Neural Network Based System Identification of an Axis of Car Suspension System

Neural networks system identification have been widely used for estimate the nonlinear model of system. In this paper, multilayer perceptron neural network is used for identifying the Nonlinear AutoRegressive with eXogenous input (NARX) model of a quarter car passive suspension system. Input output data are acquired by driving a car on a special road event. The networks structure is developed based on system model. The Networks learning algorithm is derived using Fisher's scoring method. Then the Fisher information is given as a weighted covariance matrix of inputs and outputs the network hidden layer. Unitwise Fisher's scoring method reduces to the algorithm in which each unit estimate its own weights by a weighted least square method. The results show that the method uses suitable for modeling a quarter car passive suspension systems

Sliding mode control design for the attitude and altitude of the quadrotor UAV

Recently, the quadrotor unmanned aerial vehicles (UAV) are attracting significant interest from researchers due to its widespread applications, which involve the civilian and military sectors. In this paper, a robust sliding mode control (SMC) algorithm is designed to stabilize the attitude and track the altitude of quadrotor UAV. The switching function in the SMC control law has been replaced by the error function to reduce the chattering influences. The chattering phenomenon is induced by the parameter uncertainties and external disturbances and results in critical issues, for instance, the vibration in the mechanical components. The simulation results of the traditional SMC and feedback linearization (FBL) are used as the benchmark to test and evaluate the performance of the proposed SMC, which proved that the proposed controller outperforms the traditional SMC and FBL controllers

Performance Comparison between Sliding Mode Control with PID Sliding Surface and PID Controller for an Electro-hydraulic Positioning System

In this paper, the position tracking performance of an electro-hydraulic hydraulic servo (EHS) system using sliding mode control (SMC) with proportional-integral-derivative (PID) sliding surface is presented. The dynamics of the EHS system in modelling process are developed by consider its nonlinearities incorporating a friction model. Then, SMC with PID scheme is derived from the developed dynamics equation and stability of the control system is theoretically proven by Lyapunov theorem. Finally, simulation work is demonstrated and the result shows the proposed controller can achieve better tracking performance compared with conventional PID controller with good accuracy for any desired trajectory

A Quarter Car ARX Model Identification Based on Real Car Test Data

This paper presents a system identification of a quarter car passive suspension system dynamic model based on real-time running test car data. The input-output data of a car were recorded by test-driving the car on a road surface. The input variable is the vertical acceleration of the car shaft, while the output variable is the vertical acceleration of the body of the car. Two acceleration sensors were installed on the front right corner of the car: One on top of the suspension and another on the car shaft at the bottom of the suspension. The acquired data were used to identify the mathematical model of a quarter car passive suspension system dynamics. A quarter car passive suspension system was assumed to have an ARX model structure, hence qualifies to be a candidate model for system identification. The system identification algorithm used in this work was based on linear least-square estimation. The results showed that the best ARX model of the car passive suspension system model is produced with the best fit of 90.65%, Akaike’s FPE is 5.315x10-6. The output order of the model was found to be four, the input order is two and the time delay was one. The fit rate greater than 90% and along with a very small value for the FPE means that the system identification requirements are fulfilled and the identified model is acceptable

Development of an electrical charge sensing prototype for pneumatic conveying imaging system

In the view of heterogeneous flow characteristics of solid particles in pneumatic pipeline system, electrostatic signals of an array 16 electrical charge sensors were developed. The distribution solid particle properties of the electrostatic signals in handling of vertical pneumatic conveying system under different flow conditions were monitored and experimental verification was conducted. The results show that the energy distribution of an array electrostatic signals can be used to determine the distribution of solids inside the pipe. Regardless of the differences in mass flow rate, the pattern of experimental outputs was identical which demonstrates that mass flow rate disparity has no impact on the structure of voltage output. This result also indicates that the electrical charge sensor able to quantify the dissemination of solid particles in pneumatic conveying stably and accurately

Neural network ABAC with dropout layer for activated sludge system

Due to the expensive operation of the activated sludge process and more stringent effluent requirements of wastewater treatment plant (WWTP), the wastewater treatment operator has been forced to find an alternative to improve the current control strategy, especially for those operating using an activated sludge system. The study aims to reduce the energy usage of a WWTP and to increase the effluent quality to meet the requirements of state and national laws by using the aeration control technique. The goals are achieved by varying the dissolved oxygen concentration in the benchmark plant's fifth tank according to the real ammonium measurement, a technique known as Ammonium-based aeration control (ABAC), which produced less nitrogen, resulting in better effluent and lower energy consumption. The simulation model Benchmark Simulation Model No. 1 (BSM1) was used to analyze ABAC in this study. The neural network (NN) model is used to design the ABAC controller, and simulation results were compared to the Proportional Integral (PI) controller of the BSM1 and PI ABAC control configurations. A dropout layer was added during the training process to improve neural network generalization. The dropout layer in the NN ABAC has improved the performances in terms of total nitrogen effluent violations by 4 percent less than the PI-ABAC and by 36 percent less than the PI. The NN ABAC LM dropout has been proven to be more effective in terms of energy efficiency by significantly reduced by 25 percent, effluent quality by successfully improved by 1 percent, and successfully reduced the total overall cost index by 5 percent when compared to PI-ABAC control. The study has illustrated that the NN ABAC could be used to improve the performance of the activated sludge system

Neural Network ABAC with Dropout Layer for Activated Sludge System

Due to the expensive operation of the activated sludge process and more stringent effluent requirements of wastewater treatment plant (WWTP), the wastewater treatment operator has been forced to find an alternative to improve the current control strategy, especially for those operating using an activated sludge system. The study aims to reduce the energy usage of a WWTP and to increase the effluent quality to meet the requirements of state and national laws by using the aeration control technique. The goals are achieved by varying the dissolved oxygen concentration in the benchmark plant's fifth tank according to the real ammonium measurement, a technique known as Ammonium-based aeration control (ABAC), which produced less nitrogen, resulting in better effluent and lower energy consumption. The simulation model Benchmark Simulation Model No. 1 (BSM1) was used to analyze ABAC in this study. The neural network (NN) model is used to design the ABAC controller, and simulation results were compared to the Proportional Integral (PI) controller of the BSM1 and PI ABAC control configurations. A dropout layer was added during the training process to improve neural network generalization. The dropout layer in the NN ABAC has improved the performances in terms of total nitrogen effluent violations by 4 percent less than the PI-ABAC and by 36 percent less than the PI. The NN ABAC LM dropout has been proven to be more effective in terms of energy efficiency by significantly reduced by 25 percent, effluent quality by successfully improved by 1 percent, and successfully reduced the total overall cost index by 5 percent when compared to PI-ABAC control. The study has illustrated that the NN ABAC could be used to improve the performance of the activated sludge system

Enhanced Self-Regulation Nonlinear PID For Industrial Pneumatic Actuator

The present article describes the improvement of Self-regulation Nonlinear PID (SN-PID) controller. A new function is introduced to improve the system performance in terms of transient without affecting the steady state performance. It is used to optimize the nonlinear function available on this controller. The signal error is reprocessed through this function, and the result is used to tune the nonlinear function of the controller. Furthermore, the presence of the dead zone on the proportional valve is solved using Dead Zone Compensator (DZC). Simulations and experiments were carried out on the pneumatic positioning system. Comparisons between the existing methods were examined and successfully demonstrated

Speed Effect to a Quarter Car ARX Model Based on System Identification

This paper presents the effect of car speeds on a quarter car passive suspension system model dynamics. The model is identified using system identification technique, in which the input-output data are collected by running a test car on an artificial road surface with two different speeds i.e., 10 km/h and 20 km/h. The quarter car passive suspension system dynamics is assumed to have an ARX model structure and identified using linear least-square estimation algorithm. The car vertical body acceleration, which is the output variable, is measured by installing an accelerometer sensor on the car body, above the suspension. On the other hand, the car shaft acceleration, which is the input variable, is measured by installing an accelerometer sensor at the lower arm of the car suspension. The best model for the 10 km/h car speed gives the output order () = 4, the input order () = 2, delay (d) = 1, the best fit = 90.65%, and the Akaike’s Final Prediction Error (FPE) = 5.315e-06. In contrast, the 20 km/h speed results in 4th output order (), 1stthe input order (), 1st delay (d), the best fit of 91.05%, and 7.503e-05Akaike’s FPE. These results show that the higher speed reduces the effect of the road surface to car dynamics, which is indicated by the order of the mode