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In this paper, the development of a fault-tolerant control system for an aircraft that exploits both the hardware and analytical redundancy in the system is considered. A control allocation approach is developed where the total control command is computed and distributed among the available control surfaces. The actuator’s position and rate limits are taken into account in the optimisation problem. Existing fault-tolerant control allocation techniques produce look-up tables of control gains based on certain faults in the model. In contrast, the developed reconfigurable approach presented here incorporates a new process that redistributes control efforts which is updated whenever a fault occurs. In order to correlate between control effort redistribution and the fault magnitude, a fuzzy logic scheme is implemented, which handles a wide range of fault magnitudes on-line. The approach is applied for the most severe type of fault, which is the “lock-in-place” (jam) fault. Results show that the developed approach successfully handles the faulty situations and enhances aircraft flying responses by utilising the available healthy controls.
We propose a novel stereo visual IMU-assisted (Inertial Measurement Unit) technique that extends to large inter-frame motion the use of KLT tracker (Kanade–Lucas–Tomasi). The constrained and coherent inter-frame motion acquired from the IMU is applied to detected features through homogenous transform using 3D geometry and stereoscopy properties. This predicts efficiently the projection of the optical flow in subsequent images. Accurate adaptive tracking windows limit tracking areas resulting in a minimum of lost features and also prevent tracking of dynamic objects. This new feature tracking approach is adopted as part of a fast and robust visual odometry algorithm based on double dogleg trust region method. Comparisons with gyro-aided KLT and variants approaches show that our technique is able to maintain minimum loss of features and low computational cost even on image sequences presenting important scale change. Visual odometry solution based on this IMU-assisted KLT gives more accurate result than INS/GPS solution for trajectory generation in certain context.
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