scholarly journals Rapidly Tuning the PID Controller Based on the Regional Surrogate Model Technique in the UAV Formation

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 527
Author(s):  
Binglin Wang ◽  
Xiaojun Duan ◽  
Liang Yan ◽  
Juan Deng ◽  
Jiangtao Chen
Keyword(s):  
Simulation Model ◽  
Surrogate Model ◽  
Linear Quadratic Regulator ◽  
Surrogate Models ◽  
Singular Points ◽  
Pid Controllers ◽  
Linear Quadratic ◽  
Order Of Magnitude ◽  
Pareto Fronts ◽  
Model Technique

The leader–follower structure is widely used in unmanned aerial vehicle formation. This paper adopts the proportional-integral-derivative (PID) and the linear quadratic regulator controllers to construct the leader–follower formation. Tuning the PID controllers is generally empirical; hence, various surrogate models have been introduced to identify more refined parameters with relatively lower cost. However, the construction of surrogate models faces the problem that the singular points may affect the accuracy, such that the global surrogate models may be invalid. Thus, to tune controllers quickly and accurately, the regional surrogate model technique (RSMT), based on analyzing the regional information entropy, is proposed. The proposed RSMT cooperates only with the successful samples to mitigate the effect of singular points along with a classifier screening failed samples. Implementing the RSMT with various kinds of surrogate models, this study evaluates the Pareto fronts of the original simulation model and the RSMT to compare their effectiveness. The results show that the RSMT can accurately reconstruct the simulation model. Compared with the global surrogate models, the RSMT reduces the run time of tuning PID controllers by one order of magnitude, and it improves the accuracy of surrogate models by dozens of orders of magnitude.

Stabilizing and Swinging-Up the Inverted Pendulum Using PI and PID Controllers Based on Reduced Linear Quadratic Regulator Tuned by PSO

2015 ◽  
Vol 4 (4) ◽  
pp. 52-69 ◽  
Author(s):  
M. E. Mousa ◽  
M. A. Ebrahim ◽  
M. A. Moustafa Hassan
Keyword(s):  
Pid Controller ◽  
Inverted Pendulum ◽  
Research Work ◽  
Linear Quadratic Regulator ◽  
Nonlinear Problems ◽  
Pid Controllers ◽  
Linear Quadratic ◽  
Swarm Optimization ◽  
Feed Forward ◽  
Forward Gain

The inherited instabilities in the Inverted Pendulum (IP) system make it one of the most difficult nonlinear problems in the control theory. In this research work, Proportional –Integral and Derivative (PID) Controller with a feed forward gain is used with Reduced Linear Quadratic Regulator (RLQR) for stabilizing the Cart Position and Swinging-up the Pendulum angle. Tuning the Controllers' gains is achieved by using Particle Swarm Optimization (PSO) Technique. Obtaining the combined PID controllers' gains with a feed forward gain and RLQR is a multi-dimensions control problem. The Proposed Controllers give minimum Settling Time, Rise Time, Undershoot and Over shoot for both the Cart Position and the Pendulum angle. A disturbance with different amplitudes is applied to the system, and the results showed the robustness of the systems based on the tuned controllers. The overall results are promising.

Comparison of Performance Measures of Speed Control for a DC Motor Using Hybrid Intelligent Controller and Optimal LQR

2014 ◽  
Vol 622 ◽  
pp. 23-31
Author(s):  
T. Velayudham Narmadha ◽  
Chackaravarthy Baskaran ◽  
K. Sivakumar
Keyword(s):  
Rise Time ◽  
Linear Quadratic Regulator ◽  
Speed Control ◽  
Dc Motor ◽  
Pid Controllers ◽  
Rule Base ◽  
Fuzzy Pid ◽  
Linear Quadratic ◽  
Lqr Controller ◽  
Fuzzy Pd

-In this paper , performance of fuzzy PD , fuzzy PI , fuzzy PD+I , fuzzy PID controllers are evaluated and compared. This paper also describes the speed control based on Linear Quadratic Regulator (LQR) technique. The comparison is based on their ability of controlling the speed of DC motor, which merely focuses on performance index of the controllers, and also time domain specifications such as rise time, settling time and peak overshoot. The controller is modelled using MATLAB software, the simulation results shows that the fuzzy PID controllers are the best performing candidates in all aspects but it as higher overshoot and IAE in comparison with optimal LQR. The Fuzzy PI controller exhibited null offset but suffers from poor stability and peak overshoot, whereas the fuzzy PD controller has fast rise time, with no overshoots but the IAE is much greater. Thus, the comparative analysis recommends fuzzy PID controller but it is usually associated with complicated rule base and tedious tuning. To circumvent these problems, the proposed LQR controller gives better performance than the other controllers.

scholarly journals System Identification and LQR Controller Design with Incomplete State Observation for Aircraft Trajectory Tracking

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5354
Author(s):  
Piotr Lichota ◽  
Franciszek Dul ◽  
Andrzej Karbowski
Keyword(s):  
Simulation Model ◽  
Trajectory Tracking ◽  
Controller Design ◽  
Linear Quadratic Regulator ◽  
Atmospheric Conditions ◽  
Nonlinear Transport ◽  
Linear Quadratic ◽  
Likelihood Principle ◽  
Tracking Errors ◽  
Lqr Controller

This paper presents a controller design process for an aircraft tracking problem when not all states are available. In the study, a nonlinear-transport aircraft simulation model was used and identified through Maximum Likelihood Principle and Extended Kalman Filter. The obtained mathematical model was used to design a Linear–Quadratic Regulator (LQR) with optimal weighting matrices when not all states are measured. The nonlinear aircraft simulation model with LQR controller tracking abilities were analyzed for multiple experiments with various noise levels. It was shown that the designed controller is robust and allows for accurate trajectory tracking. It was found that, in ideal atmospheric conditions, the tracking errors are small, even for unmeasured variables. In wind presence, the tracking errors were proportional to the wind velocity and acceptable for small and moderate disturbances. When turbulence was present in the experiment, state variable oscillations occurred that were proportional to the turbulence intensity and acceptable for small and moderate disturbances.

scholarly journals Uncertainty Analysis in the Optimization Process of Groundwater Exploitation Scheme Based on SVR Method—A Case Study of Hetao Plain

2018 ◽  
Author(s):  
Yongkai An ◽  
Wenxi Lu ◽  
Xueman Yan
Keyword(s):  
Uncertainty Analysis ◽  
Simulation Model ◽  
Optimization Model ◽  
Surrogate Model ◽  
Surrogate Models ◽  
Groundwater Table ◽  
Pumping Rate ◽  
Groundwater Exploitation ◽  
Computational Load ◽  
Hetao Plain

This paper introduces a surrogate model to reduce the huge computational load in the process of simulation-optimization and uncertainty analysis. First, the groundwater numerical simulation model was established, calibrated and verified in the northeast of Hetao Plain. Second, two surrogate models of simulation model were established using support vector regression (SVR) method, one (surrogate model A, SMA) was used to describe the corresponding relationship between the pumping rate and average groundwater table drawdown, and another (surrogate model B, SMB) was used to express the corresponding relationship between the hydrogeological parameter values and average groundwater table drawdown. Third, an optimization model was established to search an optimal groundwater exploitation scheme using the maximum total pumping rate as objective function and the limitative average groundwater table drawdown as constraint condition, the SMA was invoked by the optimization model for obtaining the optimal groundwater exploitation scheme. Finally, the SMB was invoked in the process of uncertainty analysis for assessing the reliability of optimal groundwater exploitation scheme. Results show that the relative error and root mean square error between simulation model and the two surrogate models are both less than 5%, which is a high approximation accuracy. The SVR surrogate model developed in this study could not only considerably reduce the computational load, but also maintain high computational accuracy. The optimal total pumping rate is 7947 m3/d and the reliability of optimal scheme is 40.21%. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme and assessing the reliability of scheme quickly and accurately.

scholarly journals PID AND LQR CONTROLLERS APPLIED TO THE INVERSE DYNAMICS OF A 3-DOF MANIPULATOR

2021 ◽  
Vol 7 (7) ◽  
Author(s):  
Josias Guimarães Batista ◽  
Darielson Araújo de Souza ◽  
Laurinda Lúcia Nogueira dos Reis ◽  
Antônio Barbosa de Souza Júnior
Keyword(s):  
Pid Controller ◽  
Inverse Dynamics ◽  
Production Systems ◽  
Linear Quadratic Regulator ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Dynamics Model ◽  
Linear Quadratic ◽  
Inverse Dynamic ◽  
Industrial Manipulator

The application in the industrial manipulator robots has grown over the years making production systems increasingly efficient. Within this context, the need for efficient controllers is required to perform the control of these manipulators. In this work the PID controller (Proportional-Integral-Derivative) and LQR (Linear Quadratic Regulator) is presented from the inverse dynamics model of a RPP (Rotational - Prismatic - Prismatic) cylindrical manipulator. The inverse dynamic model which is modeled on Simulink together with a cascaded PID controller is presented. The PID and LQR results are also presented for joint independent and joint dependent control, i.e a controlled PID is used for each joint, controlling the trajectories and speeds at the same time. This paper has as main contributions the development of the manipulator dynamics model and the design of the LQR and PID controllers applied to the inverse dynamics model, which makes the system simpler to control.

Adaptive Kriging surrogate model for the optimization design of a dense non-aqueous phase liquid-contaminated groundwater remediation process

2014 ◽  
Vol 15 (2) ◽  
pp. 263-270 ◽  
Author(s):  
Haibo Chu ◽  
Wenxi Lu
Keyword(s):  
Simulation Model ◽  
Aqueous Phase ◽  
Surrogate Model ◽  
Optimization Design ◽  
Integrated Approach ◽  
Surrogate Models ◽  
Kriging Surrogate Model ◽  
The Common ◽  
Adaptive Kriging ◽  
Remediation Process

The optimization model needs to call the simulation model to calculate the response under different conditions for many times, and this is computationally expensive and time-consuming. To solve this problem, surrogate models can be used to yield insight into the functional relationship between the design variables and the responses, instead of simulation models in the optimization. In this paper, an integrated optimization method based on adaptive Kriging surrogate models was proposed and applied to the cost optimization of a surfactant enhanced aquifer remediation process for dense non-aqueous phase liquids (DNAPLs). First, the initial samples were created by Latin hypercube sampling, and then the responses corresponding to the initial samples were computed by a simulation model. The initial Kriging model was derived through these samples. Secondly, the adaptive Kriging surrogate model was proposed based on updating initial Kriging with new samples via infill sampling criteria. The results showed that it had improved the accuracy of the surrogate model, and the added samples had provided more information about the simulation model than the common samples. Even with the same number of samples, the adaptive Kriging surrogate model performed better than the common Kriging surrogate model, which was built only once. What's more, the integrated approach not only greatly reduced the computational burden, but also determined the actual optimal DNAPLs remediation strategy.

scholarly journals A surrogate-based simulation–optimization approach for coastal aquifer management

2020 ◽  
Vol 20 (8) ◽  
pp. 3404-3418
Author(s):  
Zheng Han ◽  
Wenxi Lu ◽  
Yue Fan ◽  
Jin Lin ◽  
Qian Yuan
Keyword(s):  
Simulation Model ◽  
Groundwater Management ◽  
Optimization Model ◽  
Surrogate Model ◽  
Simulation Optimization ◽  
Surrogate Models ◽  
Variable Density ◽  
Support Vector ◽  
Aquifer Management ◽  
Groundwater Simulation

Abstract This study proposed a pumping-injection (P-I) groundwater management strategy based on a simulation–optimization (S-O) framework to mitigate seawater intrusion (SI). The methodology was applied to a real case in Longkou, China. A three-dimensional variable-density groundwater simulation model was established to simulate and predict the SI process. In the S-O framework, while solving the optimization model, it is required to call the simulation model thousands of times, which leads to enormous computational load. In this case, the Kriging and support vector regression (SVR) surrogate models were established for the simulation model respectively. Furthermore, the ensemble surrogate modeling technique was applied to construct the Kriging-SVR ensemble surrogate model. The most accurate surrogate model was selected as the substitute for the simulation model, saving considerable computing costs. The results show that the ensemble surrogate model performs better than the stand-alone surrogate models in accuracy, indicating that combining stand-alone surrogate models is a potential modeling method for the surrogate model of the variable-density groundwater simulation model. By solving the optimization model, the optimal pumping and injection schemes under different scenarios were obtained. The optimization results demonstrate that the proposed methodology is effective and stable in coastal groundwater management.

A Study on Linear Quadratic Regulator with the Desired Low-Pass Property

2013 ◽  
Vol 133 (12) ◽  
pp. 2167-2175 ◽  
Author(s):  
Katsuhiko Fuwa ◽  
Satoshi Murayama ◽  
Tatsuo Narikiyo
Keyword(s):  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Low Pass
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