This paper presents a fault tolerant approach for a coaxial octorotor regarding rotor failures. A complete architecture including error detection, fault isolation and system recovery is presented. The diagnosis system is designed with a nonlinear observer to generate residuals and an inference model to evaluate them and isolate the faulty motor. Once the motor failure is diagnosed, a recovery algorithm is applied. It uses the built-in hardware redundancy of the octorotor and compensates the loss of the failing motor by controlling its dual to keep a stable flight that allows the multirotor to continue its mission. This architecture is validated on real flights.

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This paper proposes nonlinear autoregressive (AR) models for time series, within the framework of kernel machines. Two models are investigated. In the first proposed model, the AR model is defined on the mapped samples in the feature space. In order to predict a future sample, this formulation requires to solve a pre-image problem to get back to the input space. We derive an iterative technique to provide a fine-tuned solution to this problem. The second model bypasses the pre-image problem, by defining the AR model with an hybrid model, as a tradeoff considering the computational time and the precision, by comparing it to the iterative, fine-tuned, model. By considering the stationarity assumption, we derive the corresponding Yule-Walker equations for each model, and show the ease of solving these problems. The relevance of the proposed models is studied on several time series, and compared with other well-known models in terms of accuracy and computational complexity.

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