Multilayer Neuro PID Controller based on Back Propagation Algorithm

Patel, R. B.; Kumar, Vijay

doi:10.1016/j.procs.2015.06.023

Cited by 15 publications

(6 citation statements)

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“…However, the genetic algorithm was easily to fall into local optimum. In order to solve the problem, Patel [24] appended the immigration mechanism, 10% of the elite population and the inferior population were selected as the variant population, to the neural network adaptive PID controller (MN-PID). In addition, Nie [25] presented an adaptive chaos particles swarm optimization for tuning parameters of PID controller (CSP-PID) to avoid the local minima.…”

Section: Adaptive Controller Based On Neural Networkmentioning

confidence: 99%

Design and application of adaptive PID controller based on asynchronous advantage actor–critic learning method

Sun

Duan

et al. 2019

Wireless Netw

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To address the problems of the slow convergence and inefficiency in the existing adaptive PID controllers, we propose a new adaptive PID controller using the asynchronous advantage actor-critic (A3C) algorithm. Firstly, the controller can train the multiple agents of the actor-critic structures in parallel exploiting the multi-thread asynchronous learning characteristics of the A3C structure. Secondly, in order to achieve the best control effect, each agent uses a multilayer neural network to approach the strategy function and value function to search the best parameter-tuning strategy in continuous action space. The simulation results indicate that our proposed controller can achieve the fast convergence and strong adaptability compared with conventional controllers.

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Section: Adaptive Controller Based On Neural Networkmentioning

confidence: 99%

Design and application of adaptive PID controller based on asynchronous advantage actor–critic learning method

Sun

Duan

et al. 2019

Wireless Netw

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“…In other words, the output of each layer only affects the output of next layer. Due to the structural characteristics and the advantages, BP neural network is widely applied to PID control [38].…”

Section: Bp Neural Network Based Constrained Controller Designmentioning

confidence: 99%

A novel adaptive neural network constrained control for solid oxide fuel cells via dynamic anti-windup

Liu

2016

Neurocomputing

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“…For instance, in [ 24 ], a fuzzy PID controller, which is a combination of a fuzzy controller with a PID neural network (PIDNN), was proposed. In [ 25 ], a conventional Neuro PID controller for linear or nonlinear systems that was unaffected by the unpredictability of the system’s parameters and disturbances, such as noise, was developed. However, again, these methods require a model for the controlled system as they lack stability guarantees.…”

Section: Introductionmentioning

confidence: 99%

OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results

Abdelkader

Mabrok

Koubâa

2022

Sensors

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Autonomous robots require control tuning to optimize their performance, such as optimal trajectory tracking. Controllers, such as the Proportional–Integral–Derivative (PID) controller, which are commonly used in robots, are usually tuned by a cumbersome manual process or offline data-driven methods. Both approaches must be repeated if the system configuration changes or becomes exposed to new environmental conditions. In this work, we propose a novel algorithm that can perform online optimal control tuning (OCTUNE) of a discrete linear time-invariant (LTI) controller in a classical feedback system without the knowledge of the plant dynamics. The OCTUNE algorithm uses the backpropagation optimization technique to optimize the controller parameters. Furthermore, convergence guarantees are derived using the Lyapunov stability theory to ensure stable iterative tuning using real-time data. We validate the algorithm in realistic simulations of a quadcopter model with PID controllers using the known Gazebo simulator and a real quadcopter platform. Simulations and actual experiment results show that OCTUNE can be effectively used to automatically tune the UAV PID controllers in real-time, with guaranteed convergence. Finally, we provide an open-source implementation of the OCTUNE algorithm, which can be adapted for different applications.

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Multilayer Neuro PID Controller based on Back Propagation Algorithm

Cited by 15 publications

References 1 publication

Design and application of adaptive PID controller based on asynchronous advantage actor–critic learning method

Design and application of adaptive PID controller based on asynchronous advantage actor–critic learning method

A novel adaptive neural network constrained control for solid oxide fuel cells via dynamic anti-windup

OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results

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