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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. [ABSTRACT FROM AUTHOR]

In this study, design of a novel robust control method for three-link underactuated biped robot which can satisfy appropriate constrains on the robot and cause the stability and rhythmic movement of the robot, is presented. Due to the wide use of sensors and actuators in mechanisms and robots, and existence of noise and also uncertainty in the system components or other error stemming from unmolded dynamics in the system or unwanted disturbances acting on them, there is an essential need for employment of robust control methods. In order to apply locomotion's constrains to the system, the feedback linearization method is used. Additionally this method is combined with the sliding mode method to obtain a robust control method. The other purpose of the study is the complete elimination of chattering phenomenon. To this end, the control method is combined with the backstepping method. Finally, the exponential stability of the method, which is called FLBS, is proved, and the stability of the obtained walking cycle is shown using the Poincare map. In the robot modeling procedure, the contact between the swing leg and the ground is considered to be rigid and instantaneous. The underactuated nature of the robot and the importance of the role of contact in stabilizing the robot movement are taken into account in this study. Finally, simulations based on this control method are performed which show the exponential stability of robot movement, elimination of chattering and robustness against either disturbances or uncertainties. [ABSTRACT FROM AUTHOR]

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 the realm of 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. [ABSTRACT FROM AUTHOR]

Inertial navigation system has drift error in underwater applications. Use of DVL with Kalman filter for position and attitude correction is common. Using velocity data decreases drift error in position estimation but this error exists and increases linearity with time. In this article the navigation system consists of inertial measurement unit (IMU) and a Doppler velocity log (DVL) along with depth sensor. With use of magnetic field measurement and earth magnetic field map a new measurement is generated. Discrete extended Kalman filter with indirect feedback is used for tightly coupled integrated navigation algorithm. This algorithm is based on inertial navigation error dynamics. This paper demonstrates the effectiveness of algorithm through simulation. The procedure of simulation is done by sensor data generation. Arbitrary trajectory with specific kinematic characteristic (linear and angular velocity and acceleration) is generated. Sensor data by adding noise and bias to kinematic characteristic of trajectory is produced. Simulation results reveal that the new algorithm with use of magnetic data and earth magnetic field map decreases the drift error with comparison to conventional INS-DVL integrated navigation algorithm. [ABSTRACT FROM AUTHOR]