Designing a stable model-based fuzzy controller for a novel 6-DOF parallel manipulator with rotary actuators

Azizan, H.; Keshmiri, Mehdi; Jafarinasab, Mohammad

doi:10.1109/aim.2011.6027125

Cited by 1 publication

(1 citation statement)

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“…Azizan et al designed a stable model-based fuzzy controller for this type of manipulator. 25 Stability of the controller has been guaranteed via the Lyapunov approach. Eftekhari et al presented the adaptability of a neuro-adaptive fuzzy controller on a platform designed to transport loads of unknown inertia.…”

Section: Introductionmentioning

confidence: 99%

Emulation of pilot control behavior across a Stewart platform simulator

Eftekhari

Karimpour

2018

Robotica

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SUMMARYThis paper presents a model-based controller consisting of a feedback linearization scheme and a state-dependent proportional derivative (PD) controller adapted to a parallel flight simulator Stewart mechanism. This parallel robot is considered to emulate motions of highly maneuverable aircrafts, which require well-trained pilots. The simulations are based upon a reduced-model prototype built in order to verify kinematic design aspects and control laws. Indeterminacies in the mass distribution of the system will generally affect model-based controllers, necessitating compensation or the employment of robust control methods. Through introducing the pilot's sensorial feedback of acceleration, the pilot's behavior in giving commands is emulated via an optimization process, which tunes the controller coefficients accordingly. Stability of the designed control system is guaranteed via the Lyapunov approach. To further explore the system through perilous flight scenarios, three pre-designed maneuvers are selected as test cases. It is expected that closed-loop control tasks in which a pilot tracks a target, while at the same time the controller rejects disturbances and adapts itself to the pilot's progressive skills, are ameliorated through this arrangement. Numerical results show that the proposed method is found robust in the training process in conditions of parameters indeterminacy.

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Section: Introductionmentioning

confidence: 99%