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Robust-Optimal Fuzzy Model-Based Control of Flexible Spacecraft With Actuator Amplitude and Rate Constraints

[+] Author Affiliations
Chokri Sendi, Mohammad A. Ayoubi

Santa Clara University, Santa Clara, CA

Paper No. DSCC2015-9902, pp. V001T06A005; 10 pages
doi:10.1115/DSCC2015-9902
From:
  • ASME 2015 Dynamic Systems and Control Conference
  • Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2: Hybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems
  • Columbus, Ohio, USA, October 28–30, 2015
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5724-3
  • Copyright © 2015 by ASME

abstract

This paper presents a robust-optimal fuzzy controller for position and attitude stabilization, and vibration suppression of a flexible spacecraft during antenna retargeting maneuver. The fuzzy controller is based on Takagi-Sugeno (T-S) fuzzy model and uses the dynamic parallel distributed compensator (DPDC) technique to quadratically stabilize the closed-loop system. The proposed controller is robust to parameter and unstructured uncertainties of the model. We improve the performance and the efficiency of the controller by minimizing the upper bound of the actuators amplitude and rate, and maximizing the uncertainties terms included in the T-S fuzzy model. In addition to actuator amplitude and rate constraints, a fuzzy model-based observer is considered for estimating unmeasurable states. Using Lyapunov stability theory and linear matrix inequalities (LMIs), we formulate the problem of designing an optimal-robust fuzzy controller/observer with actuator amplitude and rate constraints as a convex optimization problem. Numerical simulation is provided to demonstrate and compare the stability, performance, and robustness of the proposed fuzzy controller with a baseline nonlinear controller.

Copyright © 2015 by ASME

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