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Daniel Guggenheim School of Aerospace Engineering
Daniel Guggenheim School of Aerospace Engineering
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ItemRobust Motion Planning in the Presence of Uncertainties using a Maneuver Automaton(Georgia Institute of Technology, 2005-04-18) Topsakal, Julide JulieOne of the basic problems which have to be solved by Unmanned Automated Vehicles (UAV) involves the computation of a motion plan that would enable the system to reach a target given a set of initial conditions in presence of uncertainties on the vehicle dynamics and in the environment. Recent research efforts in this area have relied on deterministic models. To address the problem of inevitable uncertainties, a low-level control layer is typically used to ensure proper robust trajectory tracking. Such decision-tracking algorithms correct model disturbances a posteriori, while the whole movement planning is done in a purely deterministic fashion. We argue that the decision making process that takes place during movement planning, as performed by experienced human pilots, is not a purely deterministic operation, but is heavily influenced by the presence of uncertainties and reflects a risk-management policy. This research aims at addressing these uncertainties and developing an optimal control strategy that would account for the presence of system uncertainties. The underlying description of UAV trajectories will be based on a modeling language, the Maneuver Automaton, that takes into full account the vehicle dynamics, and hence guarantees flyable and trackable paths and results in a discretized solution space. Two optimal control problems, a nominal problem omitting uncertainties and a robust problem addressing the presence of uncertainties, will be defined and compared throughout this work. The incorporation of uncertainties, will ensure that the generated motion planning policies will maximize the probability to meet mission goals, weighing risks against performance.
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ItemPredictive Control of Multibody Systems for the Simulation of Maneuvering Rotorcraft(Georgia Institute of Technology, 2005-04-18) Sumer, Yalcin FaikSimulation of maneuvers with multibody models of rotorcraft vehicles is an important research area due to its complexity. During the maneuvering flight, some important design limitations are encountered such as maximum loads and maximum turning rates near the proximity of the flight envelope. This increases the demand on high fidelity models in order to define appropriate controls to steer the model close to the desired trajectory while staying inside the boundaries. A framework based on the hierarchical decomposition of the problem is used for this study. The system should be capable of generating the track by itself based on the given criteria and also capable of piloting the model of the vehicle along this track. The generated track must be compatible with the dynamic characteristics of the vehicle. Defining the constraints for the maneuver is of crucial importance when the vehicle is operating close to its performance boundaries. In order to make the problem computationally feasible, two models of the same vehicle are used where the reduced model captures the coarse level flight dynamics, while the fine scale comprehensive model represents the plant. The problem is defined by introducing planning layer and control layer strategies. The planning layer stands for solving the optimal control problem for a specific maneuver of a reduced vehicle model. The control layer takes the resulting optimal trajectory as an optimal reference path, then tracks it by using a non-linear model predictive formulation and accordingly steers the multibody model. Reduced models for the planning and tracking layers are adapted by using neural network approach online to optimize the predictive capabilities of planner and tracker. Optimal neural network architecture is obtained to augment the reduced model in the best way. The methodology of adaptive learning rate is experimented with different strategies. Some useful training modes and algorithms are proposed for these type of applications. It is observed that the neural network increased the predictive capabilities of the reduced model in a robust way. The proposed framework is demonstrated on a maneuvering problem by studying an obstacle avoidance example with violent pull-up and pull-down.
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ItemRotorcraft trim by a neural model-predictive auto-pilot(Georgia Institute of Technology, 2005-04-14) Riviello, LucaIn this work we investigate the use of state-of-the-art tools for the regulation of complex, non-linear systems to improve the methodologies currently applied to trim comprehensive virtual prototypes of rotors and rotorcrafts. Among the several methods that have been proposed in the literature, the auto-pilot approach has the potential to solve trim problems efficiently even for the large and complex vehicle models of modern comprehensive finite element-based analysis codes. In this approach, the trim condition is obtained by adjusting the controls so as to virtually ``fly' the system to the final steady (periodic) flight condition. Published proportional auto-pilots show to work well in many practical instances. However, they cannot guarantee good performance and stability in all flight conditions of interest. Limit-cycle oscillations in control time histories are often observed in practice because of the non-linear nature of the problem and the difficulties in enforcing the constant-in-time condition for the controls. To address all the above areas of concern, in this research we propose a new auto-pilot, based on non-linear model-predictive control (NMPC). The formulation uses a non-linear reference model of the system augmented with an adaptive neural element, which identifies and corrects the mismatch between reduced model and controlled system. The methodology is tested on the wind-tunnel trim of a rotor multibody model and compared to an existing implementation of a classic auto-pilot. The proposed controller shows good performance without the need of a potentially very expensive tuning phase, which is required in classical auto-pilots. Moreover, model-predictive control provides a framework for guaranteeing stability of the non-linear closed-loop system, so it seems to be a viable approach for trimming complete rotorcraft comprehensive models in free-flight.