Javier Gomez-Avila PhD
Available online 15 February 2019, Version of Record 15 February 2019.
Abstract
Conventional PID controllers use constant coefficients obtained empirically. Although these controllers are widely used for their simpleness they are not designed to control complex nonlinear systems, mainly because of their lack of adaptability. With this in mind, the following chapter presents an adaptive controller based on the PID principle using a neural network, which have shown adaptation capabilities. A multilayer perceptron is trained with the extended Kalman filter and the output of the network represents the system control input. In order to show its adaptability and effectiveness, simulations are presented on a quadrotor due to uncertainties, time delays, and unmodeled dynamics, typical of this kind of systems.
Cited by (6)
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Citation Excerpt : Regarding ANN training, in addition to the classic books that present the fundamentals in ANN [14] and other compendia [12] that present other approaches to ANN. In an adaptive PID Controller using a multilayer perceptron trained with the Extended Kalman Filter for an unmanned aerial vehicle is presented in [11]. In [15], the authors present a control method based on adaptive PID neural network and particle swarm optimization algorithm.
- Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey 2022, arXiv
- Quadcopter nonsingular finite-time adaptive robust saturated command-filtered control system under the presence of uncertainties and input saturation 2021, Nonlinear Dynamics
- Adaptive neural PD controllers for mobile manipulator trajectory tracking 2021, PeerJ Computer Science
- Design of Genetic Algorithms for the Simulation-Based Training of Artificial Neural Networks in the Context of Automated Vehicle Guidance 2020, Proceedings of the 2020 19th International Conference on Mechatronics - Mechatronika, ME 2020
- Adaptive single neuron anti-windup PID controller based on the extended Kalman filter algorithm 2020, Electronics (Switzerland)
