Robust motion control of nonlinear quadrotor model with wind disturbance observer

This paper focuses on robust wind disturbance rejection for nonlinear quadrotor models. By leveraging on nonlinear unknown observer theory, it proposes a nonlinear dynamic filter that, using sensors already on-board the aircraft, can estimate in real-time wind gust signals in the three dimensions. The wind disturbance is then treated as input to the PD controller for a quick and robust flight pathway in presence of disturbances. With this scheme, the wind disturbance can be precisely estimated online and compensated in real-time. Hence, the quadrotor can successfully reach its desired attitude and position. To show the effective and desired performance of the method, simulation results are presented in Matlab/Simulink and ROS-enabled Gazebo platform

Precise Trajectory Tracking of Multi-Rotor UAVs Using Wind Disturbance Rejection Approach

This paper discusses the resilience of the UAV quadrotor to wind disturbances. An unknown input-state observer is presented that uses the Lipschitz method to estimate the internal states and disturbances of the quadrotor and compensate for them by varying the velocities of the four rotors. The observer intends to use existing sensor measurements to estimate the unknown states of the quadrotor and reconstruct the three-dimensional wind disturbances. The estimated states and external disturbances are sent to the PD controller, which compensates for the disturbances to achieve the desired position and attitude, as well as robustness and accuracy. The Lipschitz observer was designed using the LMI approach, and the results were validated using Matlab/Simulink and using the Parrot Mambo mini quadrotor

New flood risk index in tropical area generated by using SPC technique

This study applied four hydrology parameters. The findings from Principal ComponentAnalysis confirmed that all selected parameters were significant to be taken as main tools forfurther analysis with result of R2> 0.7. SPC set up a new control limit for all selectedparameters in the study area. For those data within or beyond the Upper Control Limit value, itwas being considered as high risk for flood occurrence. New flood risk index within rangefrom 0-100 was calculated using a combination of new algebraic equation and control limitvalues obtained from SPC analysis as variable. The accuracy of FRI was tested using ANN.The result showed the accuracy of FRI was more than 90%. It can be stipulated that thecombination of chemometric techniques and SPC can produce a new standard FRI which iscost effective, accurate and flexible to be applied for the purpose of flood risk control intropical area.Keywords: flood risk index; statistical process control; chemometric technique; tropical area;control limit; prediction performance

Air quality modelling using chemometric techniques

The datasets of air quality parameters for three years (2012-2014) were applied. HACA gave the result of three different groups of similarity based on the characteristics of air quality parameters. DA shows all seven parameters (CO, O3, PM10, SO2, NOx, NO and NO2) gave the most significant variables after stepwise backward mode. PCA identifies the major source of air pollution is due to combustion of fossil fuels in motor vehicles and industrial activities. The ANN model shows a better prediction compared to the MLR model with R2 values equal to 0.819 and 0.773 respectively. This study presents that the chemometric techniques and modelling become an excellent tool in API assessment, air pollution source identification, apportionment and can be setbacks in designing an API monitoring network for effective air pollution resources management

Metal concentration at surface water using multivariate analysis and human health risk assessment

This study defined the concentration of metals in Kerteh and Paka River water and their potential health risk towards human. 54 water samples were collected and analyzed using ICP-OES. Results revealed that most of the stations in Kerteh River gave the higher concentration of Cd, Cu, Zn, Co, Ni, As, Cr and Pb compared to Paka River. However As, Cr and Pb have exceeded the permissible limit of Malaysia standard for all stations in both rivers. Cd, Cu, Zn, Co and Ni were below than Malaysian standard permissible levels during the sampling period. The principal component analysis (PCA) revealed that both geogenic and anthropogenic sources were responsible to possible metals contamination in both rivers. Moreover, risk assessments for all metals were within the safe limits, except for As in the Kerteh River for both adult and child as well as to Paka River for both genders

Clustering Imputation for Air Pollution Data

Air pollution is a global problem. The assessment of air pollution concentration data is important for evaluating human exposure and the associated risk to health. Unfortunately, air pollution monitoring stations often have periods of missing data or do not measure all pollutants. In this study, we experiment with different approaches to estimate the whole time series for a missing pollutant at a monitoring station as well as missing values within a time series. The main goal is to reduce the uncertainty in air quality assessment. To develop our approach we combine single and multiple imputation, nearest neighbour geographical distance methods and a clustering algorithm for time series. For each station that measures ozone, we produce various imputations for this pollutant and measure the similarity/error between the imputed and the real values. Our results show that imputation by average based on clustering results combined with multiple imputation for missing values is the most reliable and is associated with lower average error and standard deviation

Wind gust estimation for precise quasi-hovering control of quadrotor aircraft

This paper focuses on the control of quadrotor vehicles without wind sensors that are required to accurately track low-speed trajectories in the presence of moderate yet unknown wind gusts. By modeling the wind disturbance as exogenous inputs, and assuming that compensation of its effects can be achieved through quasi-static vehicle motions, this paper proposes an innovative estimation and control scheme comprising a linear dynamic filter for the estimation of such unknown inputs and requiring only position and attitude information. The filter is built upon results from Unknown Input Observer theory and allows estimation of wind and vehicle state without measurement of the wind itself. A simple feedback control law can be used to compensate for the offset position error induced by the disturbance. The proposed filter is independent of the recovery control scheme used to nullify the tracking error, as long as the corresponding applied rotor speeds are available. The solution is first checked in simulation environment by using the Robot Operating System middleware and the Gazebo simulator and then experimentally validated with a quadcopter system flying with real wind sources

Improved Performance in Quadrotor Trajectory Tracking using MIMO PIλ-D Control

This paper aims to develop a fractional control approach for quadrotor trajectory tracking. A fractional-order integrator (PIλ) with a feedback derivative scheme is designed to control each state of the MIMO system. The designed feedback controller stabilizes the initially unstable decoupled states and widens the stability, while PIλ provides precise trajectory tracking capabilities. After a successful simulation study, the new PIλ-D controller is implemented in the hardware environment. The various performance and load disturbance analyses reveal the effectiveness of the proposed scheme compared with the classical PD/PID controllers. The real-time study also shows that this scheme is a simple yet robust solution

Assessment on air quality pattern: a case study in Putrajaya, Malaysia

Nowadays, air quality problem has become a major issue in Malaysia for the past two decades,This study aims to determine the pattern of the air quality status, investigate the significantpollutant, relationship between air pollutants and API and evaluate the pattern of air pollutionData from Putrajaya monitoring station based on three years observation (2001-2013) wereused. Multivariate techniques such as principal component analysis (PCA), factor analysis(FA) and statistical process control (SPC). The method of PCA and FA has identified that fiveparameters with the value >0.75 affected the quality of air. SPC shows that SO2 pollutants hadinfluence the quality of air the most compared to other pollutants. From the study, it can bestipulated that the chemometric technique can provide meaningful information on the spatialvariability of large and complex air quality data.Keywords: air quality pattern; PCA; FA; SPC

Piezoresistive Effect in Plasma-Doping of Graphene Sheet for High-Performance Flexible Pressure Sensing Application

This paper presents a straightforward plasma treatment modification of graphene with an enhanced piezoresistive effect for the realization of a high-performance pressure sensor. The changes in the graphene in terms of its morphology, structure, chemical composition, and electrical properties after the NH₃/Ar plasma treatment were investigated in detail. Through a sufficient plasma treatment condition, our studies demonstrated that plasma-treated graphene sheet exhibits a significant increase in sensitivity by one order of magnitude compared to that of the unmodified graphene sheet. The plasma-doping introduced nitrogen (N) atoms inside the graphene structure and was found to play a significant role in enhancing the pressure sensing performance due to the tunneling behavior from the localized defects. The high sensitivity and good robustness demonstrated by the plasma-treated graphene sensor suggest a promising route for simple, low-cost, and ultrahigh resolution flexible sensors