Aerial manipulation aims at combining the maneuverability of aerial vehicles with the manipulation capabilities of robotic arms. This, however, comes at the cost of the additional control complexity due to the coupling of the dynamics of the two systems. In this paper we present a Nonlinear Model Predictive Control (NMPC) specifically designed for Micro Aerial Vehicles (MAVs) equipped with a robotic arm. We formulate a hybrid control model for the combined MAV-arm system which incorporates interaction forces acting on the end effector. We explain the practical implementation of our algorithm and show extensive experimental results of our custom built system performing multiple 'aerial-writing' tasks on a whiteboard, revealing accuracy in the order of millimetres.
Safe and precise reference tracking is a crucial characteristic of Micro Aerial Vehicles (MAVs) that have to operate under the influence of external disturbances in cluttered environments. In this paper, we present a Nonlinear Model Predictive Control (NMPC) that exploits the fully physics based non-linear dynamics of the system. We furthermore show how the moment and thrust control inputs can be transformed into feasible actuator commands. In order to guarantee safe operation despite potential loss of a motor under which we show our system keeps operating safely, we developed an Extended Kalman Filter (EKF) based motor failure identification algorithm. We verify the effectiveness of the developed pipeline in flight experiments with and without motor failures.
Design of a cost-effecti®e and highly controllable heat-exchanger network HEN has drawn a great deal of attention for years. One of the key issues in such a design is how to effecti®ely minimize undesirable disturbance propagation in a network with minimum cost increment. Design options in this regard include the deri®ation of a superior network structure and the selection of bypasses associated with heat exchangers. A unique system modeling approach is de®eloped to predict disturbance propagation and to reject disturbances using bypasses. A no®el mathematical representation scheme for a HEN is introduced to facilitate system analysis and design. A relati®e gain-array approach is extended to the analysis of nonsquared systems. In addition, an iterati®e design procedure is introduced to determine optimal bypass locations and nominal fractions for complete disturbance rejection, while economic penalty reaches the minimum. The efficacy of the model-based approach is demonstrated by designing three HENs where bypasses are optimally placed, and the control schemes are simultaneously de®eloped.
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