CONTROL OF MR DAMPER USING ANFIS AND PID CONTROLLER FOR OPTIMUM VEHICLE RIDE COMFORT

Suspension system design is an important challenging duty that facing car manufacturers, so the challenge has become to design the best system in terms of providing ride comfort and handling ability under all driving situations. The goal of this paper is to provide assistance in enhancing the effectiveness of the suspension system. A full car model with eight Degrees Of Freedom (DOF) was developed using MATLAB/Simulink. Validation of the Simulink model was obtained. The model was assumed to travel over a speed hump that has a half sine wave shape and amplitude that changing from 0.01 to 0.2 m. The vehicle was moving with variable speeds from 20 to 120 km/h. Magneto Rheological (MR) damper was implanted to the model to study its effect on ride comfort. Adaptive-Network-based Fuzzy Inference System (ANFIS) was used to find the optimum voltage value applied to the MR damper, to skip the hump at least displacement. This network uses road profile and the vehicle speed as inputs. A Proportional Integral Derivative (PID) controller has been used to deal with potential disturbances that may affect the obtained voltage by the ANFIS. A comparison of the results for passive suspension system and model with MR damper, and system with and without PID controller, are illustrated. Results show that the MR damper gives significant improvements of the vehicle ride performance over the passive suspension system, and the PID increases the effectiveness of the system to skip the disturbance with minimal damage

Neuro-Fuzzy System for Compensating Slow Disturbances in Adaptive Mold Level Control

[EN] Good slow disturbances attenuation in a mold level control with stopper rod is very important for avoiding several product defects and keeping down casting interruptions. The aim of this work is to improve the accuracy of the diagnosis and compensation of an adaptive mold level control method for slow disturbances related to changes of stopper rod. The advantages offered by the architecture, called Adaptive-Network-based Fuzzy Inference System, were used for training a previous model. This allowed learning based on the process data from a steel cast case study, representing all intensity levels of valve erosion and clogging. The developed model has high accuracy in its functional relationship between two compact input variables and the compensation coefficient of the valve gain variations. The future implementation of this proposal will consider a combined training of the model, which would be very convenient for maintaining good accuracy in the Fuzzy Inference System using new data from the process.This work is supported by a Project (AA-ELACERO, P211LH021-023) of the National Key Research and Development Program of Automatic, Robotic and Artificial Intelligence of Cuba.González-Yero, G.; Ramírez Leyva, R.; Ramírez Mendoza, M.; Albertos, P.; Crespo, A.; Reyes Alonso, JM. (2021). Neuro-Fuzzy System for Compensating Slow Disturbances in Adaptive Mold Level Control. Metals. 11(1):1-21. https://doi.org/10.3390/met1101005612111

A Review on ANFIS based Linearization of Non Linear Sensors

Low cost sensors having high sensitivity, better resolution and linear characteristics are required for industrial applications based on instrumentation and control. Unfortunately, the natural non linear characteristic of sensor itself and also the dynamic nature of the environment, aging effect, inherent sensor’s noise and data loss due to transients or intermittent faults affects the sensor characteristics non linearly. As the transfer characteristic of most sensors is nonlinear in nature, obtaining data from such a nonlinear sensor, by using an optimized device, has always been a design challenge. Linearization of nonlinear sensor characteristic in digital environment, is a vital step in the instrument signal conditioning process. This paper gives a brief review about how to overcome this nonlinear characteristic of the sensor using artificial intelligence such as  Hybrid Neuro Fuzzy Logic (HNFL) based on digital linearization technique using VLSI technology such as Field Programmable Gate Array (FPGA)

Modelling, simulation and proportional integral control of a pneumatic motor

Researchers have shown a considerable amount of interest in the control of pneumatic drives over the past decade, for two main reasons, firstly, the response of the system is very slow and it is difficult to attain set points due to hysteresis and secondly, the dynamic model of the system is highly non-linear, which greatly complicates controller design and development. To address these problems, two streams of research effort have evolved and these are: (i) using conventional methods to develop a modelling and control strategy, (ii) adopting a strategy that does not require mathematical model of the system. This paper presents an investigation into the modelling and control of an air motor incorporating a pneumatic equivalent of the electric H-bridge. The pneumatic H-bridge has been devised for speed and direction control of the motor. The system characteristics are divided into three regions, namely low speed, medium speed and high speed. The system is highly nonlinear in the low speed region, for which neuro-modelling, simulation and control strategies are developed

A Tutorial on Learning Human Welder\u27s Behavior: Sensing, Modeling, and Control

Human welder\u27s experiences and skills are critical for producing quality welds in manual GTAW process. Learning human welder\u27s behavior can help develop next generation intelligent welding machines and train welders faster. In this tutorial paper, various aspects of mechanizing the welder\u27s intelligence are surveyed, including sensing of the weld pool, modeling of the welder\u27s adjustments and this model-based control approach. Specifically, different sensing methods of the weld pool are reviewed and a novel 3D vision-based sensing system developed at University of Kentucky is introduced. Characterization of the weld pool is performed and human intelligent model is constructed, including an extensive survey on modeling human dynamics and neuro-fuzzy techniques. Closed-loop control experiment results are presented to illustrate the robustness of the model-based intelligent controller despite welding speed disturbance. A foundation is thus established to explore the mechanism and transformation of human welder\u27s intelligence into robotic welding system. Finally future research directions in this field are presented

A Feasibility Study for the Automated Monitoring and Control of Mine Water Discharges

The chemical treatment of mine-influenced waters is a longstanding environmental challenge for many coal operators, particularly in Central Appalachia. Mining conditions in this region present several unique obstacles to meeting NPDES effluent limits. Outlets that discharge effluent are often located in remote areas with challenging terrain where conditions do not facilitate the implementation of large-scale commercial treatment systems. Furthermore, maintenance of these systems is often laborious, expensive, and time consuming. Many large mining complexes discharge water from numerous outlets, while using environmental technicians to assess the water quality and treatment process multiple times per day. Unfortunately, this treatment method when combined with the lower limits associated with increased regulatory scrutiny can lead to the discharge of non-compliant water off of the mine permit. As an alternative solution, this thesis describes the ongoing research and development of automated protocols for the treatment and monitoring of mine water discharges. In particular, the current work highlights machine learning algorithms as a potential solution for pH control.;In this research, a bench-scale treatment system was constructed. This system simulates a series of ponds such as those found in use by Central Appalachian coal companies to treat acid mine drainage. The bench-scale system was first characterized to determine the volumetric flow rates and resident time distributions at varying flow rates and reactor configurations. Next, data collection was conducted using the bench scale system to generate training data by introducing multilevel random perturbations to the alkaline and acidic water flow rates. A fuzzy controller was then implemented in this system to administer alkaline material with the goal of automating the chemical treatment process. Finally, the performance of machine learning algorithms in predicting future water quality was evaluated to identify the critical input variables required to build these algorithms. Results indicate the machine learning controllers are viable alternatives to the manual control used by many Appalachian coal producers

Temperature Control System in Closed House for Broilers Based on ANFIS

 Indonesia is a tropical country with high ambient temperatures for broilers since daily temperature reaches an average daily temperature of 360C (maximum) and 320 C (minimum); whereas the optiml temperature for broilers is in the range of 28-300C. Thefefore, midle or large scale broiler industries have been using a control system to maintain the optimal temperature within a broiler house. Therefore, the role of a control system for regulating environmental parameters, not only temperature but also humidity, light intensity, and amonia content level, is very critical and relevant for better broiler production. This study aims to design an ANFIS control system for controlling the temperature inside a broiler house (closed house) for broiler. Data is collected at three different periods of the starter period (5 days): 29.50C-30.900C, a period of 25 days is a grower-29.0C 34.20C, and the finisher of 30 days is obtained 33.20C. Set point control simulation using the same temperature 290C for starter, grower and finisher period. The simulation results show the output in a closed house temperature fluctuates around set point the 290C-340C