High Precision Stabilization of Pan-Tilt Systems Using Reliable Angular Acceleration Feedback from a Master-Slave Kalman Filter

Evren, Sanem; Yavuz, Firat; Ünel, Mustafa

doi:10.1007/s10846-017-0522-9

Cited by 8 publications

(10 citation statements)

References 34 publications

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“…Since the signals in V (t) must remain bounded, it can be concluded that s(t) ∈ L ∞ and this implies e(t) ∈ L ∞ based on (14). It is also obtained from (37) that υ(t) ∈ L 2 , and thus…”

Section: B Closed-loop Stability Analysismentioning

confidence: 88%

“…It will be used to evaluate the performance of the proposed control algorithm in Section V. The nonlinear model of the pan-tilt system based on the Euler-Lagrange formulation is as follows [13]- [14]: where…”

Section: Remarkmentioning

confidence: 99%

“…After taking the time derivative of (14) and multiplying the resulting expression by the inertia matrix, M(q), the open loop error system is obtained as:…”

Section: Assumption 1: Periodic and Bounded Desired Trajectoriesmentioning

confidence: 99%

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A new learning controller for periodic disturbance rejection

Han

Ünel

2017

2017 11th Asian Control Conference (ASCC)

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Abstract-A new acceleration based learning control approach is developed to tackle the robust periodic trajectory tracking problem for robot manipulators. The acceleration feedback is incorporated into the learning feedforward term to provide high stiffness to the system against unknown periodic dynamics with a known period. A cascaded high gain observer is used to obtain reliable position, velocity and acceleration signals from noisy encoder measurements. A closed-loop stability proof is provided where it is shown that all system signals remain bounded and the proposed learning controller achieves global asymptotic position tracking. Simulation results obtained from a high fidelity model show that the proposed controller outperforms the learning controller that does not utilize the acceleration feedback.

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Section: B Closed-loop Stability Analysismentioning

confidence: 88%

Section: Remarkmentioning

confidence: 99%

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A new learning controller for periodic disturbance rejection

Han

Ünel

2017

2017 11th Asian Control Conference (ASCC)

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“…To obtain fast and accurate attitude states, sensor fusion techniques have been applied to IMU measurements, including wide ranges of complementary filters [5,17,18,19,20,21,22,23,24] and Kalman filters [23,24,25,26,27,28,29,30,31,32,33,34,35]. A complementary filter typically combines accelerometer output for low-frequency attitude estimation with integrated gyroscope output for high-frequency estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Kalman filter is an optimal recursive estimation scheme that uses a system’s dynamic model, known control inputs, and multiple sequential measurements from sensors to form an estimate of the system states fusing prediction and measurement online [25,26,27,28]. The extended Kalman filter (EKF) is developed for nonlinear system state estimation and has been widely used for real-time UAV systems for Euler angle based attitude estimation [23,24,29,30] as well as quaternion based attitude estimation [31,32,33,34,35]. Unscented Kalman filter (UKF) [37,38,39] and adaptive Kalman filter (AKF) [40] are other widely used sensor fusion algorithms.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Linear Quadratic Attitude Tracking Control of a Quadrotor UAV Based on IMU Sensor Data Fusion

Koksal

Jalalmaab

Fi̇dan

2018

Sensors

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In this paper, an infinite-horizon adaptive linear quadratic tracking (ALQT) control scheme is designed for optimal attitude tracking of a quadrotor unmanned aerial vehicle (UAV). The proposed control scheme is experimentally validated in the presence of real-world uncertainties in quadrotor system parameters and sensor measurement. The designed control scheme guarantees asymptotic stability of the close-loop system with the help of complete controllability of the attitude dynamics in applying optimal control signals. To achieve robustness against parametric uncertainties, the optimal tracking solution is combined with an online least squares based parameter identification scheme to estimate the instantaneous inertia of the quadrotor. Sensor measurement noises are also taken into account for the on-board Inertia Measurement Unit (IMU) sensors. To improve controller performance in the presence of sensor measurement noises, two sensor fusion techniques are employed, one based on Kalman filtering and the other based on complementary filtering. The ALQT controller performance is compared for the use of these two sensor fusion techniques, and it is concluded that the Kalman filter based approach provides less mean-square estimation error, better attitude estimation, and better attitude control performance.

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