AIMC Topic: Computer Simulation

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Nonlinear motor-mechanism coupling tank gun control system based on adaptive radial basis function neural network optimised computed torque control.

ISA transactions
This study investigates the spatial pointing control of a motor-mechanism coupling tank gun. The tank gun control system (TGCS) is driven and stabilised by the motor servo system. However, complicated nonlinearities in the TGCS are inevitable, such a...

Optimization of dewatering process of concentrate pressure filtering by support vector regression.

Scientific reports
This work studies the mechanism and optimization methods of the filter press dehydration process to better improve the efficiency of the concentrate filter press dehydration operation. Machine learning (ML) models of radial basis function (RBF)-OLS, ...

Performance enhancement of an uncertain nonlinear medical robot with optimal nonlinear robust controller.

Computers in biology and medicine
Cardiopulmonary resuscitation refers to the process of sending oxygen and blood to the body's vital organs during cardiac arrest. For this reason, designing and controlling an accurate robot is crucial to saving the lives of patients. This study aims...

Nonlinear control of a class of non-affine variable-speed variable-pitch wind turbines with radial-basis function neural networks.

ISA transactions
Due to complicated dynamics, wind turbines' governing equations are subject to uncertainties and unknown disturbance sources. Despite uncertainties and disturbance sources, the paper's focus is to design an adaptive controller that enables trajectory...

Sensor Screening Methodology for Virtually Sensing Transmission Input Loads of a Wind Turbine Using Machine Learning Techniques and Drivetrain Simulations.

Sensors (Basel, Switzerland)
The ongoing trend of building larger wind turbines (WT) to reach greater economies of scale is contributing to the reduction in cost of wind energy, as well as the increase in WT drivetrain input loads into uncharted territories. The resulting intens...

Physics-constrained deep active learning for spatiotemporal modeling of cardiac electrodynamics.

Computers in biology and medicine
The development of computational modeling and simulation have immensely benefited the study of cardiac disease mechanisms and facilitated the optimal disease diagnosis and treatment design. The dynamic propagation of cardiac electrical signals are of...

Efficient Path Planning for Mobile Robot Based on Deep Deterministic Policy Gradient.

Sensors (Basel, Switzerland)
When a traditional Deep Deterministic Policy Gradient (DDPG) algorithm is used in mobile robot path planning, due to the limited observable environment of mobile robots, the training efficiency of the path planning model is low, and the convergence s...

Formation Control of Automated Guided Vehicles in the Presence of Packet Loss.

Sensors (Basel, Switzerland)
This paper presents the formation tracking problem for non-holonomic automated guided vehicles. Specifically, we focus on a decentralized leader-follower approach using linear quadratic regulator control. We study the impact of communication packet l...

A Feed-Forward Neural Network for Increasing the Hopfield-Network Storage Capacity.

International journal of neural systems
In the hippocampal dentate gyrus (DG), pattern separation mainly depends on the concepts of 'expansion recoding', meaning random mixing of different DG input channels. However, recent advances in neurophysiology have challenged the theory of pattern ...

Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems.

Sensors (Basel, Switzerland)
The use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial intelligence and machine learning approaches are i...