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According to numerous capabilities and increasingly military and commercial applications of radio controlled helicopters, many investigations are being performed on these unmanned aircraft vehicles. Due to nonlinear, complex, unstable and coupled dynamic system and also existing limitations on manual control, the ability of automatic control of these vehicles has gained great importance. In this paper, in addition to investigating different methods of unmanned helicopters dynamic modeling, a multi-level simulator environment has been designed and implemented for flight performance analysis and effects of different parameters have been investigated. The main importance and innovation of present simulator is in possibility of dynamic flight simulation of helicopter using different theories for applications like control system design, performance analysis and real flight simulation. The main difference of the utilized methods is in theories and assumptions applied in main rotor and its flapping dynamics modeling. For each level, Kalman filter and control system design have been performed and preliminary results show the acceptable performance of estimator and controller systems. Considering the complexity of real unmanned helicopter behavior compared to previously performed models, the proposed multi-level simulator can be used as an appropriate tool for the first step before real flight tests.

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Human walking is one of the most robust and adaptive locomotion mechanisms in nature, involves sophisticated interactions between neural and biomechanical levels. It has been suggested that the coordination of this process is done in a hierarchy of levels. The lower layer contains autonomous interactions between muscles and spinal cord and the higher layer (e.g. the brain cortex) interferes when needed. Inspiringly, in this study, we present a hierarchical control architecture in order to control under-actuated and high degree of freedom systems with limit cycle behavior and it is implemented for the walking control of a 3-link biped robot. In this architecture, the system is controlled by independent control units for each joint at the lower layer. In order to stabilize the system, these units are driven by a sensory feedback from the posture of the robot. A central stabilizing controller at the upper layer arises in case of failing the units to stabilize the system and take the responsibility of training the lower layer controllers. We show that using this architecture, a highly unstable system can be stabilized with identical simple controller units even though they do not have any feedback from all other units and the robot.