The basic autonomy level is to maintain its stability following a desired path under embedded guidance, navigation and control algorithm. Generally a certain level of autonomous flight capability is required for the vehicle to achieve its mission. The unmanned small scale helicopters are particularly suitable for demanding problems which requires accurate low-speed maneuver and hovering capabilities such as detailed area mapping. The unmanned aerial vehicles (UAVs) have shown applications in different areas including crop yield prediction, land use surveys in rural and urban regions, traffic surveillance and weather research. The utilization of unmanned vehicles has become increasingly more popular today and been successfully demonstrated for various civil and military applications. Apart from the effectiveness of the optimized model, the proposed design algorithm is expected to facilitate timely development of the nonparametric model of the helicopter system. The optimized model outperformed the previous model architecture with up to 55% performance improvement. The performance of the proposed optimized model is benchmarked with one of the previously reported architectures for a similar system. The proposed hybrid algorithm was able to produce models with Pareto-optimal compromise between the design objectives. This study proposes a hybrid of conventional back propagation training algorithm for the NARX network and multiobjective differential evolution (MODE) algorithm for identification of a nonlinear model of an unmanned small scale helicopter from experimental flight data.
Otomata offline trial#
The current approach in the literature has been largely based on trial and error, while most of the reported optimization approaches have limited the domain of the problem to a single objective problem. The performance of the NARX network in terms of complexity and accuracy is largely dependent on the network architecture. On the other hand, the problem of determining network architecture for optimal/sub-optimal performances has been one of the major challenges in the use of the nonparametric approach based on Nonlinear AutoRegressive with eXogenous inputs Network (NARX-network).
However, going by a first principle approach based on physical laws governing the dynamics of the system, this task is noted to be highly challenging due to the complex nonlinear characteristics of the helicopter system. The need for a high fidelity model for design, analysis and implementation of an unmanned helicopter system (UHS) in various emerging civil applications cannot be underestimated. The MLNN in, on the other hand, managed to stabilise the real helicopter for brief periods, however later investigations showed that the system had many hidden, potential instabilities. While the FAMs in showed promise in simulation, the controllers failed to stabilise the real helicopter. Only two methods have used pilot control signals to directly train a feedback controller.
Otomata offline Offline#
This offline controller learning has been performed using actor/critic type reinformcement learning, policy search methods based on MDP models built from flight data, and back propagation of errors through neural forward models. While many of the neuro/fuzzy approaches incorporate some form of limited authority, online adaption, the majority of control authority is learnt offline. of the non-conventional studies have moved beyond simulations. As a first step toward performing real helicopter tests, an FAM attitude controller has also been developed in simulation, and its tracking performance presented. The performance of an FAM velocity control being used by a higher level waypoint controller in simulation is presented. FAMs alone were found to be suitable under all simulation conditions. While the SLNN and MLNN provided adequate control under some simulation conditions, the addition of pilot noise and pilot variation during simulation training caused these methods to fail.
Each method has been tested in simulation. Three learning architectures, single layer neural networks (SLNN), multi-layer neural networks (MLNN), and fuzzy associative memories (FAM) are considerd. This paper details the development of an online adaptive control system, designed to learn from the actions of an instructing pilot.
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