scholarly journals ENHANCEMENT OF STABILITY ON AUTONOMOUS WAYPOINT MISSION OF QUADROTOR USING LQR INTEGRATOR CONTROL

2022 ◽  
Vol 23 (1) ◽  
pp. 129-158
Author(s):  
Oktaf Agni Dhewa ◽  
Tri Kuntoro Priyambodo ◽  
Aris Nasuha ◽  
Yasir Mohd Mustofa
Keyword(s):  
Steady State ◽  
Trajectory Tracking ◽  
Control Method ◽  
Linear Quadratic Regulator ◽  
System Specification ◽  
Linear Quadratic ◽  
Environmental Disturbances ◽  
Essential Requirement ◽  
Lqr Control ◽  
State Error

The ability of the quadrotor in the waypoint trajectory tracking becomes an essential requirement in the completion of various missions nowadays. However, the magnitude of steady-state errors and multiple overshoots due to environmental disturbances leads to motion instability. These conditions make the quadrotor experience a shift and even change direction from the reference path. As a result, to minimize steady-state error and multiple overshoots, this study employs a Linear Quadratic Regulator control method with the addition of an Integrator. Comparisons between LQR without Integrator and LQR with Integrator were performed. They were implemented on a quadrotor controller to track square and zig-zag waypoint patterns. From experimental results, LQR without Integrator produce of 2 meters steady-state error and -1.04 meters undershoot average with an accuracy of 64.84 % for square pattern, along 3.19 meters steady-state error, and -1.12 meters undershoot average with an accuracy of 46.73 % for a zig-zag way. The LQR method with integrator produce of 1.06 meters steady-state error with accuracy 94.96 % without multiple-overshoot for square pattern, the 1.06 meters steady-state error, and -0.18 meters undershoot average with an accuracy of 86.49 % for the zig-zag way. The results show that the LQR control method with Integrator can minimize and improve steady-state error and multiple overshoots in quadrotor flight. The condition makes the quadrotor able to flying path waypoints with the correct system specification. ABSTRAK: Kemampuan quadrotor dalam pengesanan lintasan waypoint menjadi syarat penting dalam menyelesaikan pelbagai misi pada masa kini. Walau bagaimanapun, besarnya ralat keadaan mantap dan banyak kelebihan kerana gangguan persekitaran menyebabkan ketidakstabilan pergerakan. Keadaan ini menjadikan quadrotor mengalami pergeseran dan bahkan mengubah arah dari jalur rujukan. Oleh itu, kajian ini menggunakan kaedah kawalan Linear Quadratic Regulator dengan penambahan integrator dalam meminimumkan ralat keadaan mantap dan banyak kelebihan. Perbandingan antara LQR tanpa Integrator dan LQR dengan Integrator dilakukan. Mereka dilaksanakan pada pengawal quadrotor untuk mengesan corak titik jalan persegi dan zig-zag. Dari hasil eksperimen, LQR tanpa Integrator menghasilkan ralat keadaan mantap 2 meter dan -1.04 meter rata-rata undur tembak dengan ketepatan 64.84% untuk corak persegi, sepanjang ralat keadaan tetap 3.19 meter, dan -1.12 meter rata-rata undur bawah dengan ketepatan 46.73 % untuk cara zig-zag. Kaedah LQR dengan integrator menghasilkan ralat keadaan mantap 1.06 meter dengan ketepatan 94.96% tanpa tembakan berlebihan untuk corak segi empat sama, ralat keadaan mantap 1.06 meter, dan rata-rata undur tembak -0.18 meter dengan ketepatan 86.49% untuk zig-zag cara. Hasilnya menunjukkan bahawa kaedah kawalan LQR dengan Integrator dapat meminimumkan dan memperbaiki ralat keadaan mantap dan banyak overhoot dalam penerbangan quadrotor. Keadaan tersebut menjadikan quadrotor dapat terbang ke titik jalan dengan spesifikasi sistem yang betul.

scholarly journals System design for inverted pendulum using LQR control via IoT

2020 ◽  
Vol 11 ◽  
pp. 12
Author(s):  
Dechrit Maneetham ◽  
Petrus Sutyasadi
Keyword(s):  
Inverted Pendulum ◽  
Control Method ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Lqr Controller ◽  
Model Tuning ◽  
And Performance ◽  
Performance Results ◽  
And Control

This research proposes control method to balance and stabilize an inverted pendulum. A robust control was analyzed and adjusted to the model output with real time feedback. The feedback was obtained using state space equation of the feedback controller. A linear quadratic regulator (LQR) model tuning and control was applied to the inverted pendulum using internet of things (IoT). The system's conditions and performance could be monitored and controlled via personal computer (PC) and mobile phone. Finally, the inverted pendulum was able to be controlled using the LQR controller and the IoT communication developed will monitor to check the all conditions and performance results as well as help the inverted pendulum improved various operations of IoT control is discussed.

scholarly journals KONTROL SUDUT ATTITUDE MENGGUNAKAN LINEAR QUADRATIC REGULATOR (LQR) UNTUK QUADROTOR DENGAN PAYLOAD

ROTASI ◽  
2013 ◽  
Vol 15 (4) ◽  
pp. 16 ◽  
Author(s):  
Mochammad Ariyanto ◽  
Joga Dharma Setiawan ◽  
Ismoyo Haryanto
Keyword(s):  
Steady State ◽  
State Space ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Aerial Manipulation ◽  
Step Input ◽  
Object Interaction ◽  
Integral Action ◽  
Linear State ◽  
State Error

Quadrotor telah dikembangkan oleh peneliti di dunia ini sebagai aerial object interaction/aerial manipulation. Agar quadrotor bisa terbang mengangkat beban, maka dibutuhkan sistem kontrol yang dapat mengkompensasi perubahan pusat gravitasi. Pada penelitian ini, model matematika quadrotor dengan efek beban/payload akan dikembangkan. Untuk mengkompensasi perubahan lokasi pusat gravitasi karena efek beban, Linear Quadratic Regulator (LQR) akan digunakan untuk stabilisasi sudut Euler.Model linear state space dihitung menggunakan persmaan gerak onlinear quadrotor tanpa beban, kemudian disintesis sistem kontrol   LQR. Kontrol tersebut diterapkan dalam model matematika nonlinear dengan beban/payload. Berdasarkan hasil simulasi integral action dari kontrol LQR dapat menghilangkan steady state error setelah diberikan step input, dan kondisi sudut awal sebesar 50. Variasi beban dari 0 gr sampai dengan 200 gr tidak akan memberikan steady state error

scholarly journals A didactic platform for the study of Linear Quadratic Regulator (LQR) control for Trajectory Tracking of dc motor

2021 ◽  
pp. 10-17
Author(s):  
Joaquin Hernández-Santiago ◽  
Beatris Escobedo-Trujillo ◽  
Javier Garrido
Keyword(s):  
Trajectory Tracking ◽  
Linear Quadratic Regulator ◽  
Dc Motor ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Motor Model

The main objective of this work is to show in detail the methodology to apply the Linear Quadratic Regulator (LQR) for Trajectory Tracking in an experimental way in a didactic platform which consists of a DC motor, the motor model is explained, and how the parameters were estimated experimentally and the validation thereofs. Results are shown applying the LQR control in simulation and experimentally.

scholarly journals A Model Predictive Water-Level Difference Control Method for Automatic Control of Irrigation Canals

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 762 ◽  
Author(s):  
Kong ◽  
Lei ◽  
Wang ◽  
Long ◽  
Lu ◽  
...  
Keyword(s):  
Automatic Control ◽  
Water Level ◽  
Control Strategy ◽  
Control Method ◽  
Linear Quadratic Regulator ◽  
Automatic Regulation ◽  
Irrigation Canal ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Level Difference

In this paper, automatic control of the water level in an irrigation canal by automatic regulation of intermediate gates was studied. Previous scholars have proposed a water level difference control strategy that works to keep relative deviations in all pools the same for a particular situation where the operator does not have full control over the canal inflow, with the centralized linear quadratic regulator (LQR) control method used. While in practice, the deviation tolerance of pools may differ in some canals which limits the applicability of the control strategy. In this work, a weight coefficient was added to the deviation and the algorithm was improved to keep the relative deviations to certain proportions. The model predictive control (MPC) method was then used with this improved control strategy and was compared to the LQR control method using the same control strategy. The results showed that the improved strategy can keep the water level deviations in all pools to certain proportions, as is our objective. Also, under this difference control strategy, the MPC method greatly improved the control performance compared to the LQR control method.

scholarly journals Vibration Control Method of an Electromagnetic Isolation System Based on LQR and Coevolutionary NGA

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Lei Zhang ◽  
Xiangtao Zhuan
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Active Control ◽  
Vibration Isolation ◽  
Control Method ◽  
Linear Quadratic Regulator ◽  
State Equation ◽  
Linear Quadratic ◽  
Isolation System ◽  
Vibration Isolation System

An electromagnetic isolation system can dynamically adjust the output characteristic parameters of the system in real time through the active control strategy, which has strong adaptability to the external environment. In order to control the electromagnetic vibration isolation system effectively, an active control method is presented based on the linear quadratic regulator (LQR) approach and the coevolutionary niche genetic algorithm (NGA). In this paper, the dynamical equation and state equation of the electromagnetic isolation system are built, which include the nonlinear relationship between electromagnetic force and coil current and gap. The LQR approach is employed to maintain a steady state of an isolated object on the vibration isolation system. Meanwhile, a coevolutionary niche genetic algorithm is put forward to optimize the parameters in Q and R matrices. Simulation and experimental results demonstrate that the electromagnetic isolation system with the LQR approach and coevolutionary NGA can effectively isolate the vibration and maintain a steady state for an isolated object in comparison with the passive isolation system.

Modeling and Control of a Hybrid Wheeled Legged Robot: Disturbance Analysis

2020 ◽  
Author(s):  
Fahad Raza ◽  
Dai Owaki ◽  
Mitsuhiro Hayashibe
Keyword(s):  
Position Control ◽  
Control Method ◽  
System Modeling ◽  
Linear Quadratic Regulator ◽  
Human Robot Interaction ◽  
Physical Disorder ◽  
Legged Robot ◽  
Linear Quadratic ◽  
Control Approach ◽  
Lqr Control

Abstract The most common cause of injuries among older adults is falling. Recently, there have been numerous developments in assistive and exoskeleton systems. However, comparatively little work is being done on systems that may help people to keep an upright position and avoid falling over. In this preliminary work, we investigate the feasibility of the wheel-legged robot as a balance-assist system for the people who cannot maintain balance and walk because of an injury, old age, or neurological or physical disorder. We perform motion stability analyses of the wheel-legged robot under different conditions such as system modeling errors, sensor noise, and external disturbances. The linear quadratic regulator (LQR) control approach is adopted for balancing, steering, and translational position control of the robot. To validate our control framework and visualize results, the robot is modeled and tested in the Gazebo simulator using ROS (Robot Operating System). Subsequently, the simulation results demonstrate the effectiveness of the LQR control method under the translational and rotational pushes of the wheel-legged system for human-robot interaction.

Lateral Stability Analysis for Car-Trailer Combinations

2016 ◽  
Author(s):  
Eungkil Lee ◽  
Tao Sun ◽  
Yuping He
Keyword(s):  
Stability Analysis ◽  
Linear Stability ◽  
Linear Stability Analysis ◽  
Parametric Study ◽  
Degrees Of Freedom ◽  
Linear Quadratic Regulator ◽  
Lateral Stability ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Ct System

This paper presents a parametric study of linear lateral stability of a car-trailer (CT) combination in order to examine the fidelity, complexity, and applicability for control algorithm development for CT systems. Using MATLAB software, a linear yaw-roll model with 5 degrees of freedom (DOF) is developed to represent the CT combination. In the case of linear stability analysis, a parametric study was carried out using eigenvalue analysis based on a linear yaw-roll CT model with varying parameters. Built upon the linear stability analysis, an active trailer differential braking (ATDB) controller was designed for the CT system using the linear quadratic regulator (LQR) technique. The simulation study presented in this paper shows the effectiveness of the proposed LQR control design and the influence of different trailer parameters.

scholarly journals Signature PSO: A novel inertia weight adjustment using fuzzy signature for LQR tuning

2021 ◽  
Vol 10 (1) ◽  
pp. 308-318
Author(s):  
Achmad Komarudin ◽  
Novendra Setyawan ◽  
Leonardo Kamajaya ◽  
Mas Nurul Achmadiah ◽  
Zulfatman Zulfatman
Keyword(s):  
Linear Quadratic Regulator ◽  
Absolute Error ◽  
Control Parameters ◽  
Optimum Control ◽  
Exploration And Exploitation ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Inertia Weight ◽  
Adjustment Strategy ◽  
Complete Optimization

Particle swarm optimization (PSO) is an optimization algorithm that is simple and reliable to complete optimization. The balance between exploration and exploitation of PSO searching characteristics is maintained by inertia weight. Since this parameter has been introduced, there have been several different strategies to determine the inertia weight during a train of the run. This paper describes the method of adjusting the inertia weights using fuzzy signatures called signature PSO. Some parameters were used as a fuzzy signature variable to represent the particle situation in a run. The implementation to solve the tuning problem of linear quadratic regulator (LQR) control parameters is also presented in this paper. Another weight adjustment strategy is also used as a comparison in performance evaluation using an integral time absolute error (ITAE). Experimental results show that signature PSO was able to give a good approximation to the optimum control parameters of LQR in this case.

scholarly journals DYNAMICS BASED CONTROL OF A SKID STEERING MOBILE ROBOT

2016 ◽  
Vol 9 (2) ◽  
pp. 70 ◽  
Author(s):  
Osama Elshazly ◽  
Hossam Abbas ◽  
Zakarya Zyada
Keyword(s):  
Mobile Robot ◽  
Inverse Dynamics ◽  
Linear Quadratic Regulator ◽  
Nonlinear Controller ◽  
Linear Quadratic ◽  
Reduced Order ◽  
Lqr Control ◽  
Control Schemes ◽  
Skid Steering ◽  
Drive Model

In this paper, development of a reduced order, augmented dynamics-drive model that combines both the dynamics and drive subsystems of the skid steering mobile robot (SSMR) is presented. A Linear Quadratic Regulator (LQR) control algorithm with feed-forward compensation of the disturbances part included in the reduced order augmented dynamics-drive model is designed. The proposed controller has many advantages such as its simplicity in terms of design and implementation in comparison with complex nonlinear control schemes that are usually designed for this system. Moreover, the good performance is also provided by the controller for the SSMR comparable with a nonlinear controller based on the inverse dynamics which depends on the availability of an accurate model describing the system. Simulation results illustrate the effectiveness and enhancement provided by the proposed controller.

Composite Fuzzy Control Method for Temperature in Intelligent Moisture Analyzer

2013 ◽  
Vol 401-403 ◽  
pp. 1010-1013
Author(s):  
Jing Ling ◽  
Jin Che ◽  
Da Ming Liu
Keyword(s):  
Steady State ◽  
Fuzzy Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Time Lag ◽  
Threshold Value ◽  
National Standards ◽  
Infrared Heating ◽  
Temperature Control System ◽  
State Error

Temperature control system of infrared heating oven in moisture analyzer is characteristic of nonlinear, time-varying and time-lag. A composite fuzzy control (CFC) method is proposed, which combines improved Bang-Bang control with two-stage intelligent fuzzy control. The control algorithm is implemented by MSP430F5438. When the temperature error e between the desired temperature and actual temperature in heating oven is larger than threshold value, the improved Bang-Bang controller is employed in rapidly reducing the error; to decrease the system overshoot, the basic fuzzy controller is used; to reduce the steady-state error of basic fuzzy controller, the auxiliary fuzzy controller is applied. The steady-state error of improved fuzzy controller for oven temperature is less than 0.5°C, which is better than the Chinese National Standards for moisture content measurement.

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