scholarly journals Study on maintaining formations during satellite formation flying based on SDRE and LQR

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 394-399 ◽  
Author(s):  
Zhang Ke ◽  
He Zhenqi ◽  
Lv Meibo
Keyword(s):  
Optimal Control ◽  
Control Algorithm ◽  
Formation Flying ◽  
Control Method ◽  
Process Time ◽  
Engineering Practice ◽  
Continuous Control ◽  
Linear Quadratic ◽  
Orbit Element ◽  
Lqr Controller

AbstractDue to the influence of various perturbations of space, satellites flying in formation cannot maintain specific configurations for long durations [1, 2]. In order to ensure that formation configurations are able to meet the requirements of space missions, it is important to maintain control of formation configurations. This is an urgent problem to be solved. The traditional control method for controlling formations is based on the average orbit element, and uses the assumption that the average orbit element deviation and the instantaneous orbit element deviation are approximately equal. However, the continuous control system is more difficult to achieve in engineering practice. Using a LQR (linear quadratic regulator) optimal control algorithm and SDRE (state-dependent Riccati equation) optimal control algorithm to maintain the formation flying [3, 4]. Through simulation, it was found that when using the SDRE controller in the system transition process time is shorter than when the LQR controller is used, and fuel consumption is less for the SDRE controller than for the LQR controller.

scholarly journals Research on Control Method of Keeping Flight Formation by Using SDRE on the Sun-Earth Libration Points

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
He Zhenqi ◽  
Zhang Ke ◽  
Lv Meibai
Keyword(s):  
Control Method ◽  
Libration Point ◽  
Linear Quadratic Regulator ◽  
Transition Process ◽  
Continuous Control ◽  
The Sun ◽  
Nonlinear Dynamic Model ◽  
Linear Quadratic ◽  
State Dependent ◽  
Lqr Controller

Keeping the flying formation of spacecraft is a key problem which needs to be solved in deep space exploration missions. In this paper, the nonlinear dynamic model of formation flying is established and a series of transformations are carried out on this model equation. By using SDRE (State-Dependent Riccati Equation) algorithm, the optimal control of flying formation is realized. Compared with the traditional control method based on the average orbit elements and LQR (Linear Quadratic Regulator) control method, the SDRE control method has higher control precision and is more suitable for the advantages of continuous control in practical engineering. Finally, the parameter values of the sun-earth libration point L2 are substituted in the equation and simulation is performed. The simulation curves of SDRE controller are compared with LQR controller. The results show that the SDRE controllers time cost is less than the LQR controllers and the former’s fuel consumption is less than the latter’s in the system transition process.

scholarly journals Nonlinear Control Design of a Half-Car Model Using Feedback Linearization and an LQR Controller

2020 ◽  
Vol 10 (9) ◽  
pp. 3075
Author(s):  
Muhammad Aseer Khan ◽  
Muhammad Abid ◽  
Nisar Ahmed ◽  
Abdul Wadood ◽  
Herie Park
Keyword(s):  
Feedback Linearization ◽  
Control Method ◽  
Control Technique ◽  
Effective Control ◽  
Ride Quality ◽  
Linear Quadratic ◽  
Suspension Systems ◽  
Car Model ◽  
Road Disturbance ◽  
Lqr Controller

Effective control of ride quality and handling performance are challenges for active vehicle suspension systems, particularly for off-road applications. The nonlinearities tend to degrade the performance of active suspension systems; these introduce harshness to the ride quality and reduce off-road mobility. Typical control strategies rely on linear models of the suspension dynamics. While these models are convenient, nominally accurate, and controllable due to the abundance of linear control techniques, they neglect the nonlinearities present in real suspension systems. The techniques already implemented and methods used to cope with problem of Half-Car model were studied. Every method and technique had some drawbacks in terms of complexity, cost-effectiveness, and ease of real time implementation. In this paper, an improved control method for Half-Car model was proposed. First, input/output feedback linearization was performed to convert the nonlinear system of Half-Car model into an equivalent linear system. This was followed by a Linear Quadratic Regulator (LQR) controller. This controller had minimized the effects of road disturbances by designing a gain matrix with optimal robustness properties. The proposed control technique was applied in the presence of the deterministic road disturbance. The results were verified using the Matlab/Simulink toolbox.

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 Pre-Work for the Birth of Driver-Less Scraper (LHD) in the Underground Mine: The Path Tracking Control Based on an LQR Controller and Algorithms Comparison

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7839
Author(s):  
Haoxuan Yu ◽  
Chenxi Zhao ◽  
Shuai Li ◽  
Zijian Wang ◽  
Yulin Zhang
Keyword(s):  
Tracking Control ◽  
Control Method ◽  
Clustering Algorithms ◽  
Development Trend ◽  
Path Tracking ◽  
Linear Quadratic ◽  
Trajectory Tracking Control ◽  
Linear Quadratic Optimal Control ◽  
The Future ◽  
Lqr Controller

With the depletion of surface resources, mining will develop toward the deep surface in the future, the objective conditions such as the mining environment will be more complex and dangerous than now, and the requirements for personnel and equipment will be higher and higher. The efficient mining of deep space is inseparable from movable and flexible production and transportation equipment such as scrapers. In the new era, intelligence is leading to the development trend of scraper (LHD), path tracking control is the key to the intelligent scraper (LHD), and it is also an urgent problem to be solved for unmanned driving. This paper describes the realization of the automatic operation of articulating the scraper (LHD) from two aspects, a mathematical model and trajectory tracking control method, and it focuses on the research of the path tracking control scheme in the field of unmanned driving, that is, an LQR controller. On this basis, combined with different intelligent clustering algorithms, the parameters of the LQR controller are optimized to find the optimal solution of the LQR controller. Then, the path tracking control of an intelligent LHD unmanned driving technology is studied, focusing on the optimization of linear quadratic optimal control (LQR) and the intelligent cluster algorithms AGA, QPSO, and ACA; this research has great significance for the development of the intelligent scraper (LHD). As mining engineers, we not only need to conduct research for practical engineering projects but also need to produce theoretical designs for advanced mining technology; therefore, the area of intelligent mining is the one we need to explore at present and in the future. Finally, this paper serves as a guide to starting a conversation, and it has implications for the development and the future of underground transportation.

Numeric Approach on Optimal Control for the Path Following System in Autonomous Vehicle

2019 ◽  
Author(s):  
Huyao Wu ◽  
Bin Ran
Keyword(s):  
Optimal Control ◽  
Control Strategies ◽  
Autonomous Vehicle ◽  
Path Following ◽  
Linear Quadratic Regulator ◽  
State Feedback Control ◽  
Linear Quadratic ◽  
The Road ◽  
Lqr Controller ◽  
Dynamic Errors

Abstract In this paper, the control strategies for Path Following System (PFS) in autonomous vehicle, which lets vehicle stay in the center of its lane is discussed, we will create a plant mechanical, mathematical and error dynamics model for the study of PFS, which is stabilized by the state-feedback control law, also considers the output where the sensor is made. We apply mainly an optimal control or configure a Linear-quadratic Regulator (LQR) for state space systems and compare it to that based on the Pole Assignment (PA). Combined with a typical operating scenario of the road, we mainly consider static and dynamic errors in the moving process, and how intensely the error fluctuates and how errors are related to the next time. Figures and data show that the LQR controller successfully adjusts and gives appropriate input to let the vehicle approach to centerline, errors and the steering angle required to negotiate a curved road are presented and analyzed, finally relevant conclusions are drawn.

scholarly journals Absolute Instantaneous Optimal Control Performance Index for Active Vibration Control of Structures under Seismic Excitation

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Arcan Yanik
Keyword(s):  
Optimal Control ◽  
Performance Index ◽  
Energy Demand ◽  
Control Method ◽  
Seismic Excitation ◽  
Numerical Verification ◽  
Control Performance ◽  
Linear Quadratic ◽  
Near Fault ◽  
Instantaneous Optimal Control

In this paper, an instantaneous optimal control performance index for active control of structures under seismic excitation is analytically proposed. Absolute velocity and absolute displacement terms are implemented to the conventional state vector terms and eventually to the resulting performance index expression. The seismic response reduction effectiveness of the proposed performance index is compared with the linear quadratic regulator control (LQR). For numerical verification of the performance index, an eight-story shear building with a fully active tendon controller system under unidirectional earthquake is considered as the first example. For a more complex model, a three-dimensional tier building under the effect of bidirectional earthquakes is selected as second numerical example. Unidirectional near fault and bidirectional near fault earthquakes are used in the simulations. The control energy demand of each control method is also considered in the comparison. It is obtained from numerical simulations that the proposed performance index is as effective as LQR in attenuating structural vibrations. However, the resulting performance index does not require a priori knowledge of the seismic excitation like the LQR. The nonlinear Riccati matrix equation solution of the LQR is not required in the proposed performance index as well.

scholarly journals Active Vibration Control of the Sting Used in Wind Tunnel: Comparison of Three Control Algorithms

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xing Shen ◽  
Yuke Dai ◽  
Mingxuan Chen ◽  
Lei Zhang ◽  
Li Yu
Keyword(s):  
Optimal Control ◽  
Wind Tunnel ◽  
Vibration Control ◽  
Control Algorithm ◽  
Active Vibration Control ◽  
Linear Quadratic Regulator ◽  
Real Time Control ◽  
Linear Quadratic ◽  
Control Effort ◽  
Active Vibration

In wind tunnel tests, cantilever stings are often used as model-mount in order to reduce flow interference on experimental data. In this case, however, large-amplitude vibration and low-frequency vibration are easily produced on the system, which indicates the potential hazards of gaining inaccurate data and even damaging the structure. This paper details three algorithms, respectively, Classical PD Algorithm, Artificial Neural Network PID (NNPID), and Linear Quadratic Regulator (LQR) Optimal Control Algorithm, which can realize active vibration control of sting used in wind tunnel. The hardware platform of the first-order vibration damping system based on piezoelectric structure is set up and the real-time control software is designed to verify the feasibility and practicability of the algorithms. While the PD algorithm is the most common method in engineering, the results show that all the algorithms can achieve the purpose of over 80% reduction, and the last two algorithms perform even better. Besides, self-tuning is realized in NNPID, and with the help of the Observer/Kalman Filter Identification (OKID), LQR optimal control algorithm can make the control effort as small as possible. The paper proves the superiority of NNPID and LQR algorithms and can be an available reference for vibration control of wind tunnel system.

An improved linear quadratic regulator control method through convolutional neural network–based vibration identification

2020 ◽  
pp. 107754632093375
Author(s):  
Xinzheng Lu ◽  
Wenjie Liao ◽  
Wei Huang ◽  
Yongjia Xu ◽  
Xingyu Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Control Algorithm ◽  
Control Method ◽  
Wavelet Coefficient ◽  
Linear Quadratic Regulator ◽  
Optimal Parameters ◽  
Linear Quadratic ◽  
Negative Effects ◽  
Environmental Vibration

An efficient vibration control can reduce negative effects induced by environmental vibrations and thereby improve the performance of precision instruments and the qualities of manufacture. The performance of the widely used linear quadratic regulator control algorithm, a classical active control methodology, depends on the parameters of the control algorithm. Consequently, a set of fixed parameters cannot satisfy the demand for controlling various types of environmental vibrations. Therefore, this study proposes a vibration identification method based on a convolutional neural network. This method helps to optimize the linear quadratic regulator algorithm by selecting corresponding optimal parameters according to the identification results, thereby achieving the objective of optimal control subjected to various types of vibration inputs. Specifically, environmental vibration signals are collected, and the preliminary features of the vibrations (i.e. wavelet coefficient matrices or images) are adopted as input samples for the convolutional neural network. A genetic algorithm is used to optimize the parameters of the linear quadratic regulator algorithm for each type of vibration; subsequently, the trained convolutional neural network model with the best performance is used to identify the vibration and select the corresponding optimal parameters of the linear quadratic regulator algorithm under different types of vibration inputs. Case studies show that the performance of the improved linear quadratic regulator control method is significantly better than that of the conventional linear quadratic regulator algorithm with fixed parameters.

scholarly journals Inverse optimal control from incomplete trajectory observations

2021 ◽  
pp. 027836492199638
Author(s):  
Wanxin Jin ◽  
Dana Kulić ◽  
Shaoshuai Mou ◽  
Sandra Hirche
Keyword(s):  
Optimal Control ◽  
Objective Function ◽  
Control Algorithm ◽  
Robot Manipulator ◽  
Linear Quadratic Regulator ◽  
Inverse Optimal Control ◽  
Linear Quadratic ◽  
System States ◽  
Segment Data ◽  
The Relationship

This article develops a methodology that enables learning an objective function of an optimal control system from incomplete trajectory observations. The objective function is assumed to be a weighted sum of features (or basis functions) with unknown weights, and the observed data is a segment of a trajectory of system states and inputs. The proposed technique introduces the concept of the recovery matrix to establish the relationship between any available segment of the trajectory and the weights of given candidate features. The rank of the recovery matrix indicates whether a subset of relevant features can be found among the candidate features and the corresponding weights can be learned from the segment data. The recovery matrix can be obtained iteratively and its rank non-decreasing property shows that additional observations may contribute to the objective learning. Based on the recovery matrix, a method for using incomplete trajectory observations to learn the weights of selected features is established, and an incremental inverse optimal control algorithm is developed by automatically finding the minimal required observation. The effectiveness of the proposed method is demonstrated on a linear quadratic regulator system and a simulated robot manipulator.

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