267 research outputs found
Design and Simulation of a Neuroevolutionary Controller for a Quadcopter Drone
The problem addressed in the present paper is the design of a controller based on an evolutionary neural network for autonomous flight in quadrotor systems. The controller's objective is to govern the quadcopter in such a way that it reaches a specific position, bearing on attitude limitations during flight and upon reaching a target. Given the complex nature of quadcopters, an appropriate neural network architecture and a training algorithm were designed to guide a quadcopter toward a target. The designed controller was implemented as a single multi-layer perceptron. On the basis of the quadcopter's current state, the developed neurocontroller produces the correct rotor speed values, optimized in terms of both attitude-limitation compliance and speed. The neural network training was completed using a custom evolutionary algorithm whose design put particular emphasis on the cost function's definition. The developed neurocontroller was tested in simulation to drive a quadcopter to autonomously follow a complex path. The obtained simulated results show that the neurocontroller manages to effortlessly follow several types of paths with adequate precision while maintaining low travel times
Distributed Neurodynamics-Based Backstepping Optimal Control for Robust Constrained Consensus of Underactuated Underwater Vehicles Fleet
Robust constrained formation tracking control of underactuated underwater
vehicles (UUVs) fleet in three-dimensional space is a challenging but practical
problem. To address this problem, this paper develops a novel consensus based
optimal coordination protocol and a robust controller, which adopts a
hierarchical architecture. On the top layer, the spherical coordinate transform
is introduced to tackle the nonholonomic constraint, and then a distributed
optimal motion coordination strategy is developed. As a result, the optimal
formation tracking of UUVs fleet can be achieved, and the constraints are
fulfilled. To realize the generated optimal commands better and, meanwhile,
deal with the underactuation, at the lower-level control loop a neurodynamics
based robust backstepping controller is designed, and in particular, the issue
of "explosion of terms" appearing in conventional backstepping based
controllers is avoided and control activities are improved. The stability of
the overall UUVs formation system is established to ensure that all the states
of the UUVs are uniformly ultimately bounded in the presence of unknown
disturbances. Finally, extensive simulation comparisons are made to illustrate
the superiority and effectiveness of the derived optimal formation tracking
protocol.Comment: This paper is accepted by IEEE Transactions on Cybernetic
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