Flight control law of unmanned aerial vehicles based on robust servo linear quadratic regulator and Kalman filtering

Zhi, Yongfeng; Gaoshang, LI; Song, Qun; Ke, Yubin; Zhang, Jun

doi:10.1177/1729881416686952

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…In this way, the control signal (u) is the sum of the adaptive control signal u 1 and the LQR signal u 2 . In the system, servo system's states that are purified from the noise are estimated by the KF and the state space feedback of the system is actualized by the optimal K value computed by LQR for initial load [10]. In addition, adaptive control was added to eliminate the effects of the changing load parameters.…”

Section: State Estimation With Kalman Filtermentioning

confidence: 99%

“…And so, a response curve close to the desired reference value is obtained [7,8]. LQR control is used with a KF that estimates the real states of the system in noisy environments in state feedback servo control systems [9,10]. Lyapunov stability criteria and MIT rule are frequently used methods in designing traditional adaptive control systems to increase the system stability in time varying systems.…”

Section: Introductionmentioning

confidence: 99%

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Kalman State Estimation and LQR Assisted Adaptive Control Of a Variable Loaded Servo System

Aydoğdu

Levent

2019

Eng. Technol. Appl. Sci. Res.

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This study actualized a new hybrid adaptive controller design to increase the control performance of a variable loaded time-varying system. A structure in which LQR and adaptive control work together is proposed. At first, a Kalman filter was designed to estimate the states of the system and used with the LQR control method which is one of the optimal control servo system techniques in constant initial load. Then, for the variable loaded servo (VLS) system, the Lyapunov based adaptive control was added to the LQR control method which was inadequate due to the constant gain parameters. Thus, it was aimed to eliminate the variable load effects and increase the stability of the system. In order to show the effectiveness of the proposed method, a Quanser servo module was used in Matlab-Simulink environment. It is seen from the experimental results and performance measurements that the proposed method increases the system performance and stability by minimizing noise, variable load effect and steady-state error.

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Section: State Estimation With Kalman Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Kalman State Estimation and LQR Assisted Adaptive Control Of a Variable Loaded Servo System

Aydoğdu

Levent

2019

Eng. Technol. Appl. Sci. Res.

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“…Zhi et al [24] proposed an optimal LQR controller combine with Kalman Filter to control the quadrotor's attitude angle. They conducted the simulation for noise-free and under noise conditions to appreciate Kalman Filter's performance in rejecting the noise.…”

Section: Introductionmentioning

confidence: 99%

An effective proportional-double derivative-linear quadratic regulator controller for quadcopter attitude and altitude control

2021

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A quadcopter control system is a fundamentally difficult and challenging problem because its dynamics modelling is highly nonlinear, especially after accounting for the complicated aerodynamic effects. Plus, its variables are highly interdependent and coupled in nature. There are six controllers studied and analysed in this work which are (1) Proportional-Integral-Derivative (PID), (2) Proportional-Derivative (PD), (3) Linear Quadratic Regulator (LQR), ( 4) Proportional-Linear Quadratic Regulator (P-LQR), ( 5) Proportional-Derivative-Linear Quadratic Regulator (PD-LQR) and lastly (6) the proposed controller named Proportional-Double Derivative-Linear Quadratic Regulator (PD2-LQR) controller. The altitude control and attitude stabilization of the quadcopter have been investigated using MATLAB/Simulink software. The mathematical model of the quadcopter using the Newton-Euler approach is applied to these controllers has illuminated the attitude (i.e. pitch, yaw, and roll) and altitude motions of the quadcopter. The simulation results of the proposed PD2-LQR controller have been compared with the PD, PID, LQR, P-LQR, and PD-LQR controllers. The findings elucidated that the proposed PD2-LQR controller significantly improves the performance of the control system in almost all responses. Hence, the proposed PD2-LQR controller can be applied as an alternative controller of all four motions in quadcopters.

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“…Thus, a response curve close to the desired reference value is obtained. LQR control is used with the Kalman Filter that estimates the real states of the system in noisy environments in state space feedback servo control systems [15], [16].…”

Section: Introductionmentioning

confidence: 99%

State Estimation with Reduced-Order Observer and Adaptive-LQR Control of Time Varying Linear System

Aydoğdu¹,

Levent

2020

ELEKTRON ELEKTROTECH

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In this study, a new controller design was created to increase the control performance of a variable loaded time varying linear system. For this purpose, a state estimation with reduced order observer and adaptive-LQR (Linear–Quadratic Regulator) control structure was offered. Initially, to estimate the states of the system, a reduced-order observer was designed and used with LQR control method that is one of the optimal control techniques in the servo system with initial load. Subsequently, a Lyapunov-based adaptation mechanism was added to the LQR control to provide optimal control for varying loads as a new approach in design. Thus, it was aimed to eliminate the variable load effects and to increase the stability of the system. In order to demonstrate the effectiveness of the proposed method, a variable loaded rotary servo system was modelled as a time-varying linear system and used in simulations in Matlab-Simulink environment. Based on the simulation results and performance measurements, it was observed that the proposed method increases the system performance and stability by minimizing variable load effect.

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Flight control law of unmanned aerial vehicles based on robust servo linear quadratic regulator and Kalman filtering

Cited by 13 publications

References 13 publications

Kalman State Estimation and LQR Assisted Adaptive Control Of a Variable Loaded Servo System

Kalman State Estimation and LQR Assisted Adaptive Control Of a Variable Loaded Servo System

An effective proportional-double derivative-linear quadratic regulator controller for quadcopter attitude and altitude control

State Estimation with Reduced-Order Observer and Adaptive-LQR Control of Time Varying Linear System

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