A quaternion-based feedback is developed for the attitude stabilization of rigid bodies. The control design takes into account a priori input bounds and is based on nested saturation approach. It results in a very simple controller suitable for an embedded use with low computational resources available. The proposed method is generic not restricted to symmetric rigid bodies and does not require the knowledge of the inertia matrix of the body. The control law can be tuned to force closed-loop trajectories to enter in some a priori fixed neighborhood of the origin in a finite time and remain thereafter. The global stability is guaranteed in the case where angular velocities sensors have limited measurement range. The control law is experimentally applied to the attitude stabilization of a quadrotor mini-helicopter.
A cheap tourism airplane ADAHRS unit is developed by fusing magnetometers, rate gyros and accelerometers (MEMS technology). The rigid body orientation is modeled with quaternion, which eliminates attitude estimation singularities. The real-time implementation is done unifying a quaternion formulation of Wahba's problem with a Multiplicative Extended Kalman Filter. It includes the gyro bias model. A quaternion measurement model is introduced. It avoids the linearization step that induces undesirable effects. Accelerometers detect gravitational acceleration and centrifugal forces, resulting in incorrect attitude estimation (e.g. false horizon and subjective vertical). Therefore, some pressure sensors are added, resulting in a robust solution. The real-time implementation uses a PCMCIA data acquisition card and a TabletPC. Simulated and real data validate the ADAHRS.
Technological advances have made wireless sensor nodes cheap and reliable enough to be brought into various application domains. These nodes are powered by battery, thus they have a limited lifespan which is a major drawback for their acceptance. This paper addresses a power consumption control problem of wireless nodes equipped with batteries. Dynamic power management is used to dynamically re-configure the set of sensor nodes in order to provide given service and performance levels with a minimum number of active nodes and/or a minimum load on such components. The power control formulation is based on model predictive control with constraints and binary optimization variables, leading to a mixed integer quadratic programming problem. Simulations are performed to demonstrate the efficiency of the proposed control method.
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