110 research outputs found
UAV Model-based Flight Control with Artificial Neural Networks: A Survey
Model-Based Control (MBC) techniques have dominated flight controller designs for Unmanned Aerial Vehicles (UAVs). Despite their success, MBC-based designs rely heavily on the accuracy of the mathematical model of the real plant and they suffer from the explosion of complexity problem. These two challenges may be mitigated by Artificial Neural Networks (ANNs) that have been widely studied due to their unique features and advantages in system identification and controller design. Viewed from this perspective, this survey provides a comprehensive literature review on combined MBC-ANN techniques that are suitable for UAV flight control, i.e., low-level control. The objective is to pave the way and establish a foundation for efficient controller designs with performance guarantees. A reference template is used throughout the survey as a common basis for comparative studies to fairly determine capabilities and limitations of existing research. The end-result offers supported information for advantages, disadvantages and applicability of a family of relevant controllers to UAV prototypes
Modeling and Robust Control of Flying Robots Using Intelligent Approaches Modélisation et commande robuste des robots volants en utilisant des approches intelligentes
This thesis aims to modeling and robust controlling of a flying robot of quadrotor type. Where
we focused in this thesis on quadrotor unmanned Aerial Vehicle (QUAV). Intelligent
nonlinear controllers and intelligent fractional-order nonlinear controllers are designed to
control. The QUAV system is considered as MIMO large-scale system that can be divided on
six interconnected single-input–single-output (SISO) subsystems, which define one DOF, i.e.,
three-angle subsystems with three position subsystems. In addition, nonlinear models is
considered and assumed to suffer from the incidence of parameter uncertainty. Every
parameters such as mass, inertia of the system are assumed completely unknown and change
over time without prior information. Next, basing on nonlinear, Fractional-Order nonlinear
and the intelligent adaptive approximate techniques a control law is established for all
subsystems. The stability is performed by Lyapunov method and getting the desired output
with respect to the desired input. The modeling and control is done using
MATLAB/Simulink. At the end, the simulation tests are performed to that, the designed
controller is able to maintain best performance of the QUAV even in the presence of unknown
dynamics, parametric uncertainties and external disturbance
Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks
In this document, the development and experimental validation of a nonlinear controller with an adaptive disturbance compensation system applied on a quadrotor are presented. The introduced scheme relies on a generalized regression neural network (GRNN). The proposed scheme has a structure consisting of an inner control loop inaccessible to the user (i.e., an embedded controller) and an outer control loop which generates commands for the inner control loop. The adaptive GRNN is applied in the outer control loop. The proposed approach lies in the aptitude of the GRNN to estimate the disturbances and unmodeled dynamic effects without requiring accurate knowledge of the quadrotor parameters. The adaptation laws are deduced from a rigorous convergence analysis ensuring asymptotic trajectory tracking. The proposed control scheme is implemented on the QBall 2 quadrotor. Comparisons with respect to a PD-based control, an adaptive model regressor-based scheme, and an adaptive neural-network controller are carried out. The experimental results validate the functionality of the novel control scheme and show a performance improvement since smaller tracking error values are produced.Fil: Lopez Sanchez, Ivan. INSTITUTO POLITÉCNICO NACIONAL (IPN);Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de IngenierÃa. Instituto de Automática; ArgentinaFil: Pérez Alcocer, Ricardo. INSTITUTO POLITÉCNICO NACIONAL (IPN);Fil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de IngenierÃa. Instituto de Automática; ArgentinaFil: Carelli, Ricardo. Universidad Nacional de San Juan. Facultad de IngenierÃa. Instituto de Automática; ArgentinaFil: Moreno Valenzuela, Javier. INSTITUTO POLITÉCNICO NACIONAL (IPN)
Interval Valued Fuzzy Modeling and Indirect Adaptive Control of Quadrotor
In this paper, a combination of fuzzy clustering estimation and sliding mode
control is used to control a quadrotor system, whose mathematical model is
complex and has unknown elements, including structure, parameters, and so on.
In addition, they may be affected by external environmental disturbances. At
first, the nonlinear unknown part of the system is estimated by a fuzzy model,
A new method is presented for constructing a Takagi-Sugeno (TS) interval-valued
fuzzy model (IVFM) based on inputoutput data of the identified system.
Following the construction of the fuzzy model that estimates the unknown part
of the quadrotor system, a control and on-line adjusting of the fuzzy modeled
part of dynamics is used. In this step, the system model will be estimated in
adaptive form so that the dynamic equations can be used in sliding mode
control. Finally, the proposed technique is applied, and the simulation results
are presented to show the effectiveness of this approach in controlling the
quadrotor with unknown nonlinear dynamics.Comment: 25 page
Neural MRAC based on modified state observer
A new model reference adaptive control design method with guaranteed transient performance using neural networks is proposed in this thesis. With this method, stable tracking of a desired trajectory is realized for nonlinear system with uncertainty, and modified state observer structure is designed to enable desired transient performance with large adaptive gain and at the same time avoid high frequency oscillation. The neural network adaption rule is derived using Lyapunov theory, which guarantees stability of error dynamics and boundedness of neural network weights, and a soft switching sliding mode modification is added in order to adjust tracking error. The proposed method is tested by different theoretical application problems simulations, and also Caterpillar Electro-Hydraulic Test Bench experiments. Satisfying results show the potential of this approach --Abstract, page iv
Neural Network-based Finite-time Control of Nonlinear Systems with Unknown Dead-zones: Application to Quadrotors
Over the years, researchers have addressed several control problems of various classes of nonlinear systems. This article considers a class of uncertain strict feedback nonlinear system with unknown external disturbances and asymmetric input dead-zone. Designing a tracking controller for such system is very complex and challenging. This article aims to design a finite-time adaptive neural network backstepping tracking control for the nonlinear system under consideration. In addition, all unknown disturbances and nonlinear functions are lumped together and approximated by radial basis function neural network (RBFNN). Moreover, no prior information about the boundedness of the dead-zone parameters is required in the controller design. With the aid of a Lyapunov candidate function, it has been shown that the tracking errors converge near the origin in finite-time. Simulation results testify that the proposed control approach can force the output to follow the reference trajectory in a short time despite the presence of asymmetric input dead-zone and external disturbances. At last, in order to highlight the effectiveness of the proposed control method, it is applied to a quadrotor unmanned aerial vehicle (UAV)
Radial basis function neural network control for parallel spatial robot
The derivation of motion equations of constrained spatial multibody system is an important problem of dynamics and control of parallel robots. The paper firstly presents an overview of the calculating the torque of the driving stages of the parallel robots using Kronecker product. The main content of this paper is to derive the inverse dynamics controllers based on the radial basis function (RBF) neural network control law for parallel robot manipulators. Finally, numerical simulation of the inverse dynamics controller for a 3-RRR delta robot manipulator is presented as an illustrative example
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