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• W268643608 abstract "We describe a novel adaptive non-linear model predictive controller which is based on the idea of neural-augmentation of reference elements, both at the level of the reduced model and at the level of the control action. The new methodology is primarily motivated by the desire to consistently incorporate existing legacy modeling and control techniques into an adaptive non-linear, yet real-time-capable, control framework. At the level of the model, a reference model is augmented using an adaptive neural element. Kalman filtering is used for identifying on-line the free parameters of the neural network, with the goal of maximizing the prediction fidelity of the model with respect to the plant. At the level of the control strategy, a reference solution is augmented using a second adaptive neural element. The augmenting control network is trained on-line for correcting the reference control action and promoting it to the solution of the underlying non-linear model predictive problem. The resulting neural-augmented control strategy is non-linear, yet it is real-time capable in the sense that it requires a fixed number of operations at each step. Furthermore, it substantially eases the adaption process, since the neural elements must only be trained to capture the defects of the reference legacy elements, which is small if such reference elements are adequate. The proposed procedures are demonstrated in a virtual environment with the help of the classical model problem of the double inverted-pendulum, and with the more challenging reflexive control of an autonomous helicopter." @default.
• W268643608 created "2016-06-24" @default.
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• W268643608 date "2008-01-01" @default.
• W268643608 modified "2023-09-26" @default.
• W268643608 title "Adaptive Reference-Augmented Predictive Control, with Application to the Reflexive Control of Unmanned Rotorcraft Vehicles" @default.
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