infrared sensors were employed to follow a line using a PID controller. The data from this method were then used to train a Long Short-Term Memory (LSTM) network, which successfully replicated the behavior of the PID controller. In a subsequent experiment, the robot’s camera was used for line-following with neural networks. Images of the track were captured, categorized, and used to train a co

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Puentes, Kevin, Luis Morales, David F. Pozo-Espin, and Viviana Moya. "Enhancing Control Systems with Neural Network-Based Intelligent Controllers." Emerging Science Journal 8, no. 4 (2024): 1243–61. http://dx.doi.org/10.28991/esj-2024-08-04-01.

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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 f

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Gücüyener, İsmet. "Fuzzy Neural-Network-Based Controller." Solid State Phenomena 220-221 (January 2015): 407–12. http://dx.doi.org/10.4028/www.scientific.net/ssp.220-221.407.

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Using a controller is necessary for any automation system. The controller must be cheap, reliable, user friendly and not cause any problems for inputs and outputs. Classical control systems like proportional integral derivative (PID) put adequate results of linear systems and continuous-time. In fact, real control systems are time-variant, with non-linearity and poorly calculated dynamic variables. For this reason, conventional control systems need an expert person to adjust controller parameters in general. Sometimes an operator is required to solve control problems. Human control is not comp

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Günther, Johannes, Elias Reichensdörfer, Patrick M. Pilarski, and Klaus Diepold. "Interpretable PID parameter tuning for control engineering using general dynamic neural networks: An extensive comparison." PLOS ONE 15, no. 12 (2020): e0243320. http://dx.doi.org/10.1371/journal.pone.0243320.

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Modern automation systems largely rely on closed loop control, wherein a controller interacts with a controlled process via actions, based on observations. These systems are increasingly complex, yet most deployed controllers are linear Proportional-Integral-Derivative (PID) controllers. PID controllers perform well on linear and near-linear systems but their simplicity is at odds with the robustness required to reliably control complex processes. Modern machine learning techniques offer a way to extend PID controllers beyond their linear control capabilities by using neural networks. However,

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Ly, Trinh Thi Khanh, Nguyen Thi Thanh, Hoang Thien, and Thai Nguyen. "A Neural Network Controller Design for the Mecanum Wheel Mobile Robot." Engineering, Technology & Applied Science Research 13, no. 2 (2023): 10541–47. http://dx.doi.org/10.48084/etasr.5761.

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Advanced controllers are an excellent choice for the trajectory tracking problem of Wheeled Mobile Robots (WMRs). However, these controllers pose a challenge to the hardware structure of WMRs due to the controller's complex structure and the large number of calculations needed. In that context, designing a controller with a simple structure and a small number of computations but good real-time performance is necessary in order to improve the tracking accuracy for the WMRs without requiring high hardware architecture. In this work, a neural network controller with a simple structure for the tra

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GÖKÇE, Celal Onur. "Intelligent Quadcopter Control Using Artificial Neural Networks." Afyon Kocatepe University Journal of Sciences and Engineering 23, no. 1 (2023): 138–42. http://dx.doi.org/10.35414/akufemubid.1229424.

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An advanced controller architecture and design for quadcopter control implementation is proposed in this study. Instead of using only the error information as input to the controller, reference and measured outputs are used separately independent from each other. This enhances the performance of the controller of quadcopter being a highly non-linear platform. In this study single layer neural network is directly used as a controller. A complex controller is grown from an initially simple PID controller. This elevates the need for time consuming search in huge parameter space due to very high d

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Guarnizo Marin, José Guillermo, Nelson Díaz Aldana, and César Trujillo Rodríguez. "Design and implementation of an Inverse Neural Network Controller applied To VSC Converter for active and reactive Power Flow, based on regions of work." Revista Facultad de Ingeniería Universidad de Antioquia, no. 72 (August 5, 2014): 20–34. http://dx.doi.org/10.17533/udea.redin.15045.

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Voltage Source Converter (VSC) usually used in High Voltage Direct Current (HVDC) systems, where a VSC can be used as inverter or rectifier. VSC systems allow the independent control of active or reactive power flow using different techniques. VSC systems present nonlinear behaviors, multiple inputs and multiple outputs, therefore nonlinear controllers can be used to obtain an adequate behavior. Inverse Neural Control is an alternative of an intelligent control since a mathematical model of the system is not required for designing controllers. Additionally, Inverse Neural Control can easily ma

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Mureşan, Vlad, Mihail Abrudean, Honoriu Vălean, Mihaela Ligia Ungureşan, and Tiberiu Coloşi. "Tuning a Neural Controller with Distributed Parameters." Applied Mechanics and Materials 555 (June 2014): 341–51. http://dx.doi.org/10.4028/www.scientific.net/amm.555.341.

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In this paper, a solution for the control of a distributed parameter process, using a neural distributed parameter controller is presented. The main originality elements from the paper are the introducing of the distributed parameter controller concept, respectively the including of this type of controller in a control structure. In the paper, a method for tuning a distributed parameter controller is presented, taken as example the pH control system associated to a blunting process. Also, due to the complex structure of the controller two neural networks are used for its implementation. In ord

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N. Abd, Aula. "Adaptive Inverse Neural Network Based DC Motor Speed and Position Control Using FPGA." DJES 11, no. 3 (2018): 71–78. http://dx.doi.org/10.24237/djes.2018.11311.

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In this research two types of controllers are designed in order to control the speed and position of DC motor. The first one is a conventional PID controller and the other is an intelligent Neural Network (NN) controller that generate a control signal DC motor. Due to nonlinear parameters and movable laborers such saturation and change in load a conventional PID controller is not efficient in such application; therefore neural controller is proposed in order to decreasing the effect of these parameter and improve system performance. The proposed intelligent NN controller is adaptive inverse ne

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Mani Nepal, Subhamyu, Madan Prasad Sapkota, Nischal Shrestha, Sunil Karki, and Manoj Raj Dhungana. "Comparison Between Conventional PID Controller and Neural Network PID Controller Based on DC Motor Speed Control." KEC Journal of Science and Engineering 8, no. 1 (2024): 44–47. http://dx.doi.org/10.3126/kjse.v8i1.69264.

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DC motor is a multivariable, strong coupling and nonlinear controlled system, and is difficult to setting up the accurate mathematical model. So, it cannot achieve good control effect using traditional PID control method. To tackle this problem ANN based PID controller is proposed in this paper. The objective here is to compare the response of DC motor controlled by two different PID controllers.

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Woodford, Grant W., and Mathys C. du Plessis. "Complex Morphology Neural Network Simulation in Evolutionary Robotics." Robotica 38, no. 5 (2019): 886–902. http://dx.doi.org/10.1017/s0263574719001140.

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SUMMARYThis paper investigates artificial neural network (ANN)-based simulators as an alternative to physics-based approaches for evolving controllers in simulation for a complex snake-like robot. Prior research has been limited to robots or controllers that are relatively simple. Benchmarks are performed in order to identify effective simulator topologies. Additionally, various controller evolution strategies are proposed, investigated and compared. Using ANN-based simulators for controller fitness estimation during controller evolution is demonstrated to be a viable approach for the high-dim

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Darsivan, Fadly Jashi, Wahyudi Martono, and Waleed F. Faris. "Active Engine Mounting Control Algorithm Using Neural Network." Shock and Vibration 16, no. 4 (2009): 417–37. http://dx.doi.org/10.1155/2009/257480.

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This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the c

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Lippe, Wolfram-M., Steffen Niendieck, and Andreas Tenhagen. "On the Optimization of Fuzzy-Controllers by Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (1999): 158–63. http://dx.doi.org/10.20965/jaciii.1999.p0158.

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Methods are known for combining fuzzy-controllers with neural networks. One of the reasons of these combinations is to work around the fuzzy controllers’ disadvantage of not being adaptive. It is helpful to represent a given fuzzy controller by a neural network and to have rules adapted by a special learning algorithm. Some of these methods are applied in the NEFCONmode or the model of Lin and Lee. Unfortunately, none adapts all fuzzy-controller components. We suggest a new model enabling the user to represent a given fuzzy controller by a neural network and adapt its components as desired.

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Ji, H., and Zhi Yong Li. "Study on Intelligent Controller of Permanent Magnet Synchronous Motor." Advanced Materials Research 764 (September 2013): 149–53. http://dx.doi.org/10.4028/www.scientific.net/amr.764.149.

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This paper puts forward a novel design method of controller based on BP neural network, which is applied to the permanent magnet synchronous motor (PMSM) double closed loop speed regulation system of speed regulator, by using the neural network controller instead of traditional PID controller. It applies the nonlinear adaptive ability of neural network for optimizing the control parameters of PID controller for PMSM. The simulation model was established in Matlab/Simulink. The simulation results indicate that the neutral network PID controller, compared with the traditional PID, has strong rob

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Qasem, Gehad Ali Abdulrahman, and Mohammed Fadhl Abdullah. "Anti-Lock Braking Systems: A Comparative Study of Control Strategies and Their Impact on Vehicle Safety." International Research Journal of Innovations in Engineering and Technology 08, no. 09 (2024): 131–43. http://dx.doi.org/10.47001/irjiet/2024.809016.

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This study presents a comparative analysis of control strategies designed to enhance the performance of Anti-Lock Braking Systems (ABS) and improve vehicle safety. The research explores three key approaches: First, it evaluates Fuzzy Logic-Controlled ABS, comparing five defuzzification algorithms using MATLAB’s Fuzzy Logic Toolbox. Second, it investigates a Neural Network-Based Fault-Tolerant Control strategy, emphasizing improved fault tolerance during braking. Third, it assesses the performance of three ABS controllers—fuzzy logic, bangbang, and PID controllers. The findings reveal that Fuzz

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Alyukov, Alexander, Yuri Rozhdestvenskiy, and Sergei Aliukov. "Active Shock Absorber Control Based on Time-Delay Neural Network." Energies 13, no. 5 (2020): 1091. http://dx.doi.org/10.3390/en13051091.

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A controlled suspension usually consists of a high-level and a low-level controller. The purpose the high-level controller is to analyze external data on vehicle conditions and make decisions on the required value of the force on the shock absorber rod, while the purpose of the low-level controller is to ensure the implementation of the desired force value by controlling the actuators. Many works have focused on the design of high-level controllers of active suspensions, in which it is considered that the shock absorber can instantly and absolutely accurately implement a given control input. H

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Zhang, Wen Hui. "Adaptive Neural Network Control for X-Y NC Position Table." Key Engineering Materials 589-590 (October 2013): 654–57. http://dx.doi.org/10.4028/www.scientific.net/kem.589-590.654.

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An adaptive neural network control scheme for X-Y position table is proposed by the paper. X-Y position table model is established, controller based on neutral network is used to learn adaptive and compensate uncertainty model of X-Y position table, neutral network is used to study model. Neural network parameters base on stochastic gradient algorithm can be adjusted adaptive on line. Neural network controller base on augmented variable method is designed.

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Machavarapu, Suman, Mannam Venu Gopala Rao, and Pulipaka Venkata Ramana Rao. "Design of Load Frequency Controller for Multi-area System Using AI Techniques." Journal Européen des Systèmes Automatisés 53, no. 4 (2020): 541–48. http://dx.doi.org/10.18280/jesa.530413.

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The paper presents an adaptive Load Frequency Controller (LFC) based on a neural network for the interconnected multi-area systems. When there is an imbalance between active power generation and demand there will deviation in the frequency from the reference value. Major disturbances that lead to the variation in frequency beyond the allowable limits are variation in load demand and faults, etc. Initially PID based LFC which is a conventional controller is used to bring back the variations in frequency when there is a disturbance. But these conventional controllers will operate certain operati

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Asgari, Hamid, Mohsen Fathi Jegarkandi, XiaoQi Chen, and Raazesh Sainudiin. "Design of conventional and neural network based controllers for a single-shaft gas turbine." Aircraft Engineering and Aerospace Technology 89, no. 1 (2017): 52–65. http://dx.doi.org/10.1108/aeat-11-2014-0187.

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Purpose The purpose of this paper is to develop and compare conventional and neural network-based controllers for gas turbines. Design/methodology/approach Design of two different controllers is considered. These controllers consist of a NARMA-L2 which is an artificial neural network-based nonlinear autoregressive moving average (NARMA) controller with feedback linearization, and a conventional proportional-integrator-derivative (PID) controller for a low-power aero gas turbine. They are briefly described and their parameters are adjusted and tuned in Simulink-MATLAB environment according to t

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Mahdi, Shaymaa Mahmood, and Omar Farouq Lutfy. "Control of a servo-hydraulic system utilizing an extended wavelet functional link neural network based on sine cosine algorithms." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 847–56. https://doi.org/10.11591/ijeecs.v25.i2.pp847-856.

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Servo-hydraulic systems have been extensively employed in various industrial applications. However, these systems are characterized by their highly complex and nonlinear dynamics, which complicates the control design stage of such systems. In this paper, an extended wavelet functional link neural network (EWFLNN) is proposed to control the displacement response of the servo-hydraulic system. To optimize the controller's parameters, a recently developed optimization technique, which is called the modified sine cosine algorithm (M-SCA), is exploited as the training method. The proposed contr

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Hernandez-Barragan, Jesus, Jorge D. Rios, Javier Gomez-Avila, Nancy Arana-Daniel, Carlos Lopez-Franco, and Alma Y. Alanis. "Adaptive neural PD controllers for mobile manipulator trajectory tracking." PeerJ Computer Science 7 (February 19, 2021): e393. http://dx.doi.org/10.7717/peerj-cs.393.

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Artificial intelligence techniques have been used in the industry to control complex systems; among these proposals, adaptive Proportional, Integrative, Derivative (PID) controllers are intelligent versions of the most used controller in the industry. This work presents an adaptive neuron PD controller and a multilayer neural PD controller for position tracking of a mobile manipulator. Both controllers are trained by an extended Kalman filter (EKF) algorithm. Neural networks trained with the EKF algorithm show faster learning speeds and convergence times than the training based on backpropagat

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Bańka, Stanisław, Paweł Dworak, and Krzysztof Jaroszewski. "Design of a multivariable neural controller for control of a nonlinear MIMO plant." International Journal of Applied Mathematics and Computer Science 24, no. 2 (2014): 357–69. http://dx.doi.org/10.2478/amcs-2014-0027.

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Abstract The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive) multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF) motions of a drillship over the drilling point at the sea bottom. The designed neural controller contains a set of neural nets that determine values of its parameters chosen on the basis of two measured auxiliary signals. These are the ship’s current forward speed measured with respect to water and the systematically calculated difference between

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Urrea, Claudio, Yainet Garcia-Garcia, and John Kern. "Closed-Form Continuous-Time Neural Networks for Sliding Mode Control with Neural Gravity Compensation." Robotics 13, no. 9 (2024): 126. http://dx.doi.org/10.3390/robotics13090126.

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This study proposes the design of a robust controller based on a Sliding Mode Control (SMC) structure. The proposed controller, called Sliding Mode Control based on Closed-Form Continuous-Time Neural Networks with Gravity Compensation (SMC-CfC-G), includes the development of an inverse model of the UR5 industrial robot, which is widely used in various fields. It also includes the development of a gravity vector using neural networks, which outperforms the gravity vector obtained through traditional robot modeling. To develop a gravity compensator, a feedforward Multi-Layer Perceptron (MLP) neu

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Kung, Ying-Shieh, and Rong-Fong Fung. "Precision Control of a Piezoceramic Actuator Using Neural Networks." Journal of Dynamic Systems, Measurement, and Control 126, no. 1 (2004): 235–38. http://dx.doi.org/10.1115/1.1651535.

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In this paper, a control method combining the feedforward and feedback controllers is proposed to precisely control the dynamic performance of the piezoceramic actuator (PA). In the feedforward controller design, the hysteresis nonlinearity of the PA is modeled by using Preisach model first. Then a database of switching input/output values and a neural networks architecture treated as the inverse function of Preisach model are utilized in the feedforward controller. In the feedback controller design, a PI controller is used to regulate the output error. Finally, some experimental results are v

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Banda, Gururaj, and Sri Gowri Kolli. "An Intelligent Adaptive Neural Network Controller for a Direct Torque Controlled eCAR Propulsion System." World Electric Vehicle Journal 12, no. 1 (2021): 44. http://dx.doi.org/10.3390/wevj12010044.

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This article deals with an intelligent adaptive neural network (ANN) controller for a direct torque controlled (DTC) electric vehicle (EV) propulsion system. With the realization of artificial intelligence (AI) conferred adaptive controllers, the torque control of an electric car (eCAR) propulsion motor can be achieved by estimating the stator reference flux voltage used to synthesize the space vector pulse width modulation (SVPWM) for a DTC scheme. The proposed ANN tool optimizes the parameters of a proportional integral (PI) controller with real-time data and offers splendid dynamic stabilit

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39

Khleaf, Hussain K., Ali Kareem Nahar, and Ansam Subhi Jabbar. "Intelligent control of DC-DC converter based on PID-neural network." International Journal of Power Electronics and Drive Systems (IJPEDS) 10, no. 4 (2019): 2254. http://dx.doi.org/10.11591/ijpeds.v10.i4.pp2254-2262.

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This paper introduced a “PID-NN” based on Particle Swarm Optimization control that was applied to a boost converter operating in large-signal domains. Simulation results have shown that the proposed “PID-NN controller” could enhance the (boost converter) startup response with the use of fewer on-off switch operations compared to the Conventional “PID controllers”. This result is of high importance in practice since reducing the number of on-off switches can effectively decrease the transient disturbances and losses due to switching. Simulations also prove that the proposed “PID-NN controller”

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40

Aguado Behar, Alberto, Alfonso Noriega Ponce, Antonio Ordaz Hernández, and Vladimir Rauch Sitar. "Self-Tuning Neural Controller." IFAC Proceedings Volumes 31, no. 4 (1998): 107–11. http://dx.doi.org/10.1016/s1474-6670(17)42142-2.

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Sunar, M., A. M. A. Gurain, and M. Mohandes. "Substructural neural network controller." Computers & Structures 78, no. 4 (2000): 575–81. http://dx.doi.org/10.1016/s0045-7949(00)00039-0.

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Krishnapura, Venugopal G., and Arthur Jutan. "A neural adaptive controller." Chemical Engineering Science 55, no. 18 (2000): 3803–12. http://dx.doi.org/10.1016/s0009-2509(00)00034-8.

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Ruano, A. E. B., D. I. Jones, and P. J. Fleming. "A neural network controller." IFAC Proceedings Volumes 24, no. 7 (1991): 27–32. http://dx.doi.org/10.1016/b978-0-08-041699-1.50009-4.

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44

Long, Hoang Duc, and Natalia Dudarenko. "Synchronization Of Two-Rotor Vibration Units Using Neural Network-Based PID Controller." Cybernetics and Physics, Volume 11, 2022, Number 3 (November 17, 2022): 136–44. http://dx.doi.org/10.35470/2226-4116-2022-11-3-136-144.

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In the paper, the problem of controlled synchronization of Two-rotor Vibration Units is considered. A new bidirectional control law combined with Neural Network based PID controller is proposed for multisynchronization of rotors. Besides, an algorithm for design of Neural Network based PID Controller of Two rotor Vibration Units is developed. The robustness of the proposed controller under the effect of unknown exogenous disturbances is illustrated.

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45

Tan, Yonghong, and Achiel Van Cauwenberghe. "Nonlinear neural controller with neural Smith predictor." Neural Processing Letters 1, no. 2 (1994): 24–27. http://dx.doi.org/10.1007/bf02310939.

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Capi, Genci. "Effect of Genetic Encoding on Evolution of Efficient Neural Controllers." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 4 (2008): 377–81. http://dx.doi.org/10.20965/jaciii.2008.p0377.

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In this paper, we present a new method based on multiobjective evolutionary algorithms to evolve low complexity neural controllers for the robots that have to perform two different tasks, simultaneously. In our method, each task and the structure of neural controller are considered as separated objective functions. We compare the results of two different encoding schemes: (1) Connectionist encoding and (2) Node based encoding. Simulation results show that multiobjective evolution can be successfully applied to generate low complexity neural controllers. In addition, node based encoding outperf

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Voevoda, Alexsander, and Victor Shipagin. "Synthesis of a neural network control regulator of a nonlinear model of an inverted pendulum on a cart." Science Bulletin of the Novosibirsk State Technical University, no. 2-3 (November 13, 2020): 25–36. http://dx.doi.org/10.17212/1814-1196-2020-2-3-25-36.

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In this article, we consider a method for selecting a structure of a neural network used to regulate an "inverted pendulum on a cart" object taking into account its additional features of a mathematical description, namely, nonlinear parameters. The algorithm is illustrated by the example of control synthesis which includes two neuroregulators. One of them is responsible for bringing the cart to the specified position, and the second is responsible for holding the pendulum in a vertical position. The structure transformations will be performed for the controller responsible for bringing the ca

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Yamada, Takayuki, and Tetsuro Yabuta. "Adaptive Neural Network Controllers for Dynamics Systems." Journal of Robotics and Mechatronics 2, no. 4 (1990): 245–57. http://dx.doi.org/10.20965/jrm.1990.p0245.

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Many studies such as Kawato's work have been undertaken in order to apply both the flexibility and learning ability of neural networks to dynamic system controllers. However, their characteristics have not yet been completely clarified. On the other hand, many studies have established conventional control theories such as adaptive control. If we can clarify the relationship between neural network controllers and adaptive controllers, the two control algorithms will be developed considerably by making use of the advantages of each. Therefore, this paper proposes a neural network direct controll

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49

Hussain, K. Khleaf, K. Nahar Ali, and S. Jabbar Ansam. "Intelligent control of DC-DC converter based on PID-neural network." International Journal of Power Electronics and Drive System (IJPEDS) 10, no. 4 (2019): 2254–62. https://doi.org/10.11591/ijpeds.v10.i4.pp2254-2262.

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Abstract:

This paper introduced a “PID-NN” based on Particle Swarm Optimization control that was applied to a boost converter operating in large-signal domains. Simulation results have shown that the proposed “PID-NN controller” could enhance the (boost converter) startup response with the use of fewer on-off switch operations compared to the Conventional “PID controllers”. This result has been of high importance in practice for reducing the number of on-off switches can effectively decrease the transient disturbances and losses due to switching. Simulations also prov

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50

Leite, M. S., T. L. Fujiki, F. V. Silva, and A. M. F. Fileti. "Online Intelligent Controllers for an Enzyme Recovery Plant: Design Methodology and Performance." Enzyme Research 2010 (December 27, 2010): 1–13. http://dx.doi.org/10.4061/2010/250843.

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This paper focuses on the development of intelligent controllers for use in a process of enzyme recovery from pineapple rind. The proteolytic enzyme bromelain (EC 3.4.22.4) is precipitated with alcohol at low temperature in a fed-batch jacketed tank. Temperature control is crucial to avoid irreversible protein denaturation. Fuzzy or neural controllers offer a way of implementing solutions that cover dynamic and nonlinear processes. The design methodology and a comparative study on the performance of fuzzy-PI, neurofuzzy, and neural network intelligent controllers are presented. To tune the fuz

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