Real-time tuning of PID controller based on optimization algorithms for a quadrotor

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muharrem Selim Can ◽  
Hamdi Ercan
Keyword(s):  
Real Time ◽  
Mean Square Error ◽  
Trajectory Tracking ◽  
Pid Controller ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Mean Square ◽  
Control Parameters ◽  
Content Type ◽  
Real Time Simulations

Purpose This study aims to develop a quadrotor with a robust control system against weight variations. A Proportional-Integral-Derivative (PID) controller based on Particle Swarm Optimization and Differential Evaluation to tune the parameters of PID has been implemented with real-time simulations of the quadrotor. Design/methodology/approach The optimization algorithms are combined with the PID control mechanism of the quadrotor to increase the performance of the trajectory tracking for a quadrotor. The dynamical model of the quadrotor is derived by using Newton-Euler equations. Findings In this study, the most efficient control parameters of the quadrotor are selected using evolutionary optimization algorithms in real-time simulations. The control parameters of PID directly affect the controller’s performance that position error and stability improved by tuning the parameters. Therefore, the optimization algorithms can be used to improve the trajectory tracking performance of the quadrotor. Practical implications The online optimization result showed that evolutionary algorithms improve the performance of the trajectory tracking of the quadrotor. Originality/value This study states the design of an optimized controller compared with manually tuned controller methods. Fitness functions are defined as a custom fitness function (overshoot, rise-time, settling-time and steady-state error), mean-square-error, root-mean-square-error and sum-square-error. In addition, all the simulations are performed based on a realistic simulation environment. Furthermore, the optimization process of the parameters is implemented in real-time that the proposed controller searches better parameters with real-time simulations and finds the optimal parameter online.

A review of Zimbabwe’s macro-fiscal forecasts

2017 ◽  
Vol 8 (4) ◽  
pp. 433-440 ◽  
Author(s):  
Chathebert Mudhunguyo
Keyword(s):  
Root Mean Square Error ◽  
Service Delivery ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Public Service ◽  
Spillover Effects ◽  
Mean Square ◽  
Public Service Delivery ◽  
Content Type ◽  
Forecasting Errors

Purpose The purpose of this paper is to evaluate accuracy of macro fiscal forecasts done by Government of Zimbabwe and the spillover effects of forecasting errors over the period 2010-2015. Design/methodology/approach In line with the study objectives, the study employed the root mean square error methodology to measure the accuracy of macro fiscal forecasts, borrowing from the work of Calitz et al. (2013). The spillover effects were assessed through running simple regression in Eviews programme. The data used in the analysis are based on annual national budget forecasts presented to the Parliament by the Minister of Finance. Actual data come from the Ministry of Finance budget outturns and Zimbabwe Statistical Agency published national accounts. Findings The results of the root mean square error revealed relatively high levels of macro-fiscal forecasting errors, with revenue recording the highest. The forecasting errors display a tendency of under predicting the strength of economic recovery during boom and over predicting its strength during periods of weakness. The study although found significant evidence of GDP forecasting errors translating into revenue forecasting inaccuracies, the GDP forecasting errors fail to fully account for the revenue errors. Revenue errors were, however, found to be positive and significant in explaining the budget balance errors. Originality/value In other jurisdictions, particularly developed countries, they undertake regular evaluation of their forecasts in order to improve their forecasting procedures, which translate into quality public service delivery. The situation is lagging in Zimbabwe. Given the poor performance in public service delivery in Zimbabwe, this study contributes in dissecting the sources of the challenge by providing a comprehensive review of macro fiscal forecasts.

scholarly journals Indian COVID-19 dynamics: Prediction using autoregressive integrated moving average modelling

2021 ◽  
Vol 52 (1) ◽  
pp. 6-14
Author(s):  
Amit Tak ◽  
Sunita Dia ◽  
Mahendra Dia ◽  
Todd Wehner
Keyword(s):  
Root Mean Square Error ◽  
Real Time ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Information Criteria ◽  
Evidence Based ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
Akaike Information Criteria

Background: The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centrepiece in evidence-based disease management. Numerous approaches that use mathematical modelling have been used to predict the outcome of the pandemic, including data-driven models, empirical and hybrid models. This study was aimed at prediction of COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Material and Methods: Real-time Indian data of cumulative cases and deaths of COVID-19 was retrieved from the Johns Hopkins dashboard. The dataset from 11 March 2020 to 25 June 2020 (n = 107 time points) was used to fit the autoregressive integrated moving average model. The model with minimum Akaike Information Criteria was used for forecasting. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) were used to validate the model. Results: The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths, respectively, with minimum Akaike Information Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 June 2020 to 5 July 2020 showed a trend toward continuous increment. The PredRMSE and BaseRMSE of ARIMA (1,3,2) model were 21,137 and 166,330, respectively. Similarly, PredRMSE and BaseRMSE of ARIMA (3,3,1) model were 668.7 and 5,431, respectively. Conclusion: It is proposed that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimisation and evidence-based decision making for a subsequent state of affairs.

scholarly journals PERFORMANCE ANALYSIS OF A REAL-TIME ADAPTIVE PREDICTION ALGORITHM FOR TRAFFIC CONGESTION

2018 ◽  
Author(s):  
Khodabacchus Muhamad Nadeem ◽  
Tulsi Pawan Fowdur
Keyword(s):  
Real Time ◽  
Mean Square Error ◽  
Traffic Congestion ◽  
Selection Criterion ◽  
Prediction Algorithm ◽  
Mean Square ◽  
General Packet Radio Service ◽  
Prediction Algorithms ◽  
Cloud Server ◽  
Congestion Prediction

Traffic congestion is a major factor to consider in the development of a sustainable urban road network. In the past, several mechanisms have been developed to predict congestion, but few have considered an adaptive real-time congestion prediction. This paper proposes two congestion prediction approaches are created. The approaches choose between five different prediction algorithms using the Root Mean Square Error model selection criterion. The implementation consisted of a Global Positioning System based transmitter connected to an Arduino board with a Global System for Mobile/General Packet Radio Service shield that relays the vehicle’s position to a cloud server. A control station then accesses the vehicle’s position in real-time, computes its speed. Based on the calculated speed, it estimates the congestion level and it applies the prediction algorithms to the congestion level to predict the congestion for future time intervals. The performance of the prediction algorithms was analysed, and it was observed that the proposed schemes provide the best prediction results with a lower Mean Square Error than all other prediction algorithms when compared with the actual traffic congestion states.  

scholarly journals Speed Proportional Integrative Derivative Controller: Optimization Functions in Metaheuristic Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Luis Fernando de Mingo López ◽  
Francisco Serradilla García ◽  
José Eugenio Naranjo Hernández ◽  
Nuria Gómez Blas
Keyword(s):  
Pid Controller ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Metaheuristic Algorithms ◽  
Mathematical Background ◽  
Speed Controller ◽  
Search Optimization ◽  
Metaheuristic Search ◽  
Self Driving Cars ◽  
The Right

Recent advancements in computer science include some optimization models that have been developed and used in real applications. Some metaheuristic search/optimization algorithms have been tested to obtain optimal solutions to speed controller applications in self-driving cars. Some metaheuristic algorithms are based on social behaviour, resulting in several search models, functions, and parameters, and thus algorithm-specific strengths and weaknesses. The present paper proposes a fitness function on the basis of the mathematical description of proportional integrative derivate controllers showing that mean square error is not always the best measure when looking for a solution to the problem. The fitness developed in this paper contains features and equations from the mathematical background of proportional integrative derivative controllers to calculate the best performance of the system. Such results are applied to quantitatively evaluate the performance of twenty-one optimization algorithms. Furthermore, improved versions of the fitness function are considered, in order to investigate which aspects are enhanced by applying the optimization algorithms. Results show that the right fitness function is a key point to get a good performance, regardless of the chosen algorithm. The aim of this paper is to present a novel objective function to carry out optimizations of the gains of a PID controller, using several computational intelligence techniques to perform the optimizations. The result of these optimizations will demonstrate the improved efficiency of the selected control schema.

scholarly journals Dynamic Target Tracking and Ingressing of a Small UAV Using Monocular Sensor Based on the Geometric Constraints

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1931
Author(s):  
Zi-Hao Wang ◽  
Wen-Jie Chen ◽  
Kai-Yu Qin
Keyword(s):  
Root Mean Square Error ◽  
Target Tracking ◽  
Real Time ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Geometric Constraints ◽  
Mean Square ◽  
Moving Targets ◽  
Small Uav ◽  
Dynamic Target

In many applications of airborne visual techniques for unmanned aerial vehicles (UAVs), lightweight sensors and efficient visual positioning and tracking algorithms are essential in a GNSS-denied environment. Meanwhile, many tasks require the ability of recognition, localization, avoiding, or flying pass through these dynamic obstacles. In this paper, for a small UAV equipped with a lightweight monocular sensor, a single-frame parallel-features positioning method (SPPM) is proposed and verified for a real-time dynamic target tracking and ingressing problem. The solution is featured with systematic modeling of the geometric characteristics of moving targets, and the introduction of numeric iteration algorithms to estimate the geometric center of moving targets. The geometric constraint relationships of the target feature points are modeled as non-linear equations for scale estimation. Experiments show that the root mean square error percentage of static target tracking is less than 1.03% and the root mean square error of dynamic target tracking is less than 7.92 cm. Comprehensive indoor flight experiments are conducted to show the real-time convergence of the algorithm, the effectiveness of the solution in locating and tracking a moving target, and the excellent robustness to measurement noises.

Integration effects of UAVs in non-segregated airspace

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tamer Savas ◽  
Oznur Usanmaz ◽  
Ozlem Sahin ◽  
Ertan Çınar ◽  
Murat Karaderili
Keyword(s):  
Real Time ◽  
Simulation Method ◽  
Flight Time ◽  
Flight Distance ◽  
Content Type ◽  
Time Simulation ◽  
Proposed Model ◽  
Comparison Results ◽  
Quantitative Results ◽  
Real Time Simulations

Purpose The study aims to design a new route model for unmanned aerial vehicles (UAVs) to integrate them into non-segregated airspace. Design/methodology/approach The proposed route model was assessed and validated through real-time simulations. Findings The comparison results of baseline and proposed route model show that a reduction of 38% and 41% in the total flight time and total flight distance were obtained in favour of the proposed model, respectively. Practical implications The proposed route model can be applied by airspace designers and UAV users to perform safe and efficient landing in non-segregated airspace. Originality/value In this study, a new proposed route model is constructed for UAVs. Quantitative results, using a real-time simulation method, are achieved in terms of flight distance and flight time.

scholarly journals Research and Application of a Smart Monitoring System to Monitor the Deformation of a Dam and a Slope

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yongfei Wang ◽  
Dingbin Shen ◽  
Jiankang Chen ◽  
Liang Pei ◽  
Yanling Li ◽  
...  
Keyword(s):  
Real Time ◽  
Mean Square Error ◽  
Monitoring System ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Large Field ◽  
Maximum Relative Error ◽  
Mean Square ◽  
Integrated Monitoring ◽  
The Real

Deformation monitoring is one of the most important means of providing feedback to ensure the safety of projects. Problems plague the existing automatic monitoring system, such as the small monitoring range of monitoring devices, the inadequate field safety protection, and the low accuracy under extreme weather conditions. These problems greatly reduce the real time and reliability of deformation monitoring data and restrict the real-time intelligent control of engineering safety risk. In this paper, a multitype instrument-integrated monitoring system based mainly on the total positioning station (TPS) and supplemented by the Global Navigation Satellite System (GNSS) was promoted with the methods of large field angle, data complementation, environmental perception and judgment, automatic status control, and baseline calibration-meteorological fusion correction. The application results of Pubugou Station show that the averages of mean square error of points (APMSE) for the dam are 0.41∼1.65 mm and the averages of mean square error of height (AHMSE) are 0.42∼0.89 mm. Moreover, the APMSE and AHMSE for the slope are less than 3 mm. The maximum relative error of the TPS and GNSS data compared with the artificial monitoring data is less than 10%. Besides, the system has good overall performance and is of significant comprehensive benefits. The proposed system realizes the all-weather real-time monitoring of deformation and enhances the emergency response capability of special conditions in dams during the operation period.

Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm

2020 ◽  
pp. 107754632095138
Author(s):  
Rosmazi Rosli ◽  
Zamri Mohamed
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Search Algorithm ◽  
Fitness Function ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Magnetorheological Damper ◽  
Size Factor ◽  
Mean Square

This article presents a new modified cuckoo search algorithm with dynamic discovery probability and step-size factor for optimizing the Bouc–Wen Model in magnetorheological damper application. The newly proposed algorithm was tested using a set of standard benchmark functions with different searching space and global optima placement. An engineering optimization application was chosen to evaluate the performance of the algorithm in complex engineering applications. The optimization task involved hysteresis parameter identification of the root mean square error between the model and an actual magnetorheological damper. The magnetorheological damper response was chosen as the objective function. The final value of the fitness function and the iteration number it took to converge were used as the qualifying indicator to the proposed cuckoo search algorithm efficiency. A comparison was done against particle swarm optimization, genetic algorithm, and sine–cosine algorithm, where the modified cuckoo search algorithm showed the lowest root mean square error and fastest convergence rate among the three algorithms.

scholarly journals Estimating Aboveground Biomass Using Allometric Models And Adaptive Learning Rate Optimization Algorithms

2021 ◽  
Vol 25 (7) ◽  
pp. 1139-1146
Author(s):  
S.J. Okonkwo ◽  
Z.H. Mshelia
Keyword(s):  
Mean Square Error ◽  
Root Mean Square ◽  
Adaptive Learning ◽  
Aboveground Biomass ◽  
Optimization Algorithms ◽  
Decay Rates ◽  
Learning Rate ◽  
Mean Square ◽  
Allometric Models ◽  
Significant Difference

Forest aboveground biomass (AGB) is imperative in the study of climate change and the carbon cycle in the global terrestrial ecosystem. Developing a credible approach to estimate forest biomass and carbon stocks is essential. Four allometric models were used with two optimization algorithms; Modified Root Mean Square Propagation (Modified RMSProp) and Modified Adaptive Moment Estimation (Modified Adam) were also used to train each model. Convergence was achieved after 1000 iterations of Modified RMSProp and 200 iterations of Modified Adam for all the models. A learning rate of 0.01 and exponential decay rates of 0.9 and 0.999 for the first and second momentum. A loss function of 0.5 Mean Square Error (0.5 MSE) was used and Root Mean Square Error (RMSE) was used to judge the accuracy of the models. The study showed that the optimization algorithms were both able to accurately optimize three of the four allometric models. While Modified Adam was the more efficient optimizer, it had the highest RMSE value 2.3910 and Modified RMSProp had the least RMSE value 0.37381. However, there was no statistically significant difference between the accuracy of the models optimized by both algorithms.

Accuracy Improvement of Solar Power Estimation Using Real-Time Degradation Computation of PV Panels

2021 ◽  
pp. 2150242
Author(s):  
Parveen Bhola ◽  
Saurabh Bhardwaj
Keyword(s):  
Root Mean Square Error ◽  
Real Time ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Meteorological Parameters ◽  
Solar Power ◽  
Performance Ratio ◽  
Accurate Estimation ◽  
Support Vector ◽  
Mean Square

Many applications including power trading and planning require the accurate estimation of solar power in real time. As the power output of the solar panels degrades over the time period, so its real-time estimation is tough without the degradation parameter. In the proposed method, the effect of degradation in terms of performance ratio is incorporated along with other meteorological parameters. The degradation is calculated in real time using the clustering-based technique without physical inspection on site. Initially, the power is estimated using Support Vector Regression (SVR) model with the meteorological parameters. The estimation is further fine-tuned in sync with the degradation rate. The model is validated on the real data (Meteorological parameters and Solar power) procured from the solar plant. After refinement, the estimation results show significant improvement in terms of statistical measures. Now, the estimation accuracy in terms of coefficient of determination R2 is 92% and the error metrics normalized root mean square error (NMRSE), mean absolute percentage error (MAPE), root mean square error (RMSE) are 7.13, 5.92 and 14.54, respectively.

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