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Stabilization and tracking control of pan-tilt platforms using novel estimators and acceleration based robust control techniques

Evren Han, Sanem (2017) Stabilization and tracking control of pan-tilt platforms using novel estimators and acceleration based robust control techniques. [Thesis]

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Official URL: http://risc01.sabanciuniv.edu/record=b1655713 (Table of Contents)

Abstract

High precision stabilization is one of the fundamental problems in the control of robotic manipulators. It is generally regarded as a special case of the trajectory tracking problem in the control literature. This thesis focuses on the development of various robust control algorithms for robotic systems to achieve and maintain high precision stabilization against periodic/aperiodic parameter uncertainties and unknown external disturbances due to terrain changes, high frequency vibrations and sudden shocks, wind and other environmental factors. Robust stabilization problem is first tackled by employing angular acceleration feedback in an inner loop acceleration controller. To this end, a novel masterslave type Kalman filter algorithm is proposed where an extended Kalman filter (EKF) and an inverse phi-algorithm are combined in a master-slave configuration to estimate reliable angular acceleration signals by fusing 3-axis gyroscope, 3- axis accelerometer and 3-axis magnetometer data. Performance of the proposed estimator is evaluated through a high fidelity simulation model where estimated accelerations are used as feedback signals in the stabilization control of a pantilt platform subject to external disturbances. When the acceleration feedback is incorporated into the control loop, higher precision stabilization is achieved. The performance of the proposed estimator is compared to Newton predictor enhanced Kalman filter (NPEKF) and the error state Kalman filter (ErKF). The master-slave Kalman filter outperforms NPEKF and provides comparable results with ErKF. A polytopic quasi-LPV model of the pan-tilt system is developed and an LMI based optimal LQR controller that utilizes acceleration feedback is then synthesized based on this LPV model. Since the parameter vector is 4 dimensional, the desired LQR controller is synthesized by interpolating LMIs at 16 vertices of the polytope. A cascaded nonlinear high gain observer is designed to obtain reliableestimates of position, velocity and acceleration signals from noisy encoder measurements. Simulation results show that the proposed LMI based optimal LQR controller outperforms the classical LQR controller. This thesis also tackles the robust periodic trajectory tracking problem of robot manipulators. A hybrid learning based adaptive control approach using acceleration feedback is developed for robot manipulators subject to parameter uncertainties and unknown periodic dynamics with a known period. Learning and adaptive feedforward terms are designed to compensate for periodic and aperiodic disturbances. The acceleration feedback is incorporated into both learning and adaptive controllers to provide higher stiffness to the system against unknown periodic disturbances and robustness to parameter uncertainties. A closed-loop stability proof is provided where it is shown that all system signals remain bounded and the proposed hybrid controller achieves global asymptotic position tracking. Results obtained from a high fidelity simulation model demonstrates the validity and effectiveness of the developed hybrid controller.

Item Type:Thesis
Additional Information:Yükseköğretim Kurulu Tez Merkezi Tez No: 478288.
Uncontrolled Keywords:Stabilization. -- Acceleration feedback. -- Adaptive control. -- Learning control. -- Sensor fusion. -- Master-Slave kalman filter. -- High gain observer. -- Stabilizasyon. -- İvme geri beslemesi. -- Uyarlamalı kontrol. -- Öğrenmeli kontrol. -- Sensör füzyonu. -- Usta-Yamak kalman filtresi. -- Yüksek kazanç gözlemleyicisi.
Subjects:T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics
ID Code:34588
Deposited By:IC-Cataloging
Deposited On:27 Apr 2018 15:32
Last Modified:27 Apr 2018 15:32

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